WorldWideScience

Sample records for model selection bias

  1. Model selection bias and Freedman's paradox

    Science.gov (United States)

    Lukacs, P.M.; Burnham, K.P.; Anderson, D.R.

    2010-01-01

    In situations where limited knowledge of a system exists and the ratio of data points to variables is small, variable selection methods can often be misleading. Freedman (Am Stat 37:152-155, 1983) demonstrated how common it is to select completely unrelated variables as highly "significant" when the number of data points is similar in magnitude to the number of variables. A new type of model averaging estimator based on model selection with Akaike's AIC is used with linear regression to investigate the problems of likely inclusion of spurious effects and model selection bias, the bias introduced while using the data to select a single seemingly "best" model from a (often large) set of models employing many predictor variables. The new model averaging estimator helps reduce these problems and provides confidence interval coverage at the nominal level while traditional stepwise selection has poor inferential properties. ?? The Institute of Statistical Mathematics, Tokyo 2009.

  2. Dealing with selection bias in educational transition models

    DEFF Research Database (Denmark)

    Holm, Anders; Jæger, Mads Meier

    2011-01-01

    This paper proposes the bivariate probit selection model (BPSM) as an alternative to the traditional Mare model for analyzing educational transitions. The BPSM accounts for selection on unobserved variables by allowing for unobserved variables which affect the probability of making educational...... transitions to be correlated across transitions. We use simulated and real data to illustrate how the BPSM improves on the traditional Mare model in terms of correcting for selection bias and providing credible estimates of the effect of family background on educational success. We conclude that models which...... account for selection on unobserved variables and high-quality data are both required in order to estimate credible educational transition models....

  3. Selecting, weeding, and weighting biased climate model ensembles

    Science.gov (United States)

    Jackson, C. S.; Picton, J.; Huerta, G.; Nosedal Sanchez, A.

    2012-12-01

    In the Bayesian formulation, the "log-likelihood" is a test statistic for selecting, weeding, or weighting climate model ensembles with observational data. This statistic has the potential to synthesize the physical and data constraints on quantities of interest. One of the thorny issues for formulating the log-likelihood is how one should account for biases. While in the past we have included a generic discrepancy term, not all biases affect predictions of quantities of interest. We make use of a 165-member ensemble CAM3.1/slab ocean climate models with different parameter settings to think through the issues that are involved with predicting each model's sensitivity to greenhouse gas forcing given what can be observed from the base state. In particular we use multivariate empirical orthogonal functions to decompose the differences that exist among this ensemble to discover what fields and regions matter to the model's sensitivity. We find that the differences that matter are a small fraction of the total discrepancy. Moreover, weighting members of the ensemble using this knowledge does a relatively poor job of adjusting the ensemble mean toward the known answer. This points out the shortcomings of using weights to correct for biases in climate model ensembles created by a selection process that does not emphasize the priorities of your log-likelihood.

  4. Selection Bias in Educational Transition Models: Theory and Empirical Evidence

    DEFF Research Database (Denmark)

    Holm, Anders; Jæger, Mads

    Most studies using Mare’s (1980, 1981) seminal model of educational transitions find that the effect of family background decreases across transitions. Recently, Cameron and Heckman (1998, 2001) have argued that the “waning coefficients” in the Mare model are driven by selection on unobserved...... the United States, United Kingdom, Denmark, and the Netherlands shows that when we take selection into account the effect of family background variables on educational transitions is largely constant across transitions. We also discuss several difficulties in estimating educational transition models which...... variables. This paper, first, explains theoretically how selection on unobserved variables leads to waning coefficients and, second, illustrates empirically how selection leads to biased estimates of the effect of family background on educational transitions. Our empirical analysis using data from...

  5. Berkson’s bias, selection bias, and missing data

    OpenAIRE

    Westreich, Daniel

    2012-01-01

    While Berkson’s bias is widely recognized in the epidemiologic literature, it remains underappreciated as a model of both selection bias and bias due to missing data. Simple causal diagrams and 2×2 tables illustrate how Berkson’s bias connects to collider bias and selection bias more generally, and show the strong analogies between Berksonian selection bias and bias due to missing data. In some situations, considerations of whether data are missing at random or missing not at random is less i...

  6. Selection bias in species distribution models: An econometric approach on forest trees based on structural modeling

    Science.gov (United States)

    Martin-StPaul, N. K.; Ay, J. S.; Guillemot, J.; Doyen, L.; Leadley, P.

    2014-12-01

    Species distribution models (SDMs) are widely used to study and predict the outcome of global changes on species. In human dominated ecosystems the presence of a given species is the result of both its ecological suitability and human footprint on nature such as land use choices. Land use choices may thus be responsible for a selection bias in the presence/absence data used in SDM calibration. We present a structural modelling approach (i.e. based on structural equation modelling) that accounts for this selection bias. The new structural species distribution model (SSDM) estimates simultaneously land use choices and species responses to bioclimatic variables. A land use equation based on an econometric model of landowner choices was joined to an equation of species response to bioclimatic variables. SSDM allows the residuals of both equations to be dependent, taking into account the possibility of shared omitted variables and measurement errors. We provide a general description of the statistical theory and a set of applications on forest trees over France using databases of climate and forest inventory at different spatial resolution (from 2km to 8km). We also compared the outputs of the SSDM with outputs of a classical SDM (i.e. Biomod ensemble modelling) in terms of bioclimatic response curves and potential distributions under current climate and climate change scenarios. The shapes of the bioclimatic response curves and the modelled species distribution maps differed markedly between SSDM and classical SDMs, with contrasted patterns according to species and spatial resolutions. The magnitude and directions of these differences were dependent on the correlations between the errors from both equations and were highest for higher spatial resolutions. A first conclusion is that the use of classical SDMs can potentially lead to strong miss-estimation of the actual and future probability of presence modelled. Beyond this selection bias, the SSDM we propose represents

  7. A selection model for accounting for publication bias in a full network meta-analysis.

    Science.gov (United States)

    Mavridis, Dimitris; Welton, Nicky J; Sutton, Alex; Salanti, Georgia

    2014-12-30

    Copas and Shi suggested a selection model to explore the potential impact of publication bias via sensitivity analysis based on assumptions for the probability of publication of trials conditional on the precision of their results. Chootrakool et al. extended this model to three-arm trials but did not fully account for the implications of the consistency assumption, and their model is difficult to generalize for complex network structures with more than three treatments. Fitting these selection models within a frequentist setting requires maximization of a complex likelihood function, and identification problems are common. We have previously presented a Bayesian implementation of the selection model when multiple treatments are compared with a common reference treatment. We now present a general model suitable for complex, full network meta-analysis that accounts for consistency when adjusting results for publication bias. We developed a design-by-treatment selection model to describe the mechanism by which studies with different designs (sets of treatments compared in a trial) and precision may be selected for publication. We fit the model in a Bayesian setting because it avoids the numerical problems encountered in the frequentist setting, it is generalizable with respect to the number of treatments and study arms, and it provides a flexible framework for sensitivity analysis using external knowledge. Our model accounts for the additional uncertainty arising from publication bias more successfully compared to the standard Copas model or its previous extensions. We illustrate the methodology using a published triangular network for the failure of vascular graft or arterial patency.

  8. Testing for biases in selection on avian reproductive traits and partitioning direct and indirect selection using quantitative genetic models

    NARCIS (Netherlands)

    Reed, Thomas E; Gienapp, Phillip; Visser, Marcel E

    2016-01-01

    Key life history traits such as breeding time and clutch size are frequently both heritable and under directional selection, yet many studies fail to document micro-evolutionary responses. One general explanation is that selection estimates are biased by the omission of correlated traits that have c

  9. Testing for biases in selection on avian reproductive traits and partitioning direct and indirect selection using quantitative genetic models

    NARCIS (Netherlands)

    Reed, Thomas E; Gienapp, Phillip; Visser, Marcel E

    2016-01-01

    ey life history traits such as breeding time and clutch size are frequently both heritable and under directional selection, yet many studies fail to document micro-evolutionary responses. One general explanation is that selection estimates are biased by the omission of correlated traits that have ca

  10. Distinguishing Selection Bias and Confounding Bias in Comparative Effectiveness Research.

    Science.gov (United States)

    Haneuse, Sebastien

    2016-04-01

    Comparative effectiveness research (CER) aims to provide patients and physicians with evidence-based guidance on treatment decisions. As researchers conduct CER they face myriad challenges. Although inadequate control of confounding is the most-often cited source of potential bias, selection bias that arises when patients are differentially excluded from analyses is a distinct phenomenon with distinct consequences: confounding bias compromises internal validity, whereas selection bias compromises external validity. Despite this distinction, however, the label "treatment-selection bias" is being used in the CER literature to denote the phenomenon of confounding bias. Motivated by an ongoing study of treatment choice for depression on weight change over time, this paper formally distinguishes selection and confounding bias in CER. By formally distinguishing selection and confounding bias, this paper clarifies important scientific, design, and analysis issues relevant to ensuring validity. First is that the 2 types of biases may arise simultaneously in any given study; even if confounding bias is completely controlled, a study may nevertheless suffer from selection bias so that the results are not generalizable to the patient population of interest. Second is that the statistical methods used to mitigate the 2 biases are themselves distinct; methods developed to control one type of bias should not be expected to address the other. Finally, the control of selection and confounding bias will often require distinct covariate information. Consequently, as researchers plan future studies of comparative effectiveness, care must be taken to ensure that all data elements relevant to both confounding and selection bias are collected.

  11. Covariate balance assessment, model selection and bias in propensity score matching: A simulation study

    NARCIS (Netherlands)

    Ali, Sanni; Groenwold, Rolf H.H.; Belitser, S.; Roes, Kit C.B.; Hoes, Arno W.; De Boer, Anthonius; Klungel, Olaf H.

    2015-01-01

    Background: In building propensity score (PS) model, inclusion of interaction/square terms in addition to the main terms and the use of balance measures has been suggested. However, the impact of assessing balance of several sets of covariates and their interactions/squares on bias/precision is not

  12. Accounting for selection bias in species distribution models: An econometric approach on forested trees based on structural modeling

    Science.gov (United States)

    Ay, Jean-Sauveur; Guillemot, Joannès; Martin-StPaul, Nicolas K.; Doyen, Luc; Leadley, Paul

    2015-04-01

    Species distribution models (SDMs) are widely used to study and predict the outcome of global change on species. In human dominated ecosystems the presence of a given species is the result of both its ecological suitability and human footprint on nature such as land use choices. Land use choices may thus be responsible for a selection bias in the presence/absence data used in SDM calibration. We present a structural modelling approach (i.e. based on structural equation modelling) that accounts for this selection bias. The new structural species distribution model (SSDM) estimates simultaneously land use choices and species responses to bioclimatic variables. A land use equation based on an econometric model of landowner choices was joined to an equation of species response to bioclimatic variables. SSDM allows the residuals of both equations to be dependent, taking into account the possibility of shared omitted variables and measurement errors. We provide a general description of the statistical theory and a set of application on forested trees over France using databases of climate and forest inventory at different spatial resolution (from 2km to 8 km). We also compared the output of the SSDM with outputs of a classical SDM in term of bioclimatic response curves and potential distribution under current climate. According to the species and the spatial resolution of the calibration dataset, shapes of bioclimatic response curves the modelled species distribution maps differed markedly between the SSDM and classical SDMs. The magnitude and directions of these differences were dependent on the correlations between the errors from both equations and were highest for higher spatial resolutions. A first conclusion is that the use of classical SDMs can potentially lead to strong miss-estimation of the actual and future probability of presence modelled. Beyond this selection bias, the SSDM we propose represents a crucial step to account for economic constraints on tree

  13. A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies.

    Science.gov (United States)

    Fieberg, John R; Conn, Paul B

    2014-05-01

    An important assumption in observational studies is that sampled individuals are representative of some larger study population. Yet, this assumption is often unrealistic. Notable examples include online public-opinion polls, publication biases associated with statistically significant results, and in ecology, telemetry studies with significant habitat-induced probabilities of missed locations. This problem can be overcome by modeling selection probabilities simultaneously with other predictor-response relationships or by weighting observations by inverse selection probabilities. We illustrate the problem and a solution when modeling mixed migration strategies of northern white-tailed deer (Odocoileus virginianus). Captures occur on winter yards where deer migrate in response to changing environmental conditions. Yet, not all deer migrate in all years, and captures during mild years are more likely to target deer that migrate every year (i.e., obligate migrators). Characterizing deer as conditional or obligate migrators is also challenging unless deer are observed for many years and under a variety of winter conditions. We developed a hidden Markov model where the probability of capture depends on each individual's migration strategy (conditional versus obligate migrator), a partially latent variable that depends on winter severity in the year of capture. In a 15-year study, involving 168 white-tailed deer, the estimated probability of migrating for conditional migrators increased nonlinearly with an index of winter severity. We estimated a higher proportion of obligates in the study cohort than in the population, except during a span of 3 years surrounding back-to-back severe winters. These results support the hypothesis that selection biases occur as a result of capturing deer on winter yards, with the magnitude of bias depending on the severity of winter weather. Hidden Markov models offer an attractive framework for addressing selection biases due to their

  14. A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies

    OpenAIRE

    Fieberg, John R.; Paul B Conn

    2014-01-01

    An important assumption in observational studies is that sampled individuals are representative of some larger study population. Yet, this assumption is often unrealistic. Notable examples include online public-opinion polls, publication biases associated with statistically significant results, and in ecology, telemetry studies with significant habitat-induced probabilities of missed locations. This problem can be overcome by modeling selection probabilities simultaneously with other predicto...

  15. Select strengths and biases of models in representing the Arctic winter boundary layer over sea ice

    NARCIS (Netherlands)

    Pithan, Felix; Ackerman, Andrew; Angevine, Wayne M.; Hartung, Kerstin; Ickes, Luisa; Kelley, Maxwell; Medeiros, Brian; Sandu, Irina; Steeneveld, Gert Jan; Sterk, H.A.M.

    2016-01-01

    Weather and climate models struggle to represent lower tropospheric temperature and moisture profiles and surface fluxes in Arctic winter, partly because they lack or misrepresent physical processes that are specific to high latitudes. Observations have revealed two preferred states of the Arctic

  16. Select strengths and biases of models in representing the Arctic winter boundary layer over sea ice

    NARCIS (Netherlands)

    Pithan, Felix; Ackerman, Andrew; Angevine, Wayne M.; Hartung, Kerstin; Ickes, Luisa; Kelley, Maxwell; Medeiros, Brian; Sandu, Irina; Steeneveld, Gert Jan; Sterk, H.A.M.

    2016-01-01

    Weather and climate models struggle to represent lower tropospheric temperature and moisture profiles and surface fluxes in Arctic winter, partly because they lack or misrepresent physical processes that are specific to high latitudes. Observations have revealed two preferred states of the Arctic

  17. Modeling confirmation bias and polarization

    CERN Document Server

    Del Vicario, Michela; Caldarelli, Guido; Stanley, H Eugene; Quattrociocchi, Walter

    2016-01-01

    Online users tend to select claims that adhere to their system of beliefs and to ignore dissenting information. Confirmation bias, indeed, plays a pivotal role in viral phenomena. Furthermore, the wide availability of content on the web fosters the aggregation of likeminded people where debates tend to enforce group polarization. Such a configuration might alter the public debate and thus the formation of the public opinion. In this paper we provide a mathematical model to study online social debates and the related polarization dynamics. We assume the basic updating rule of the Bounded Confidence Model (BCM) and we develop two variations a) the Rewire with Bounded Confidence Model (RBCM), in which discordant links are broken until convergence is reached; and b) the Unbounded Confidence Model, under which the interaction among discordant pairs of users is allowed even with a negative feedback, either with the rewiring step (RUCM) or without it (UCM). From numerical simulations we find that the new models (UCM...

  18. Dynamic Nigrostriatal Dopamine Biases Action Selection.

    Science.gov (United States)

    Howard, Christopher D; Li, Hao; Geddes, Claire E; Jin, Xin

    2017-03-22

    Dopamine is thought to play a critical role in reinforcement learning and goal-directed behavior, but its function in action selection remains largely unknown. Here we demonstrate that nigrostriatal dopamine biases ongoing action selection. When mice were trained to dynamically switch the action selected at different time points, changes in firing rate of nigrostriatal dopamine neurons, as well as dopamine signaling in the dorsal striatum, were found to be associated with action selection. This dopamine profile is specific to behavioral choice, scalable with interval duration, and doesn't reflect reward prediction error, timing, or value as single factors alone. Genetic deletion of NMDA receptors on dopamine or striatal neurons or optogenetic manipulation of dopamine concentration alters dopamine signaling and biases action selection. These results unveil a crucial role of nigrostriatal dopamine in integrating diverse information for regulating upcoming actions, and they have important implications for neurological disorders, including Parkinson's disease and substance dependence. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. Modeling confirmation bias and polarization

    Science.gov (United States)

    Del Vicario, Michela; Scala, Antonio; Caldarelli, Guido; Stanley, H. Eugene; Quattrociocchi, Walter

    2017-01-01

    Online users tend to select claims that adhere to their system of beliefs and to ignore dissenting information. Confirmation bias, indeed, plays a pivotal role in viral phenomena. Furthermore, the wide availability of content on the web fosters the aggregation of likeminded people where debates tend to enforce group polarization. Such a configuration might alter the public debate and thus the formation of the public opinion. In this paper we provide a mathematical model to study online social debates and the related polarization dynamics. We assume the basic updating rule of the Bounded Confidence Model (BCM) and we develop two variations a) the Rewire with Bounded Confidence Model (RBCM), in which discordant links are broken until convergence is reached; and b) the Unbounded Confidence Model, under which the interaction among discordant pairs of users is allowed even with a negative feedback, either with the rewiring step (RUCM) or without it (UCM). From numerical simulations we find that the new models (UCM and RUCM), unlike the BCM, are able to explain the coexistence of two stable final opinions, often observed in reality. Lastly, we present a mean field approximation of the newly introduced models. PMID:28074874

  20. Modeling confirmation bias and polarization

    Science.gov (United States)

    Del Vicario, Michela; Scala, Antonio; Caldarelli, Guido; Stanley, H. Eugene; Quattrociocchi, Walter

    2017-01-01

    Online users tend to select claims that adhere to their system of beliefs and to ignore dissenting information. Confirmation bias, indeed, plays a pivotal role in viral phenomena. Furthermore, the wide availability of content on the web fosters the aggregation of likeminded people where debates tend to enforce group polarization. Such a configuration might alter the public debate and thus the formation of the public opinion. In this paper we provide a mathematical model to study online social debates and the related polarization dynamics. We assume the basic updating rule of the Bounded Confidence Model (BCM) and we develop two variations a) the Rewire with Bounded Confidence Model (RBCM), in which discordant links are broken until convergence is reached; and b) the Unbounded Confidence Model, under which the interaction among discordant pairs of users is allowed even with a negative feedback, either with the rewiring step (RUCM) or without it (UCM). From numerical simulations we find that the new models (UCM and RUCM), unlike the BCM, are able to explain the coexistence of two stable final opinions, often observed in reality. Lastly, we present a mean field approximation of the newly introduced models.

  1. Selection bias and measures of inequality

    OpenAIRE

    Vazquez-alvarez, Rosalia; Melenberg, Bertrand; Soest, Arthur van

    2002-01-01

    Variables typically used to measure inequality (e.g., wage earnings, household income or expenditure), are often plagued by nonrandom item nonresponse. Ignoring non-respondents or making (often untestable) assumptions on the nonresponse sub-population can lead to selection bias on estimates of inequality. This paper draws on the approach by Manski (1989,1994) to derive bounding intervals on both the Gini coefficient and the Inter-Quartile range. Both sets of bounds provide alternative measure...

  2. The Truth and Bias Model of Judgment

    Science.gov (United States)

    West, Tessa V.; Kenny, David A.

    2011-01-01

    We present a new model for the general study of how the truth and biases affect human judgment. In the truth and bias model, judgments about the world are pulled by 2 primary forces, the truth force and the bias force, and these 2 forces are interrelated. The truth and bias model differentiates force and value, where the force is the strength of…

  3. The Truth and Bias Model of Judgment

    Science.gov (United States)

    West, Tessa V.; Kenny, David A.

    2011-01-01

    We present a new model for the general study of how the truth and biases affect human judgment. In the truth and bias model, judgments about the world are pulled by 2 primary forces, the truth force and the bias force, and these 2 forces are interrelated. The truth and bias model differentiates force and value, where the force is the strength of…

  4. Selection bias in dynamically measured supermassive black hole samples: scaling relations and correlations between residuals in semi-analytic galaxy formation models

    Science.gov (United States)

    Barausse, Enrico; Shankar, Francesco; Bernardi, Mariangela; Dubois, Yohan; Sheth, Ravi K.

    2017-07-01

    Recent work has confirmed that the scaling relations between the masses of supermassive black holes and host-galaxy properties such as stellar masses and velocity dispersions may be biased high. Much of this may be caused by the requirement that the black hole sphere of influence must be resolved for the black hole mass to be reliably estimated. We revisit this issue with a comprehensive galaxy evolution semi-analytic model. Once tuned to reproduce the (mean) correlation of black hole mass with velocity dispersion, the model cannot account for the correlation with stellar mass. This is independent of the model's parameters, thus suggesting an internal inconsistency in the data. The predicted distributions, especially at the low-mass end, are also much broader than observed. However, if selection effects are included, the model's predictions tend to align with the observations. We also demonstrate that the correlations between the residuals of the scaling relations are more effective than the relations themselves at constraining models for the feedback of active galactic nuclei (AGNs). In fact, we find that our model, while in apparent broad agreement with the scaling relations when accounting for selection biases, yields very weak correlations between their residuals at fixed stellar mass, in stark contrast with observations. This problem persists when changing the AGN feedback strength, and is also present in the hydrodynamic cosmological simulation Horizon-AGN, which includes state-of-the-art treatments of AGN feedback. This suggests that current AGN feedback models are too weak or simply not capturing the effect of the black hole on the stellar velocity dispersion.

  5. Selection bias and the perils of benchmarking.

    Science.gov (United States)

    Denrell, Jerker

    2005-04-01

    To find the secrets of business success, what could be more natural than studying successful businesses? In fact, nothing could be more dangerous, warns this Stanford professor. Generalizing from the examples of successful companies is like generalizing about New England weather from data taken only in the summer. That's essentially what businesspeople do when they learn from good examples and what consultants, authors, and researchers do when they study only existing companies or--worse yet--only high-performing companies. They reach conclusions from unrepresentative data samples, falling into the classic statistical trap of selection bias. Drawing on a wealth of case studies, for instance, one researcher concluded that great leaders share two key traits: They persist, often despite initial failures, and they are able to persuade others to join them. But those traits are also the hallmarks of spectacularly unsuccessful entrepreneurs, who must persist in the face of failure to incur large losses and must be able to persuade others to pour their money down the drain. To discover what makes a business successful, then, managers should look at both successes and failures. Otherwise, they will overvalue risky business practices, seeing only those companies that won big and not the ones that lost dismally. They will not be able to tell if their current good fortune stems from smart business practices or if they are actually coasting on past accomplishments or good luck. Fortunately, economists have developed relatively simple tools that can correct for selection bias even when data about failed companies are hard to come by. Success may be inspirational, but managers are more likely to find the secrets of high performance if they give the stories of their competitors'failures as full a hearing as they do the stories of dazzling successes.

  6. Estimating Sampling Selection Bias in Human Genetics: A Phenomenological Approach

    Science.gov (United States)

    Risso, Davide; Taglioli, Luca; De Iasio, Sergio; Gueresi, Paola; Alfani, Guido; Nelli, Sergio; Rossi, Paolo; Paoli, Giorgio; Tofanelli, Sergio

    2015-01-01

    This research is the first empirical attempt to calculate the various components of the hidden bias associated with the sampling strategies routinely-used in human genetics, with special reference to surname-based strategies. We reconstructed surname distributions of 26 Italian communities with different demographic features across the last six centuries (years 1447–2001). The degree of overlapping between "reference founding core" distributions and the distributions obtained from sampling the present day communities by probabilistic and selective methods was quantified under different conditions and models. When taking into account only one individual per surname (low kinship model), the average discrepancy was 59.5%, with a peak of 84% by random sampling. When multiple individuals per surname were considered (high kinship model), the discrepancy decreased by 8–30% at the cost of a larger variance. Criteria aimed at maximizing locally-spread patrilineages and long-term residency appeared to be affected by recent gene flows much more than expected. Selection of the more frequent family names following low kinship criteria proved to be a suitable approach only for historically stable communities. In any other case true random sampling, despite its high variance, did not return more biased estimates than other selective methods. Our results indicate that the sampling of individuals bearing historically documented surnames (founders' method) should be applied, especially when studying the male-specific genome, to prevent an over-stratification of ancient and recent genetic components that heavily biases inferences and statistics. PMID:26452043

  7. Bias-correction in vector autoregressive models

    DEFF Research Database (Denmark)

    Engsted, Tom; Pedersen, Thomas Quistgaard

    2014-01-01

    We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study......, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable...... improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find...

  8. Biased selection in Twin Cities health plans.

    Science.gov (United States)

    Dowd, B; Feldman, R

    1985-01-01

    The data in Tables 1 through 4 show significant differences in the enrollment of higher health-related financial risk individuals and their families among health plans. FFS enrollees are older and exhibit more chronic illness on average. IPAs enroll a greater proportion of females than do PGP or FFS plans. PGPs and IPAs do not differ significantly in the age and chronic illness of their enrollees, but IPAs enroll a significantly greater proportion of females than do PGPs. The age difference between FFS and prepaid plans appears to be greater for long-term enrollees. The same pattern is true of chronic illness, but the results are often not statistically significant. We do not have time-series data, however, and cannot conclude that future comparisons among long-term enrollees will remains as they are now. In any care our data do not support the hypothesis that biased selection is a short-term problem that will be corrected as the population in prepaid plans ages. Our data contain a cross-section of environments for health plans in firms: long- and short-term offerings, long- and short-term enrollees, high and low out-of-pocket premium costs, etc. Our strongest results are the simplest: across all plans and environments there are significant differences in enrollee characteristics. These differences would not be inefficient if all groups paid actuarially fair premiums. However, mandatory offering and community-rating allow prepaid plans to enroll a younger population with less chronic illness and to maintain an information asymmetry that prevents employers and employees from determining--either prior to or following enrollment--the relationship of the prepaid plan's premium to its marginal cost.

  9. Perceptual and performance biases in action selection

    OpenAIRE

    2008-01-01

    When we see an object in the world, there may be a large number of different ways to interact with that object. This large 'visuomotor space' can be constrained through affordances (perceptually available object properties defining potential uses), task demands and the actor's intentions. The effects of perceptual biases can be modified by performance factors, such as a limb's end-state-comfort (ESC; Rosenbaum et al. 1990). We investigated how two other potential performance biases affected i...

  10. Select strengths and biases of models in representing the Arctic winter boundary layer over sea ice: the Larcform 1 single column model intercomparison

    Science.gov (United States)

    Pithan, Felix; Ackerman, Andrew; Angevine, Wayne M.; Hartung, Kerstin; Ickes, Luisa; Kelley, Maxwell; Medeiros, Brian; Sandu, Irina; Steeneveld, Gert-Jan; Sterk, H. A. M.; Svensson, Gunilla; Vaillancourt, Paul A.; Zadra, Ayrton

    2016-09-01

    Weather and climate models struggle to represent lower tropospheric temperature and moisture profiles and surface fluxes in Arctic winter, partly because they lack or misrepresent physical processes that are specific to high latitudes. Observations have revealed two preferred states of the Arctic winter boundary layer. In the cloudy state, cloud liquid water limits surface radiative cooling, and temperature inversions are weak and elevated. In the radiatively clear state, strong surface radiative cooling leads to the build-up of surface-based temperature inversions. Many large-scale models lack the cloudy state, and some substantially underestimate inversion strength in the clear state. Here, the transformation from a moist to a cold dry air mass is modeled using an idealized Lagrangian perspective. The trajectory includes both boundary layer states, and the single-column experiment is the first Lagrangian Arctic air formation experiment (Larcform 1) organized within GEWEX GASS (Global atmospheric system studies). The intercomparison reproduces the typical biases of large-scale models: some models lack the cloudy state of the boundary layer due to the representation of mixed-phase microphysics or to the interaction between micro- and macrophysics. In some models, high emissivities of ice clouds or the lack of an insulating snow layer prevent the build-up of surface-based inversions in the radiatively clear state. Models substantially disagree on the amount of cloud liquid water in the cloudy state and on turbulent heat fluxes under clear skies. Observations of air mass transformations including both boundary layer states would allow for a tighter constraint of model behavior.

  11. Observations and Models of Galaxy Assembly Bias

    Science.gov (United States)

    Campbell, Duncan A.

    2017-01-01

    The assembly history of dark matter haloes imparts various correlations between a halo’s physical properties and its large scale environment, i.e. assembly bias. It is common for models of the galaxy-halo connection to assume that galaxy properties are only a function of halo mass, implicitly ignoring how assembly bias may affect galaxies. Recently, programs to model and constrain the degree to which galaxy properties are influenced by assembly bias have been undertaken; however, the extent and character of galaxy assembly bias remains a mystery. Nevertheless, characterizing and modeling galaxy assembly bias is an important step in understanding galaxy evolution and limiting any systematic effects assembly bias may pose in cosmological measurements using galaxy surveys.I will present work on modeling and constraining the effect of assembly bias in two galaxy properties: stellar mass and star-formation rate. Conditional abundance matching allows for these galaxy properties to be tied to halo formation history to a variable degree, making studies of the relative strength of assembly bias possible. Galaxy-galaxy clustering and galactic conformity, the degree to which galaxy color is correlated between neighbors, are sensitive observational measures of galaxy assembly bias. I will show how these measurements can be used to constrain galaxy assembly bias and the peril of ignoring it.

  12. [Inverse probability weighting (IPW) for evaluating and "correcting" selection bias].

    Science.gov (United States)

    Narduzzi, Silvia; Golini, Martina Nicole; Porta, Daniela; Stafoggia, Massimo; Forastiere, Francesco

    2014-01-01

    the Inverse probability weighting (IPW) is a methodology developed to account for missingness and selection bias caused by non-randomselection of observations, or non-random lack of some information in a subgroup of the population. to provide an overview of IPW methodology and an application in a cohort study of the association between exposure to traffic air pollution (nitrogen dioxide, NO₂) and 7-year children IQ. this methodology allows to correct the analysis by weighting the observations with the probability of being selected. The IPW is based on the assumption that individual information that can predict the probability of inclusion (non-missingness) are available for the entire study population, so that, after taking account of them, we can make inferences about the entire target population starting from the nonmissing observations alone.The procedure for the calculation is the following: firstly, we consider the entire population at study and calculate the probability of non-missing information using a logistic regression model, where the response is the nonmissingness and the covariates are its possible predictors.The weight of each subject is given by the inverse of the predicted probability. Then the analysis is performed only on the non-missing observations using a weighted model. IPW is a technique that allows to embed the selection process in the analysis of the estimates, but its effectiveness in "correcting" the selection bias depends on the availability of enough information, for the entire population, to predict the non-missingness probability. In the example proposed, the IPW application showed that the effect of exposure to NO2 on the area of verbal intelligence quotient of children is stronger than the effect showed from the analysis performed without regard to the selection processes.

  13. Target selection biases from recent experience transfer across effectors.

    Science.gov (United States)

    Moher, Jeff; Song, Joo-Hyun

    2016-02-01

    Target selection is often biased by an observer's recent experiences. However, not much is known about whether these selection biases influence behavior across different effectors. For example, does looking at a red object make it easier to subsequently reach towards another red object? In the current study, we asked observers to find the uniquely colored target object on each trial. Randomly intermixed pre-trial cues indicated the mode of action: either an eye movement or a visually guided reach movement to the target. In Experiment 1, we found that priming of popout, reflected in faster responses following repetition of the target color on consecutive trials, occurred regardless of whether the effector was repeated from the previous trial or not. In Experiment 2, we examined whether an inhibitory selection bias away from a feature could transfer across effectors. While priming of popout reflects both enhancement of the repeated target features and suppression of the repeated distractor features, the distractor previewing effect isolates a purely inhibitory component of target selection in which a previewed color is presented in a homogenous display and subsequently inhibited. Much like priming of popout, intertrial suppression biases in the distractor previewing effect transferred across effectors. Together, these results suggest that biases for target selection driven by recent trial history transfer across effectors. This indicates that representations in memory that bias attention towards or away from specific features are largely independent from their associated actions.

  14. Biased selection within the social health insurance market in Colombia.

    Science.gov (United States)

    Castano, Ramon; Zambrano, Andres

    2006-12-01

    Reducing the impact of insurance market failures with regulations such as community-rated premiums, standardized benefit packages and open enrolment, yield limited effect because they create room for selection bias. The Colombian social health insurance system started a market approach in 1993 expecting to improve performance of preexisting monopolistic insurance funds by exposing them to competition by new entrants. This paper tests the hypothesis that market failures would lead to biased selection favoring new entrants. Two household surveys are analyzed using Self-Reported Health Status and the presence of chronic conditions as prospective indicators of individual risk. Biased selection is found to take place, leading to adverse selection among incumbents, and favorable selection among new entrants. This pattern is absent in 1997 but is evident in 2003. Given that the two incumbents analyzed are public organizations, the fiscal implications of the findings in terms of government bailouts, are analyzed.

  15. Addressing selection bias in dental health services research.

    Science.gov (United States)

    Lee, J Y; Rozier, R G; Norton, E C; Vann, W F

    2005-10-01

    When randomization is not possible, researchers must control for non-random assignment to experimental groups. One technique for statistical adjustment for non-random assignment is through the use of a two-stage analytical technique. The purpose of this study was to demonstrate the use of this technique to control for selection bias in examining the effects of the The Supplemental Program for Women, Infants, and Children's (WIC) on dental visits. From 5 data sources, an analysis file was constructed for 49,512 children ages 1-5 years. The two-stage technique was used to control for selection bias in WIC participation, the potentially endogenous variable. Specification tests showed that WIC participation was not random and that selection bias was present. The effects of the WIC on dental use differed by 36% after adjustment for selection bias by means of the two-stage technique. This technique can be used to control for potential selection bias in dental research when randomization is not possible.

  16. Bias Modeling for Distantly Supervised Relation Extraction

    Directory of Open Access Journals (Sweden)

    Yang Xiang

    2015-01-01

    Full Text Available Distant supervision (DS automatically annotates free text with relation mentions from existing knowledge bases (KBs, providing a way to alleviate the problem of insufficient training data for relation extraction in natural language processing (NLP. However, the heuristic annotation process does not guarantee the correctness of the generated labels, promoting a hot research issue on how to efficiently make use of the noisy training data. In this paper, we model two types of biases to reduce noise: (1 bias-dist to model the relative distance between points (instances and classes (relation centers; (2 bias-reward to model the possibility of each heuristically generated label being incorrect. Based on the biases, we propose three noise tolerant models: MIML-dist, MIML-dist-classify, and MIML-reward, building on top of a state-of-the-art distantly supervised learning algorithm. Experimental evaluations compared with three landmark methods on the KBP dataset validate the effectiveness of the proposed methods.

  17. Player Selection Bias in National Football League Draftees.

    Science.gov (United States)

    Beyer, Kyle S; Fukuda, David H; Redd, Michael J; Stout, Jeffrey R; Hoffman, Jay R

    2016-11-01

    Beyer, KS, Fukuda, DH, Redd, MJ, Stout, JR, and Hoffman, JR. Player selection bias in National Football League draftees. J Strength Cond Res 30(11): 2965-2971, 2016-Relative age effects (RAEs) have been studied as a potential factor associated with player selection bias in numerous sports. However, little research has examined the role of RAEs among National Football League (NFL) draftees. The purpose of the current study was to determine the existence of RAEs in NFL draftees from the last 10 NFL drafts. Draftee birth dates were collected and divided into calendar and scholastic quarters (SQ1-SQ4). To determine the presence of RAEs in specific subsets, NFL draftees were grouped according to round drafted, position, level of conference play, and age at the time of the draft. Significant χ tests (p ≤ 0.05) comparing observed birth-date distributions vs. the expected birth-date distribution from the general population were followed up by calculating the standardized residual for each quarter (z > ±2.0 indicating significance). Overall, no RAEs were seen when birth-date distribution was assessed using calendar quarters (p = 0.47), but more draftees were born in SQ2 (December-February) than expected (p < 0.01; z = +2.2). Significantly more draftees were born in SQ2 than expected for middle-round draftees (p = 0.01; z = +2.4), skill positions (p = 0.03; z = +2.3), Power Five college draftees (p < 0.01; z = +2.6), and early draftees (p < 0.01; z = +3.1). However, reverse RAEs were seen among late draftees, with fewer draftees being born in SQ2 (z = -3.6) and more being born in SQ4 (June-August; z = +2.6) than expected. In contrast to previous research, the current study observed significant RAEs in NFL draftees from the last 10 years. This player selection bias should be considered when evaluating long-term athlete development models in American football.

  18. Selection bias in the reported performances of AD classification pipelines.

    Science.gov (United States)

    Mendelson, Alex F; Zuluaga, Maria A; Lorenzi, Marco; Hutton, Brian F; Ourselin, Sébastien

    2017-01-01

    The last decade has seen a great proliferation of supervised learning pipelines for individual diagnosis and prognosis in Alzheimer's disease. As more pipelines are developed and evaluated in the search for greater performance, only those results that are relatively impressive will be selected for publication. We present an empirical study to evaluate the potential for optimistic bias in classification performance results as a result of this selection. This is achieved using a novel, resampling-based experiment design that effectively simulates the optimisation of pipeline specifications by individuals or collectives of researchers using cross validation with limited data. Our findings indicate that bias can plausibly account for an appreciable fraction (often greater than half) of the apparent performance improvement associated with the pipeline optimisation, particularly in small samples. We discuss the consistency of our findings with patterns observed in the literature and consider strategies for bias reduction and mitigation.

  19. Sex-Specific Selection and Sex-Biased Gene Expression in Humans and Flies.

    Science.gov (United States)

    Cheng, Changde; Kirkpatrick, Mark

    2016-09-01

    Sexual dimorphism results from sex-biased gene expression, which evolves when selection acts differently on males and females. While there is an intimate connection between sex-biased gene expression and sex-specific selection, few empirical studies have studied this relationship directly. Here we compare the two on a genome-wide scale in humans and flies. We find a distinctive "Twin Peaks" pattern in humans that relates the strength of sex-specific selection, quantified by genetic divergence between male and female adults at autosomal loci, to the degree of sex-biased expression. Genes with intermediate degrees of sex-biased expression show evidence of ongoing sex-specific selection, while genes with either little or completely sex-biased expression do not. This pattern apparently results from differential viability selection in males and females acting in the current generation. The Twin Peaks pattern is also found in Drosophila using a different measure of sex-specific selection acting on fertility. We develop a simple model that successfully recapitulates the Twin Peaks. Our results suggest that many genes with intermediate sex-biased expression experience ongoing sex-specific selection in humans and flies.

  20. Sex-Specific Selection and Sex-Biased Gene Expression in Humans and Flies.

    Directory of Open Access Journals (Sweden)

    Changde Cheng

    2016-09-01

    Full Text Available Sexual dimorphism results from sex-biased gene expression, which evolves when selection acts differently on males and females. While there is an intimate connection between sex-biased gene expression and sex-specific selection, few empirical studies have studied this relationship directly. Here we compare the two on a genome-wide scale in humans and flies. We find a distinctive "Twin Peaks" pattern in humans that relates the strength of sex-specific selection, quantified by genetic divergence between male and female adults at autosomal loci, to the degree of sex-biased expression. Genes with intermediate degrees of sex-biased expression show evidence of ongoing sex-specific selection, while genes with either little or completely sex-biased expression do not. This pattern apparently results from differential viability selection in males and females acting in the current generation. The Twin Peaks pattern is also found in Drosophila using a different measure of sex-specific selection acting on fertility. We develop a simple model that successfully recapitulates the Twin Peaks. Our results suggest that many genes with intermediate sex-biased expression experience ongoing sex-specific selection in humans and flies.

  1. A model for codon position bias in RNA editing

    CERN Document Server

    Liu, T; Liu, Tsunglin; Bundschuh, Ralf

    2005-01-01

    RNA editing can be crucial for the expression of genetic information via inserting, deleting, or substituting a few nucleotides at specific positions in an RNA sequence. Within coding regions in an RNA sequence, editing usually occurs with a certain bias in choosing the positions of the editing sites. In the mitochondrial genes of {\\it Physarum polycephalum}, many more editing events have been observed at the third codon position than at the first and second, while in some plant mitochondria the second codon position dominates. Here we propose an evolutionary model that explains this bias as the basis of selection at the protein level. The model predicts a distribution of the three positions rather close to the experimental observation in {\\it Physarum}. This suggests that the codon position bias in {\\it Physarum} is mainly a consequence of selection at the protein level.

  2. Modeling Rater Disagreement for ADHD: Are Parents or Teachers Biased?

    Science.gov (United States)

    Hartman, Christie A.; Rhee, Soo H.; Willcutt, Erik G.; Pennington, Bruce F

    2007-01-01

    The present study is the first to utilize twin modeling to examine whether parent-teacher disagreement for ADHD ratings is due to parent or teacher bias, or due to raters observing different but valid ADHD behaviors. A joint analysis was conducted with 106 twin pairs, including twins selected for ADHD and control twin pairs. Total ADHD scores were…

  3. Adaptive Unified Biased Estimators of Parameters in Linear Model

    Institute of Scientific and Technical Information of China (English)

    Hu Yang; Li-xing Zhu

    2004-01-01

    To tackle multi collinearity or ill-conditioned design matrices in linear models,adaptive biased estimators such as the time-honored Stein estimator,the ridge and the principal component estimators have been studied intensively.To study when a biased estimator uniformly outperforms the least squares estimator,some suficient conditions are proposed in the literature.In this paper,we propose a unified framework to formulate a class of adaptive biased estimators.This class includes all existing biased estimators and some new ones.A suficient condition for outperforming the least squares estimator is proposed.In terms of selecting parameters in the condition,we can obtain all double-type conditions in the literature.

  4. Only pick the right grains: Modelling the bias due to subjective grain-size interval selection for chronometric and fingerprinting approaches.

    Science.gov (United States)

    Dietze, Michael; Fuchs, Margret; Kreutzer, Sebastian

    2016-04-01

    Many modern approaches of radiometric dating or geochemical fingerprinting rely on sampling sedimentary deposits. A key assumption of most concepts is that the extracted grain-size fraction of the sampled sediment adequately represents the actual process to be dated or the source area to be fingerprinted. However, these assumptions are not always well constrained. Rather, they have to align with arbitrary, method-determined size intervals, such as "coarse grain" or "fine grain" with partly even different definitions. Such arbitrary intervals violate principal process-based concepts of sediment transport and can thus introduce significant bias to the analysis outcome (i.e., a deviation of the measured from the true value). We present a flexible numerical framework (numOlum) for the statistical programming language R that allows quantifying the bias due to any given analysis size interval for different types of sediment deposits. This framework is applied to synthetic samples from the realms of luminescence dating and geochemical fingerprinting, i.e. a virtual reworked loess section. We show independent validation data from artificially dosed and subsequently mixed grain-size proportions and we present a statistical approach (end-member modelling analysis, EMMA) that allows accounting for the effect of measuring the compound dosimetric history or geochemical composition of a sample. EMMA separates polymodal grain-size distributions into the underlying transport process-related distributions and their contribution to each sample. These underlying distributions can then be used to adjust grain-size preparation intervals to minimise the incorporation of "undesired" grain-size fractions.

  5. Uncovering Circumbinary Planetary Architectural Properties from Selection Biases

    CERN Document Server

    Li, Gongjie; Tao, Molei

    2016-01-01

    The new discoveries of circumbinary planetary systems shed light on the understanding of planetary system formation. Learning the architectural properties of these systems is essential for constraining the different formation mechanisms. We first revisit the stability limit of circumbinary planets. Next, we focus on eclipsing stellar binaries and obtain an analytical expression for the transit probability in a realistic setting, where finite observation period and planetary orbital precession are included. Then, understanding of the architectural properties of the currently observed transiting systems is refined, based on Bayesian analysis and a series of hypothesis tests. We find 1) it is not a selection bias that the innermost planets reside near the stability limit for eight of the nine observed systems, and this is consistent with a log uniform distribution of the planetary semi-major axis; 2) it is not a selection bias that the planetary and stellar orbits are nearly coplanar ($\\lesssim 3^\\circ$), and th...

  6. Velocity bias in a LCDM model

    CERN Document Server

    Colin, Pierre; Kravtsov, A V; Colin, Pedro; Klypin, Anatoly; Kravtsov, Andrey V.

    2000-01-01

    We use N-body simulations to study the velocity bias of dark matter halos, the difference in the velocity fields of dark matter and halos, in a flat low- density LCDM model. The high force, 2kpc/h, and mass, 10^9Msun/h, resolution allows dark matter halos to survive in very dense environments of groups and clusters making it possible to use halos as galaxy tracers. We find that the velocity bias pvb measured as a ratio of pairwise velocities of the halos to that of the dark matter evolves with time and depends on scale. At high redshifts (z ~5) halos move generally faster than the dark matter almost on all scales: pvb(r)~1.2, r>0.5Mpc/h. At later moments the bias decreases and gets below unity on scales less than r=5Mpc/h: pvb(r)~(0.6-0.8) at z=0. We find that the evolution of the pairwise velocity bias follows and probably is defined by the spatial antibias of the dark matter halos at small scales. One-point velocity bias b_v, defined as the ratio of the rms velocities of halos and dark matter, provides a mo...

  7. Reversed item bias: an integrative model.

    Science.gov (United States)

    Weijters, Bert; Baumgartner, Hans; Schillewaert, Niels

    2013-09-01

    In the recent methodological literature, various models have been proposed to account for the phenomenon that reversed items (defined as items for which respondents' scores have to be recoded in order to make the direction of keying consistent across all items) tend to lead to problematic responses. In this article we propose an integrative conceptualization of three important sources of reversed item method bias (acquiescence, careless responding, and confirmation bias) and specify a multisample confirmatory factor analysis model with 2 method factors to empirically test the hypothesized mechanisms, using explicit measures of acquiescence and carelessness and experimentally manipulated versions of a questionnaire that varies 3 item arrangements and the keying direction of the first item measuring the focal construct. We explain the mechanisms, review prior attempts to model reversed item bias, present our new model, and apply it to responses to a 4-item self-esteem scale (N = 306) and the 6-item Revised Life Orientation Test (N = 595). Based on the literature review and the empirical results, we formulate recommendations on how to use reversed items in questionnaires.

  8. Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

    CERN Document Server

    Richards, Joseph W; Brink, Henrik; Miller, Adam A; Bloom, Joshua S; Butler, Nathaniel R; James, J Berian; Long, James P; Rice, John

    2011-01-01

    Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because a) standard assumptions for machine-learned model selection procedures break down and b) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting (IW), co-training (CT), and active learning (AL). We argue that AL---where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up---i...

  9. Bias Modeling for Distantly Supervised Relation Extraction

    OpenAIRE

    Yang Xiang; Yaoyun Zhang; Xiaolong Wang; Yang Qin; Wenying Han

    2015-01-01

    Distant supervision (DS) automatically annotates free text with relation mentions from existing knowledge bases (KBs), providing a way to alleviate the problem of insufficient training data for relation extraction in natural language processing (NLP). However, the heuristic annotation process does not guarantee the correctness of the generated labels, promoting a hot research issue on how to efficiently make use of the noisy training data. In this paper, we model two types of biases to reduce...

  10. Gender Wage Gap Accounting: The Role of Selection Bias.

    Science.gov (United States)

    Bar, Michael; Kim, Seik; Leukhina, Oksana

    2015-10-01

    Mulligan and Rubinstein (2008) (MR) argued that changing selection of working females on unobservable characteristics, from negative in the 1970s to positive in the 1990s, accounted for nearly the entire closing of the gender wage gap. We argue that their female wage equation estimates are inconsistent. Correcting this error substantially weakens the role of the rising selection bias (39 % versus 78 %) and strengthens the contribution of declining discrimination (42 % versus 7 %). Our findings resonate better with related literature. We also explain why our finding of positive selection in the 1970s provides additional support for MR's main hypothesis that an exogenous rise in the market value of unobservable characteristics contributed to the closing of the gender gap.

  11. Does exclusion of protest zeros and warm-glow bidders cause selection bias in Contingent Valuation?

    DEFF Research Database (Denmark)

    Grammatikopoulou, Ioanna; Olsen, Søren Bøye; Pouta, Eija

    different models to test for the potential impacts of how these positive warm glow and protest zero bidders are treated. We first exclude the warm glow cases, secondly we include them, and, finally, we correct for selection bias by using the Full Information Maximum Likelihood method for grouped data model....... Our findings show that removal of warm glow positive bidders does not distort the WTP estimate in any significant way. However, using the same approach for protest zero bidders, we find strong evidence of selection bias associated with removal of protest zero responses. Specifically, WTP estimates...... obtained after removal of protest responses are found to be biased downwards and the aggregated welfare measures would be significantly underestimated in our case. These results suggest that there could be serious consequences associated with the common approach of removing protest zero bidders in CVM....

  12. Associations among Selective Attention, Memory Bias, Cognitive Errors and Symptoms of Anxiety in Youth

    Science.gov (United States)

    Watts, Sarah E.; Weems, Carl F.

    2006-01-01

    The purpose of this study was to examine the linkages among selective attention, memory bias, cognitive errors, and anxiety problems by testing a model of the interrelations among these cognitive variables and childhood anxiety disorder symptoms. A community sample of 81 youth (38 females and 43 males) aged 9-17 years and their parents completed…

  13. A Model of Inductive Bias Learning

    CERN Document Server

    Baxter, J

    2011-01-01

    A major problem in machine learning is that of inductive bias: how to choose a learner's hypothesis space so that it is large enough to contain a solution to the problem being learnt, yet small enough to ensure reliable generalization from reasonably-sized training sets. Typically such bias is supplied by hand through the skill and insights of experts. In this paper a model for automatically learning bias is investigated. The central assumption of the model is that the learner is embedded within an environment of related learning tasks. Within such an environment the learner can sample from multiple tasks, and hence it can search for a hypothesis space that contains good solutions to many of the problems in the environment. Under certain restrictions on the set of all hypothesis spaces available to the learner, we show that a hypothesis space that performs well on a sufficiently large number of training tasks will also perform well when learning novel tasks in the same environment. Explicit bounds are also de...

  14. Estimates of the average strength of natural selection are not inflated by sampling error or publication bias.

    Science.gov (United States)

    Knapczyk, Frances N; Conner, Jeffrey K

    2007-10-01

    Kingsolver et al.'s review of phenotypic selection gradients from natural populations provided a glimpse of the form and strength of selection in nature and how selection on different organisms and traits varies. Because this review's underlying database could be a key tool for answering fundamental questions concerning natural selection, it has spawned discussion of potential biases inherent in the review process. Here, we explicitly test for two commonly discussed sources of bias: sampling error and publication bias. We model the relationship between variance among selection gradients and sample size that sampling error produces by subsampling large empirical data sets containing measurements of traits and fitness. We find that this relationship was not mimicked by the review data set and therefore conclude that sampling error does not bias estimations of the average strength of selection. Using graphical tests, we find evidence for bias against publishing weak estimates of selection only among very small studies (N<38). However, this evidence is counteracted by excess weak estimates in larger studies. Thus, estimates of average strength of selection from the review are less biased than is often assumed. Devising and conducting straightforward tests for different biases allows concern to be focused on the most troublesome factors.

  15. A Heckman Selection- t Model

    KAUST Repository

    Marchenko, Yulia V.

    2012-03-01

    Sample selection arises often in practice as a result of the partial observability of the outcome of interest in a study. In the presence of sample selection, the observed data do not represent a random sample from the population, even after controlling for explanatory variables. That is, data are missing not at random. Thus, standard analysis using only complete cases will lead to biased results. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. The method was criticized in the literature because of its sensitivity to the normality assumption. In practice, data, such as income or expenditure data, often violate the normality assumption because of heavier tails. We first establish a new link between sample selection models and recently studied families of extended skew-elliptical distributions. Then, this allows us to introduce a selection-t (SLt) model, which models the error distribution using a Student\\'s t distribution. We study its properties and investigate the finite-sample performance of the maximum likelihood estimators for this model. We compare the performance of the SLt model to the conventional Heckman selection-normal (SLN) model and apply it to analyze ambulatory expenditures. Unlike the SLNmodel, our analysis using the SLt model provides statistical evidence for the existence of sample selection bias in these data. We also investigate the performance of the test for sample selection bias based on the SLt model and compare it with the performances of several tests used with the SLN model. Our findings indicate that the latter tests can be misleading in the presence of heavy-tailed data. © 2012 American Statistical Association.

  16. Bias-Correction in Vector Autoregressive Models: A Simulation Study

    Directory of Open Access Journals (Sweden)

    Tom Engsted

    2014-03-01

    Full Text Available We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find that it compares very favorably in non-stationary models.

  17. Correction of biased climate simulated by biased physics through parameter estimation in an intermediate coupled model

    Science.gov (United States)

    Zhang, Xuefeng; Zhang, Shaoqing; Liu, Zhengyu; Wu, Xinrong; Han, Guijun

    2016-09-01

    Imperfect physical parameterization schemes are an important source of model bias in a coupled model and adversely impact the performance of model simulation. With a coupled ocean-atmosphere-land model of intermediate complexity, the impact of imperfect parameter estimation on model simulation with biased physics has been studied. Here, the biased physics is induced by using different outgoing longwave radiation schemes in the assimilation and "truth" models. To mitigate model bias, the parameters employed in the biased longwave radiation scheme are optimized using three different methods: least-squares parameter fitting (LSPF), single-valued parameter estimation and geography-dependent parameter optimization (GPO), the last two of which belong to the coupled model parameter estimation (CMPE) method. While the traditional LSPF method is able to improve the performance of coupled model simulations, the optimized parameter values from the CMPE, which uses the coupled model dynamics to project observational information onto the parameters, further reduce the bias of the simulated climate arising from biased physics. Further, parameters estimated by the GPO method can properly capture the climate-scale signal to improve the simulation of climate variability. These results suggest that the physical parameter estimation via the CMPE scheme is an effective approach to restrain the model climate drift during decadal climate predictions using coupled general circulation models.

  18. Entropy-based gene ranking without selection bias for the predictive classification of microarray data

    Directory of Open Access Journals (Sweden)

    Serafini Maria

    2003-11-01

    Full Text Available Abstract Background We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process. Results With E-RFE, we speed up the recursive feature elimination (RFE with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Conclusions Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

  19. Estimates of External Validity Bias When Impact Evaluations Select Sites Nonrandomly

    Science.gov (United States)

    Bell, Stephen H.; Olsen, Robert B.; Orr, Larry L.; Stuart, Elizabeth A.

    2016-01-01

    Evaluations of educational programs or interventions are typically conducted in nonrandomly selected samples of schools or districts. Recent research has shown that nonrandom site selection can yield biased impact estimates. To estimate the external validity bias from nonrandom site selection, we combine lists of school districts that were…

  20. [Potential selection bias in telephone surveys: landline and mobile phones].

    Science.gov (United States)

    Garcia-Continente, Xavier; Pérez-Giménez, Anna; López, María José; Nebot, Manel

    2014-01-01

    The increasing use of mobile phones in the last decade has decreased landline telephone coverage in Spanish households. This study aimed to analyze sociodemographic characteristics and health indicators by type of telephone service (mobile phone vs. landline or landline and mobile phone). Two telephone surveys were conducted in Spanish samples (February 2010 and February 2011). Multivariate logistic regression analyses were performed to analyze differences in the main sociodemographic characteristics and health indicators according to the type of telephone service available in Spanish households. We obtained 2027 valid responses (1627 landline telephones and 400 mobile phones). Persons contacted through a mobile phone were more likely to be a foreigner, to belong to the manual social class, to have a lower educational level, and to be a smoker than those contacted through a landline telephone. The profile of the population that has only a mobile phone differs from that with a landline telephone. Therefore, telephone surveys that exclude mobile phones could show a selection bias. Copyright © 2013 SESPAS. Published by Elsevier Espana. All rights reserved.

  1. The Return to College: Selection Bias and Dropout Risk

    OpenAIRE

    Hendricks, Lutz; Leukhina, Oksana

    2014-01-01

    We study two long-standing questions: (i) What part of the measured return to education is due to selection? (ii) The ex post return to schooling is higher than the return to most financial assets. How large are the contributions of various frictions to the "high" return to schooling? We focus in particular on the roles of college dropout risk, borrowing constraints, and learning about ability. We develop and calibrate a model of school choice. Key model features are: (i) ability heterogeneit...

  2. Photo-z Estimation: An Example of Nonparametric Conditional Density Estimation under Selection Bias

    CERN Document Server

    Izbicki, Rafael; Freeman, Peter E

    2016-01-01

    Redshift is a key quantity for inferring cosmological model parameters. In photometric redshift estimation, cosmologists use the coarse data collected from the vast majority of galaxies to predict the redshift of individual galaxies. To properly quantify the uncertainty in the predictions, however, one needs to go beyond standard regression and instead estimate the full conditional density f(z|x) of a galaxy's redshift z given its photometric covariates x. The problem is further complicated by selection bias: usually only the rarest and brightest galaxies have known redshifts, and these galaxies have characteristics and measured covariates that do not necessarily match those of more numerous and dimmer galaxies of unknown redshift. Unfortunately, there is not much research on how to best estimate complex multivariate densities in such settings. Here we describe a general framework for properly constructing and assessing nonparametric conditional density estimators under selection bias, and for combining two o...

  3. Improving uncertainty estimation in urban hydrological modeling by statistically describing bias

    Directory of Open Access Journals (Sweden)

    D. Del Giudice

    2013-10-01

    Full Text Available Hydrodynamic models are useful tools for urban water management. Unfortunately, it is still challenging to obtain accurate results and plausible uncertainty estimates when using these models. In particular, with the currently applied statistical techniques, flow predictions are usually overconfident and biased. In this study, we present a flexible and relatively efficient methodology (i to obtain more reliable hydrological simulations in terms of coverage of validation data by the uncertainty bands and (ii to separate prediction uncertainty into its components. Our approach acknowledges that urban drainage predictions are biased. This is mostly due to input errors and structural deficits of the model. We address this issue by describing model bias in a Bayesian framework. The bias becomes an autoregressive term additional to white measurement noise, the only error type accounted for in traditional uncertainty analysis. To allow for bigger discrepancies during wet weather, we make the variance of bias dependent on the input (rainfall or/and output (runoff of the system. Specifically, we present a structured approach to select, among five variants, the optimal bias description for a given urban or natural case study. We tested the methodology in a small monitored stormwater system described with a parsimonious model. Our results clearly show that flow simulations are much more reliable when bias is accounted for than when it is neglected. Furthermore, our probabilistic predictions can discriminate between three uncertainty contributions: parametric uncertainty, bias, and measurement errors. In our case study, the best performing bias description is the output-dependent bias using a log-sinh transformation of data and model results. The limitations of the framework presented are some ambiguity due to the subjective choice of priors for bias parameters and its inability to address the causes of model discrepancies. Further research should focus on

  4. Race-related cognitive test bias in the active study: a mimic model approach.

    Science.gov (United States)

    Aiken Morgan, Adrienne T; Marsiske, Michael; Dzierzewski, Joseph M; Jones, Richard N; Whitfield, Keith E; Johnson, Kathy E; Cresci, Mary K

    2010-10-01

    The present study investigated evidence for race-related test bias in cognitive measures used in the baseline assessment of the ACTIVE clinical trial. Test bias against African Americans has been documented in both cognitive aging and early life span studies. Despite significant mean performance differences, Multiple Indicators Multiple Causes (MIMIC) models suggested most differences were at the construct level. There was little evidence that specific measures put either group at particular advantage or disadvantage and little evidence of cognitive test bias in this sample. Small group differences in education, cognitive status, and health suggest positive selection may have attenuated possible biases.

  5. Observational selection biases in time-delay strong lensing and their impact on cosmography

    Science.gov (United States)

    Collett, Thomas E.; Cunnington, Steven D.

    2016-11-01

    Inferring cosmological parameters from time-delay strong lenses requires a significant investment of telescope time; it is therefore tempting to focus on the systems with the brightest sources, the highest image multiplicities and the widest image separations. We investigate if this selection bias can influence the properties of the lenses studied and the cosmological parameters inferred. Using an ellipsoidal power-law deflector population, we build a sample of double- and quadruple-image systems. Assuming reasonable thresholds on image separation and flux, based on current lens monitoring campaigns, we find that the typical density profile slopes of monitorable lenses are significantly shallower than the input ensemble. From a sample of quads, we find that this selection function can introduce a 3.5 per cent bias on the inferred time-delay distances if the properties of the input ensemble are (incorrectly) used as priors on the lens model. This bias remains at the 2.4 per cent level when high-resolution imaging of the quasar host is used to precisely infer the properties of individual lenses. We also investigate if the lines of sight for monitorable strong lenses are biased. The expectation value for the line-of-sight convergence is increased by 0.009 (0.004) for quads (doubles) implying a 0.9 per cent (0.4 per cent) bias on H0. We therefore conclude that whilst the properties of typical quasar lenses and their lines of sight do deviate from the global population, the total magnitude of this effect is likely to be a subdominant effect for current analyses, but has the potential to be a major systematic for samples of ˜25 or more lenses.

  6. Separating Selection Bias and Non-coverage in Internet Panels using Propensity Matching.

    NARCIS (Netherlands)

    Lensvelt-Mulders, G.J.L.M.; Lugtig, P.J.; Hubregtse, M.

    2009-01-01

    Many internet-panels consist of self-selected respondents and hence cover a relatively small part of the population. Estimates based on Internet-panels therefore may suffer from non-coverage and self-selection bias. One way to correct for these biases is to use adjustment weighting(Lee, 2006). Howev

  7. Hummingbird responses to gender-biased nectar production: are nectar biases maintained by natural or sexual selection?

    Science.gov (United States)

    Carlson, Jane E

    2008-01-01

    Pollinators mediate the evolution of secondary floral traits through both natural and sexual selection. Gender-biased nectar, for example, could be maintained by one or both, depending on the interactions between plants and pollinators. Here, I investigate pollinator responses to gender-biased nectar using the dichogamous herb Chrysothemis friedrichsthaliana (Gesneriaceae) which produces more nectar during the male floral phase. Previous research showed that the hummingbird pollinator Phaethornis striigularis visited male-phase flowers more often than female-phase flowers, and multiple visits benefited male more than female fecundity. If sexual selection maintains male-biased rewards, hummingbirds should prefer more-rewarding flowers independent of floral gender. If, however, differential rewards are partially maintained through natural selection, hummingbirds should respond to asymmetry with visits that reduce geitonogamy, i.e. selfing and pollen discounting. In plants with male biases, these visit types include single-flower visits and movements from low to high rewards. To test these predictions, I manipulated nectar asymmetry between pairs of real or artificial flowers on plants and recorded foraging behaviour. I also assessed maternal costs of selfing using hand pollinations. For plants with real flowers, hummingbirds preferred more-rewarding flowers and male-phase morphology, the latter possibly owing to previous experience. At artificial arrays, hummingbirds responded to extreme reward asymmetry with increased single-flower visits; however, they moved from high to low rewards more often than low to high. Finally, selfed flowers did not produce inferior seeds. In summary, sexual selection, more so than geitonogamy avoidance, maintains nectar biases in C. friedrichsthaliana, in one of the clearest examples of sexual selection in plants, to date. PMID:18460431

  8. Strong biases in infrared-selected gravitational lenses

    CERN Document Server

    Serjeant, Stephen

    2012-01-01

    Bright submm-selected galaxies have been found to be a rich source of strong gravitational lenses. However, strong gravitational lensing of extended sources leads inevitably to differential magnification. In this paper I quantify the effect of differential magnification on simulated far-infrared and submm surveys of strong gravitational lenses, using a foreground population of Navarro-Frenk-White plus de Vaucouleurs' density profiles, with a model source resembling the Cosmic Eyelash and QSO J1148+5251. Some emission line diagnostics are surprisingly unaffected by differential magnification effects: for example, the bolometric fractions of [C II] 158um and CO(J=1-0), often used to infer densities and ionisation parameters, have typical differential magnification effects that are smaller than the measurement errors. However, the CO ladder itself is significantly affected. Far-infrared lensed galaxy surveys (e.g. at 60um) strongly select for high-redshift galaxies with caustics close to AGN, boosting the appare...

  9. Climate model bias correction and the role of timescales

    Directory of Open Access Journals (Sweden)

    J. O. Haerter

    2010-10-01

    Full Text Available It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing biases. In principle, statistical bias correction methodologies act on model output so the statistical properties of the corrected data match those of the observations. However the improvements to the statistical properties of the data are limited to the specific time scale of the fluctuations that are considered. For example, a statistical bias correction methodology for mean daily values might be detrimental to monthly statistics. Also, in applying bias corrections derived from present day to scenario simulations, an assumption is made of persistence of the bias over the largest timescales. We examine the effects of mixing fluctuations on different time scales and suggest an improved statistical methodology, referred to here as a cascade bias correction method, that eliminates, or greatly reduces, the negative effects.

  10. Heterogeneous individuals' behavioral biases model and numerical simulation

    Institute of Scientific and Technical Information of China (English)

    ZHANG Da-yong; LIANG Guo-wei

    2010-01-01

    A model of the relationships between individual cognitive biases and individual decision-making based on the analysis of cognitive biases of bonded rationality individual,has been established in this paper by introducing a set of new variables callod overconfidence coefficient and attribution bias coefficient to the sentiment model.The irrational expectation and irrational risk aversion as two inseparable aspects of bonded rationality are expressed in an unified model,and a method of measuring individual cognitive biases is proposed,which overcomes the shortcomings of traditional normative models that can not describe the differences of behaviors among heterogeneous individuals.As a result,numerical simulations show that individual cognitive risk is a positive interaction with overconfidence coefficient,and a negative interaction with attribution bias coefficient.

  11. Guidelines for Selecting Bias-Free Textbooks and Storybooks.

    Science.gov (United States)

    Council on Interracial Books for Children, Inc., New York, NY.

    These guidelines are meant to assist parents and educators in identifying books for children that are free of sexism, racism, ageism, and classism. Methods for analyzing both storybooks and textbooks for bias are presented. Special emphasis is placed on evaluating basal readers and history texts. A number of evaluative checklists are presented…

  12. Modeling Temporal Bias of Uplift Events in Recommender Systems

    KAUST Repository

    Altaf, Basmah

    2013-05-08

    Today, commercial industry spends huge amount of resources in advertisement campaigns, new marketing strategies, and promotional deals to introduce their product to public and attract a large number of customers. These massive investments by a company are worthwhile because marketing tactics greatly influence the consumer behavior. Alternatively, these advertising campaigns have a discernible impact on recommendation systems which tend to promote popular items by ranking them at the top, resulting in biased and unfair decision making and loss of customers’ trust. The biasing impact of popularity of items on recommendations, however, is not fixed, and varies with time. Therefore, it is important to build a bias-aware recommendation system that can rank or predict items based on their true merit at given time frame. This thesis proposes a framework that can model the temporal bias of individual items defined by their characteristic contents, and provides a simple process for bias correction. Bias correction is done either by cleaning the bias from historical training data that is used for building predictive model, or by ignoring the estimated bias from the predictions of a standard predictor. Evaluated on two real world datasets, NetFlix and MovieLens, our framework is shown to be able to estimate and remove the bias as a result of adopted marketing techniques from the predicted popularity of items at a given time.

  13. Intron evolution in Neurospora: the role of mutational bias and selection.

    Science.gov (United States)

    Sun, Yu; Whittle, Carrie A; Corcoran, Pádraic; Johannesson, Hanna

    2015-01-01

    We used comparative and population genomics to study intron evolutionary dynamics in the fungal model genus Neurospora. For our investigation, we used well-annotated genomes of N. crassa, N. discreta, and N. tetrasperma, and 92 resequenced genomes of N. tetrasperma from natural populations. By analyzing the four well-annotated genomes, we identified 9495 intron sites in 7619 orthologous genes. Our data supports nonhomologous end joining (NHEJ) and tandem duplication as mechanisms for intron gains in the genus and the RT-mRNA process as a mechanism for intron loss. We found a moderate intron gain rate (5.78-6.89 × 10(-13) intron gains per nucleotide site per year) and a high intron loss rate (7.53-13.76 × 10(-10) intron losses per intron sites per year) as compared to other eukaryotes. The derived intron gains and losses are skewed to high frequencies, relative to neutral SNPs, in natural populations of N. tetrasperma, suggesting that selection is involved in maintaining a high intron turnover. Furthermore, our analyses of the association between intron population-level frequency and genomic features suggest that selection is involved in shaping a 5' intron position bias and a low intron GC content. However, intron sequence analyses suggest that the gained introns were not exposed to recent selective sweeps. Taken together, this work contributes to our understanding of the importance of mutational bias and selection in shaping the intron distribution in eukaryotic genomes.

  14. THEORETICAL ANALYSIS AND PRACTICE ON THE SELECTION OF KEY PARAMETERS FOR HORIZONTAL BIAS BURNER

    Institute of Scientific and Technical Information of China (English)

    刘泰生; 许晋源

    2003-01-01

    The air flow ratio and the pulverized-coal mass flux ratio between the rich and lean sides are the key parameters of horizontal bias burner. In order to realize high combustion efficiency, excellent stability of ignition, low NOx emission and safe operation, six principal demands are presented on the selection of key parameters. An analytical model is established on the basis of the demands, the fundamentals of combustion and the operation results. An improved horizontal bias burner is also presented and applied. The experiment and numerical simulation results show the improved horizontal bias burner can realize proper key parameters, lower NOx emission, high combustion efficiency and excellent performance of part load operation without oil support. It also can reduce the circumfluence and low velocity zone existing at the downstream sections of vanes, and avoid the burnout of the lean primary-air nozzle and the jam in the lean primary-air channel. The operation and test results verify the reasonableness and feasibility of the analytical model.

  15. A theoretical model for analysing gender bias in medicine

    Directory of Open Access Journals (Sweden)

    Johansson Eva E

    2009-08-01

    Full Text Available Abstract During the last decades research has reported unmotivated differences in the treatment of women and men in various areas of clinical and academic medicine. There is an ongoing discussion on how to avoid such gender bias. We developed a three-step-theoretical model to understand how gender bias in medicine can occur and be understood. In this paper we present the model and discuss its usefulness in the efforts to avoid gender bias. In the model gender bias is analysed in relation to assumptions concerning difference/sameness and equity/inequity between women and men. Our model illustrates that gender bias in medicine can arise from assuming sameness and/or equity between women and men when there are genuine differences to consider in biology and disease, as well as in life conditions and experiences. However, gender bias can also arise from assuming differences when there are none, when and if dichotomous stereotypes about women and men are understood as valid. This conceptual thinking can be useful for discussing and avoiding gender bias in clinical work, medical education, career opportunities and documents such as research programs and health care policies. Too meet the various forms of gender bias, different facts and measures are needed. Knowledge about biological differences between women and men will not reduce bias caused by gendered stereotypes or by unawareness of health problems and discrimination associated with gender inequity. Such bias reflects unawareness of gendered attitudes and will not change by facts only. We suggest consciousness-rising activities and continuous reflections on gender attitudes among students, teachers, researchers and decision-makers.

  16. The Body Mass Index-Mortality Link across the Life Course: Two Selection Biases and Their Effects.

    Directory of Open Access Journals (Sweden)

    Hui Zheng

    Full Text Available In this study, we investigated two selection biases that may affect the obesity-mortality link over the life course: mortality selection and healthy participant effects. If these selection mechanisms are stronger among obese adults than among non-obese adults, they may contribute to the weakening obesity-mortality link over the life course. We used data from the National Health and Nutrition Examination Survey 1988-2010 with linked mortality files from 1988-2011. We employed weighted Cox models to test and adjust for these two selection biases. We also used complementary log-log models, adjusted for a normal distribution of frailty, to test for mortality selection effects; accelerated failure-time models to mitigate the mortality selection effect; and ordinary least squares regression to test for healthy participant effects. The link between class II/III obesity and mortality weakens at older ages. We did not find evidence for significant mortality selection or healthy participant effects. Also, even if the healthy participant effects were stronger among obese adults, they are not strong enough to produce a weakening association between obesity and morbidity at higher ages at the time of the survey. Therefore, neither of these selection biases explains the diminishing effect of class II/III obesity on mortality over the life course.

  17. Bias of selection on human copy-number variants.

    Directory of Open Access Journals (Sweden)

    2006-02-01

    Full Text Available Although large-scale copy-number variation is an important contributor to conspecific genomic diversity, whether these variants frequently contribute to human phenotype differences remains unknown. If they have few functional consequences, then copy-number variants (CNVs might be expected both to be distributed uniformly throughout the human genome and to encode genes that are characteristic of the genome as a whole. We find that human CNVs are significantly overrepresented close to telomeres and centromeres and in simple tandem repeat sequences. Additionally, human CNVs were observed to be unusually enriched in those protein-coding genes that have experienced significantly elevated synonymous and nonsynonymous nucleotide substitution rates, estimated between single human and mouse orthologues. CNV genes encode disproportionately large numbers of secreted, olfactory, and immunity proteins, although they contain fewer than expected genes associated with Mendelian disease. Despite mouse CNVs also exhibiting a significant elevation in synonymous substitution rates, in most other respects they do not differ significantly from the genomic background. Nevertheless, they encode proteins that are depleted in olfactory function, and they exhibit significantly decreased amino acid sequence divergence. Natural selection appears to have acted discriminately among human CNV genes. The significant overabundance, within human CNVs, of genes associated with olfaction, immunity, protein secretion, and elevated coding sequence divergence, indicates that a subset may have been retained in the human population due to the adaptive benefit of increased gene dosage. By contrast, the functional characteristics of mouse CNVs either suggest that advantageous gene copies have been depleted during recent selective breeding of laboratory mouse strains or suggest that they were preferentially fixed as a consequence of the larger effective population size of wild mice. It

  18. Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples

    Science.gov (United States)

    Kessler, R.; Scolnic, D.

    2017-02-01

    We present a new technique to create a bin-averaged Hubble diagram (HD) from photometrically identified SN Ia data. The resulting HD is corrected for selection biases and contamination from core-collapse (CC) SNe, and can be used to infer cosmological parameters. This method, called “BEAMS with Bias Corrections” (BBC), includes two fitting stages. The first BBC fitting stage uses a posterior distribution that includes multiple SN likelihoods, a Monte Carlo simulation to bias-correct the fitted SALT-II parameters, and CC probabilities determined from a machine-learning technique. The BBC fit determines (1) a bin-averaged HD (average distance versus redshift), and (2) the nuisance parameters α and β, which multiply the stretch and color (respectively) to standardize the SN brightness. In the second stage, the bin-averaged HD is fit to a cosmological model where priors can be imposed. We perform high-precision tests of the BBC method by simulating large (150,000 event) data samples corresponding to the Dark Energy Survey Supernova Program. Our tests include three models of intrinsic scatter, each with two different CC rates. In the BBC fit, the SALT-II nuisance parameters α and β are recovered to within 1% of their true values. In the cosmology fit, we determine the dark energy equation of state parameter w using a fixed value of {{{Ω }}}{{M}} as a prior: averaging over all six tests based on 6 × 150,000 = 900,000 SNe, there is a small w-bias of 0.006+/- 0.002. Finally, the BBC fitting code is publicly available in the SNANA package.

  19. Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples

    CERN Document Server

    Kessler, Richard

    2016-01-01

    We present a new technique to create a bin-averaged Hubble Diagram (HD) from photometrically identified SN~Ia data. The resulting HD is corrected for selection biases and contamination from core collapse (CC) SNe, and can be used to infer cosmological parameters. This method, called "Bias Corrected Distances" (BCD), includes two fitting stages. The first BCD fitting stage combines a Bayesian likelihood with a Monte Carlo simulation to bias-correct the fitted SALT-II parameters, and also incorporates CC probabilities determined from a machine learning technique. The BCD fit determines 1) a bin-averaged HD (average distance vs. redshift), and 2) the nuisance parameters alpha and beta, which multiply the stretch and color (respectively) to standardize the SN brightness. In the second stage, the bin-averaged HD is fit to a cosmological model where priors can be imposed. We perform high precision tests of the BCD method by simulating large (150,000 event) data samples corresponding to the Dark Energy Survey Supern...

  20. Model Selection for Geostatistical Models

    Energy Technology Data Exchange (ETDEWEB)

    Hoeting, Jennifer A.; Davis, Richard A.; Merton, Andrew A.; Thompson, Sandra E.

    2006-02-01

    We consider the problem of model selection for geospatial data. Spatial correlation is typically ignored in the selection of explanatory variables and this can influence model selection results. For example, the inclusion or exclusion of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as applied to a geostatistical model. We offer a heuristic derivation of the AIC in this context and provide simulation results that show that using AIC for a geostatistical model is superior to the often used approach of ignoring spatial correlation in the selection of explanatory variables. These ideas are further demonstrated via a model for lizard abundance. We also employ the principle of minimum description length (MDL) to variable selection for the geostatistical model. The effect of sampling design on the selection of explanatory covariates is also explored.

  1. Model biases in rice phenology under warmer climates.

    Science.gov (United States)

    Zhang, Tianyi; Li, Tao; Yang, Xiaoguang; Simelton, Elisabeth

    2016-06-07

    Climate-induced crop yields model projections are constrained by the accuracy of the phenology simulation in crop models. Here, we use phenology observations from 775 trials with 19 rice cultivars in 5 Asian countries to compare the performance of four rice phenology models (growing-degree-day (GDD), exponential, beta and bilinear models) when applied to warmer climates. For a given cultivar, the difference in growing season temperature (GST) varied between 2.2 and 8.2 °C in different trials, which allowed us to calibrate the models for lower GST and validate under higher GST, with three calibration experiments. The results show that in warmer climates the bilinear and beta phenology models resulted in gradually increasing bias for phenology predication and double yield bias per percent increase in phenology simulation bias, while the GDD and exponential models maintained a comparatively constant bias. The phenology biases were primarily attributed to varying phenological patterns to temperature in models, rather than on the size of the calibration dataset. Additionally, results suggest that model simulations based on multiple cultivars provide better predictability than using one cultivar. Therefore, to accurately capture climate change impacts on rice phenology, we recommend simulations based on multiple cultivars using the GDD and exponential phenology models.

  2. Model biases in rice phenology under warmer climates

    Science.gov (United States)

    Zhang, Tianyi; Li, Tao; Yang, Xiaoguang; Simelton, Elisabeth

    2016-06-01

    Climate-induced crop yields model projections are constrained by the accuracy of the phenology simulation in crop models. Here, we use phenology observations from 775 trials with 19 rice cultivars in 5 Asian countries to compare the performance of four rice phenology models (growing-degree-day (GDD), exponential, beta and bilinear models) when applied to warmer climates. For a given cultivar, the difference in growing season temperature (GST) varied between 2.2 and 8.2 °C in different trials, which allowed us to calibrate the models for lower GST and validate under higher GST, with three calibration experiments. The results show that in warmer climates the bilinear and beta phenology models resulted in gradually increasing bias for phenology predication and double yield bias per percent increase in phenology simulation bias, while the GDD and exponential models maintained a comparatively constant bias. The phenology biases were primarily attributed to varying phenological patterns to temperature in models, rather than on the size of the calibration dataset. Additionally, results suggest that model simulations based on multiple cultivars provide better predictability than using one cultivar. Therefore, to accurately capture climate change impacts on rice phenology, we recommend simulations based on multiple cultivars using the GDD and exponential phenology models.

  3. Improving uncertainty estimation in urban hydrological modeling by statistically describing bias

    Directory of Open Access Journals (Sweden)

    D. Del Giudice

    2013-04-01

    Full Text Available Hydrodynamic models are useful tools for urban water management. Unfortunately, it is still challenging to obtain accurate results and plausible uncertainty estimates when using these models. In particular, with the currently applied statistical techniques, flow predictions are usually overconfident and biased. In this study, we present a flexible and computationally efficient methodology (i to obtain more reliable hydrological simulations in terms of coverage of validation data by the uncertainty bands and (ii to separate prediction uncertainty into its components. Our approach acknowledges that urban drainage predictions are biased. This is mostly due to input errors and structural deficits of the model. We address this issue by describing model bias in a Bayesian framework. The bias becomes an autoregressive term additional to white measurement noise, the only error type accounted for in traditional uncertainty analysis in urban hydrology. To allow for bigger discrepancies during wet weather, we make the variance of bias dependent on the input (rainfall or/and output (runoff of the system. Specifically, we present a structured approach to select, among five variants, the optimal bias description for a given urban or natural case study. We tested the methodology in a small monitored stormwater system described by means of a parsimonious model. Our results clearly show that flow simulations are much more reliable when bias is accounted for than when it is neglected. Furthermore, our probabilistic predictions can discriminate between three uncertainty contributions: parametric uncertainty, bias (due to input and structural errors, and measurement errors. In our case study, the best performing bias description was the output-dependent bias using a log-sinh transformation of data and model results. The limitations of the framework presented are some ambiguity due to the subjective choice of priors for bias parameters and its inability to directly

  4. Semiparametric efficient and robust estimation of an unknown symmetric population under arbitrary sample selection bias

    KAUST Repository

    Ma, Yanyuan

    2013-09-01

    We propose semiparametric methods to estimate the center and shape of a symmetric population when a representative sample of the population is unavailable due to selection bias. We allow an arbitrary sample selection mechanism determined by the data collection procedure, and we do not impose any parametric form on the population distribution. Under this general framework, we construct a family of consistent estimators of the center that is robust to population model misspecification, and we identify the efficient member that reaches the minimum possible estimation variance. The asymptotic properties and finite sample performance of the estimation and inference procedures are illustrated through theoretical analysis and simulations. A data example is also provided to illustrate the usefulness of the methods in practice. © 2013 American Statistical Association.

  5. Understanding the tropical warm temperature bias simulated by climate models

    Science.gov (United States)

    Brient, Florent; Schneider, Tapio

    2017-04-01

    The state-of-the-art coupled general circulation models have difficulties in representing the observed spatial pattern of surface tempertaure. A majority of them suffers a warm bias in the tropical subsiding regions located over the eastern parts of oceans. These regions are usually covered by low-level clouds scattered from stratus along the coasts to more vertically developed shallow cumulus farther from them. Models usually fail to represent accurately this transition. Here we investigate physical drivers of this warm bias in CMIP5 models through a near-surface energy budget perspective. We show that overestimated solar insolation due to a lack of stratocumulus mostly explains the warm bias. This bias also arises partly from inter-model differences in surface fluxes that could be traced to differences in near-surface relative humidity and air-sea temperature gradient. We investigate the role of the atmosphere in driving surface biases by comparing historical and atmopsheric (AMIP) experiments. We show that some differences in boundary-layer characteristics, mostly those related to cloud fraction and relative humidity, are already present in AMIP experiments and may be the drivers of coupled biases. This gives insights in how models can be improved for better simulations of the tropical climate.

  6. Liberal bias and the five-factor model.

    Science.gov (United States)

    Charney, Evan

    2015-01-01

    Duarte et al. draw attention to the "embedding of liberal values and methods" in social psychological research. They note how these biases are often invisible to the researchers themselves. The authors themselves fall prey to these "invisible biases" by utilizing the five-factor model of personality and the trait of openness to experience as one possible explanation for the under-representation of political conservatives in social psychology. I show that the manner in which the trait of openness to experience is conceptualized and measured is a particularly blatant example of the very liberal bias the authors decry.

  7. Selection biases in observational studies affect associations between 'moderate' alcohol consumption and mortality.

    Science.gov (United States)

    Naimi, Timothy S; Stockwell, Timothy; Zhao, Jinhui; Xuan, Ziming; Dangardt, Frida; Saitz, Rich; Liang, Wenbin; Chikritzhs, Tanya

    2017-02-01

    Selection biases may lead to systematic overestimate of protective effects from 'moderate' alcohol consumption. Overall, most sources of selection bias favor low-volume drinkers in relation to non-drinkers. Studies that attempt to address these types of bias generally find attenuated or non-significant relationships between low-volume alcohol consumption and cardiovascular disease, which is the major source of possible protective effects on mortality from low-volume consumption. Furthermore, observed mortality effects among established low-volume consumers are of limited relevance to health-related decisions about whether to initiate consumption or to continue drinking purposefully into old age. Short of randomized trials with mortality end-points, there are a number of approaches that can minimize selection bias involving low-volume alcohol consumption.

  8. Selective Attention and Drug Related Attention Bias in Methadone Maintenance Patients

    Directory of Open Access Journals (Sweden)

    Majid Nejati

    2011-12-01

    Full Text Available One of the main problems of the drug abusers is drug related attention bias, which causes craving, and as a result drive the drug abusers to take narcotics. Methadone is used as a maintenance treatment for drug abusers. The purpose of this study is evaluation of the effect of Methadone maintenance therapy (MMT on selective attention and drug related attention bias. This study investigated drug cue-related attention bias and selective attention in 16 methadone-maintained patients before and 45 days after methadone therapy period. Stroop color-word test and addiction Stroop test were used as measurement methods. Results show less reaction time and higher accuracy in Color-Word Stroop Test after MMT and less delay for addict related word in addiction Stroop test. It is concluded that methadone can improve selective attention capability and reduce drug related attention bias.

  9. Empirical aspects about Heckman Procedure Application: Is there sample selection bias in the Brazilian Industry

    Directory of Open Access Journals (Sweden)

    Flávio Kaue Fiuza-Moura

    2015-12-01

    Full Text Available There are several labor market researches whose main goal is to analyze the probability of employment and the structure of wage determination and, for empirical purposes, most of these researches deploy Heckman sample selection bias hazard detection and correction procedure. However, few Brazilian studies are focused in this procedure applicability, especially concerning specific industries. This paper aims to approach these issues by testing the existence of sample selection bias in Brazilian manufacturing industry, and to analyze the impact of the bias correction procedure over the estimated coefficients of OLS Mincer equations. We found sample selection bias hazard only in manufacturing segments which average wages are lower than market average and only in groups of workers which average wage level is below the market average (women, especially blacks. The analysis and comparison of Mincer equations with and without Heckman’s sample selection bias correction procedure brought up that the estimation’s coefficients related to wage differential for male over female workers and the wage differential for urban over non-urban workers tends to be overestimated in cases which the sample selection bias isn’t corrected.

  10. Postcopulatory fertilization bias as a form of cryptic sexual selection.

    Science.gov (United States)

    Calsbeek, Ryan; Bonneaud, Camille

    2008-05-01

    Males and females share most of their genetic material yet often experience very different selection pressures. Some traits that are adaptive when expressed in males may therefore be maladaptive when expressed in females. Recent studies demonstrating negative correlations in fitness between parents and their opposite-sex progeny suggest that natural selection may favor a reduction in trait correlations between the sexes to partially mitigate intralocus sexual conflict. We studied sex-specific forms of selection acting in Anolis lizards in the Greater Antilles, a group for which the importance of natural selection has been well documented in species-level diversification, but for which less is known about sexual selection. Using the brown anole (Anolis sagrei), we measured fitness-related variation in morphology (body size), and variation in two traits reflecting whole animal physiological condition: running endurance and immune function. Correlations between body size and physiological traits were opposite between males and females and the form of natural selection acting on physiological traits significantly differed between the sexes. Moreover, physiological traits in progeny were correlated with the body-size of their sires, but correlations were null or even negative between parents and their opposite-sex progeny. Although results based on phenotypic and genetic correlations, as well as the action of natural selection, suggest the potential for intralocus sexual conflict, females used sire body size as a cue to sort sperm for the production of either sons or daughters. Our results suggest that intralocus sexual conflict may be at least partly resolved through post-copulatory sperm choice in A. sagrei.

  11. The Swift Gamma-Ray Burst redshift distribution: selection biases or rate evolution at high-z?

    CERN Document Server

    Coward, David; Branchesi, Marica; Gendre, Bruce; Stratta, Giulia

    2013-01-01

    We employ realistic constraints on selection effects to model the Gamma-Ray Burst (GRB) redshift distribution using {\\it Swift} triggered redshift samples acquired from optical afterglows and the TOUGH survey. Models for the Malmquist bias, redshift desert, and the fraction of afterglows missing because of host galaxy dust extinction, are used to show how the "true" GRB redshift distribution is distorted to its presently observed biased distribution. Our analysis, which accounts for the missing fraction of redshifts in the two data subsets, shows that a combination of selection effects (both instrumental and astrophysical) can describe the observed GRB redshift distribution. The observed distribution supports the case for dust extinction as the dominant astrophysical selection effect that shapes the redshift distribution.

  12. An SIS model for cultural trait transmission with conformity bias.

    Science.gov (United States)

    Walters, Caroline E; Kendal, Jeremy R

    2013-12-01

    Epidemiological models have been applied to human health-related behaviors that are affected by social interaction. Typically these models have not considered conformity bias, that is, the exaggerated propensity to adopt commonly observed behaviors or opinions, or content biases, where the content of the learned trait affects the probability of adoption. Here we consider an interaction of these two effects, presenting an SIS-type model for the spread and persistence of a behavior which is transmitted via social learning. Uptake is controlled by a nonlinear dependence on the proportion of individuals demonstrating the behavior in a population. Three equilibrium solutions are found, their linear stability is analyzed and the results are compared with a model for unbiased social learning. Our analysis focuses on the effects of the strength of conformity bias and the effects of content biases which alter a conformity threshold frequency of the behavior, above which there is an exaggerated propensity for adoption. The strength of the conformity bias is found to qualitatively alter the predictions regarding whether the trait becomes endemic within the population and the proportion of individuals who display the trait when it is endemic. As the conformity strength increases, the number of feasible equilibrium solutions increases from two to three, leading to a situation where the stable equilibrium attained is dependent upon the initial state. Varying the conformity threshold frequency directionally alters the behavior invasion threshold. Finally we discuss the possible application of this model to binge drinking behavior.

  13. Equivalence between Step Selection Functions and Biased Correlated Random Walks for Statistical Inference on Animal Movement.

    Science.gov (United States)

    Duchesne, Thierry; Fortin, Daniel; Rivest, Louis-Paul

    2015-01-01

    Animal movement has a fundamental impact on population and community structure and dynamics. Biased correlated random walks (BCRW) and step selection functions (SSF) are commonly used to study movements. Because no studies have contrasted the parameters and the statistical properties of their estimators for models constructed under these two Lagrangian approaches, it remains unclear whether or not they allow for similar inference. First, we used the Weak Law of Large Numbers to demonstrate that the log-likelihood function for estimating the parameters of BCRW models can be approximated by the log-likelihood of SSFs. Second, we illustrated the link between the two approaches by fitting BCRW with maximum likelihood and with SSF to simulated movement data in virtual environments and to the trajectory of bison (Bison bison L.) trails in natural landscapes. Using simulated and empirical data, we found that the parameters of a BCRW estimated directly from maximum likelihood and by fitting an SSF were remarkably similar. Movement analysis is increasingly used as a tool for understanding the influence of landscape properties on animal distribution. In the rapidly developing field of movement ecology, management and conservation biologists must decide which method they should implement to accurately assess the determinants of animal movement. We showed that BCRW and SSF can provide similar insights into the environmental features influencing animal movements. Both techniques have advantages. BCRW has already been extended to allow for multi-state modeling. Unlike BCRW, however, SSF can be estimated using most statistical packages, it can simultaneously evaluate habitat selection and movement biases, and can easily integrate a large number of movement taxes at multiple scales. SSF thus offers a simple, yet effective, statistical technique to identify movement taxis.

  14. A model for ferrite-loaded transversely biased coaxial resonators

    DEFF Research Database (Denmark)

    Acar, Öncel; Zhurbenko, Vitaliy; Johansen, Tom Keinicke

    2013-01-01

    This work describes a simple model for shortened coaxial cavity resonators with transversely biased ferrite elements. The ferrite allows the resonance frequency to be tuned, and the presented model provides a method of approximately calculating these frequencies to generate the tuning curve...

  15. Modeling The Large Scale Bias of Neutral Hydrogen

    CERN Document Server

    Marin, Felipe; Seo, Hee-Jong; Vallinotto, Alberto

    2009-01-01

    We present analytical estimates of the large scale bias of neutral Hydrogen (HI) based on the Halo Occupation Distribution formalism. We use a simple, non-parametric model which monotonically relates the total mass of a halo with its HI mass at zero redshift; for earlier times we assume limiting models for the HI density parameter evolution, consistent with the data presently available, as well as two main scenarios for the evolution of our HI mass - Halo mass relation. We find that both the linear and the first non-linear bias terms exhibit a remarkable evolution with redshift, regardless of the specific limiting model assumed for the HI evolution. These analytical predictions are then shown to be consistent with measurements performed on the Millennium Simulation. Additionally, we show that this strong bias evolution does not sensibly affect the measurement of the HI Power Spectrum.

  16. The MOSDEF Survey: AGN Multi-wavelength Identification, Selection Biases, and Host Galaxy Properties

    Science.gov (United States)

    Azadi, Mojegan; Coil, Alison L.; Aird, James; Reddy, Naveen; Shapley, Alice; Freeman, William R.; Kriek, Mariska; Leung, Gene C. K.; Mobasher, Bahram; Price, Sedona H.; Sanders, Ryan L.; Shivaei, Irene; Siana, Brian

    2017-01-01

    We present results from the MOSFIRE Deep Evolution Field (MOSDEF) survey on the identification, selection biases, and host galaxy properties of 55 X-ray, IR, and optically selected active galactic nuclei (AGNs) at 1.4optical spectra of galaxies and AGNs and use the BPT diagram to identify optical AGNs. We examine the uniqueness and overlap of the AGNs identified at different wavelengths. There is a strong bias against identifying AGNs at any wavelength in low-mass galaxies, and an additional bias against identifying IR AGNs in the most massive galaxies. AGN hosts span a wide range of star formation rates (SFRs), similar to inactive galaxies once stellar mass selection effects are accounted for. However, we find (at ∼2–3σ significance) that IR AGNs are in less dusty galaxies with relatively higher SFR and optical AGNs in dusty galaxies with relatively lower SFR. X-ray AGN selection does not display a bias with host galaxy SFR. These results are consistent with those from larger studies at lower redshifts. Within star-forming galaxies, once selection biases are accounted for, we find AGNs in galaxies with similar physical properties as inactive galaxies, with no evidence for AGN activity in particular types of galaxies. This is consistent with AGNs being fueled stochastically in any star-forming host galaxy. We do not detect a significant correlation between SFR and AGN luminosity for individual AGN hosts, which may indicate the timescale difference between the growth of galaxies and their supermassive black holes.

  17. Selective drop-out in longitudinal studies and non-biased prediction of behaviour disorders.

    Science.gov (United States)

    Wolke, Dieter; Waylen, Andrea; Samara, Muthanna; Steer, Colin; Goodman, Robert; Ford, Tamsin; Lamberts, Koen

    2009-09-01

    Participant drop-out occurs in all longitudinal studies, and if systematic, may lead to selection biases and erroneous conclusions being drawn from a study. We investigated whether drop out in the Avon Longitudinal Study of Parents And Children (ALSPAC) was systematic or random, and if systematic, whether it had an impact on the prediction of disruptive behaviour disorders. Teacher reports of disruptive behaviour among currently participating, previously participating and never participating children aged 8 years in the ALSPAC longitudinal study were collected. Data on family factors were obtained in pregnancy. Simulations were conducted to explain the impact of selective drop-out on the strength of prediction. Drop out from the ALSPAC cohort was systematic and children who dropped out were more likely to suffer from disruptive behaviour disorder. Systematic participant drop-out according to the family variables, however, did not alter the association between family factors obtained in pregnancy and disruptive behaviour disorder at 8 years of age. Cohort studies are prone to selective drop-out and are likely to underestimate the prevalence of psychiatric disorder. This empirical study and the simulations confirm that the validity of regression models is only marginally affected despite range restrictions after selective drop-out.

  18. The relationship between tropical precipitation biases and the Saharan heat low bias in CMIP5 models

    Science.gov (United States)

    Dixon, Ross D.; Vimont, Daniel J.; Daloz, Anne Sophie

    2017-08-01

    This study focuses on the relationship in global climate models between three features: the Saharan Heat Low (SHL), Sahel precipitation, and the Atlantic Intertropical Convergence Zone (ITCZ). Previous work showed that both coupled (CMIP) and uncoupled (AMIP) ocean/atmosphere models that place the SHL farther to the north are associated with increased precipitation across the Sahel. Further, the northward SHL placement is also associated with a northward shift in the Atlantic ITCZ in coupled CMIP models, but an eastward shift in uncoupled AMIP models. We perform three experiments with the Community Earth System Model to better understand relationships between these features. We find that when a northward-shifted Atlantic ITCZ is locally forced, there is no coherent response in the SHL and Sahel precipitation. However, when a northward-shifted Atlantic ITCZ is forced by altering the cross equatorial energy transport, the SHL shifts northward and Sahel precipitation increases, consistent with model biases. Finally, when the SHL strength is forced directly, there is a weak but robust increase in Sahel precipitation and a northward shift in the Atlantic ITCZ. The results of these experiments emphasize the important role of global scale energy biases on the simulation of West African climate, and show a possible feedback from West African climate onto the Atlantic ITCZ.

  19. Recruiter Selection Model

    Science.gov (United States)

    2006-05-01

    interests include feature selection, statistical learning, multivariate statistics, market research, and classification. He may be contacted at...current youth market , and reducing barriers to Army enlistment. Part of the Army Recruiting Initiatives was the creation of a recruiter selection...Selection Model DevelPed by the Openuier Reseach Crate of E...lneSstm Erapseeeng Depce-teo, WViitd Ntt. siliec Academy, NW..t Point, 271 Weau/’itt 21M

  20. Selection bias in genetic-epidemiological studies of cleft lip and palate

    Energy Technology Data Exchange (ETDEWEB)

    Christensen, K.; Holm, N.V.; Kock, K. (Odense Univ. (Denmark)); Olsen, J. (Aarhus Univ. (Denmark)); Fogh-Anderson, P.

    1992-09-01

    The possible impact of selection bias in genetic and epidemiological studies of cleft lip and palate was studied, using three nationwide ascertainment sources and an autopsy study in a 10% sample of the Danish population. A total of 670 cases were identified. Two national record systems, when used together, were found suitable for ascertaining facial cleft in live births. More than 95% ascertainment was obtained by means of surgical files for cleft lip (with or without cleft palate) without associated malformations/syndromes. However, surgical files could be a poor source for studying isolated cleft palate (CP) (only a 60% and biased ascertainment), and they cannot be used to study the prevalence of associated malformations or syndromes in facial cleft cases. The male:female ratio was 0.88 in surgically treated cases of CP and was 1.5 in nonoperated CP cases, making the overall sex ratio for CP 1.1 (95% confidence limits 0.86-1.4) The sex ratio for CP without associated malformation was 1.1 (95% confidence limits 0.84-1.6). One of the major test criteria in CP multifactorial threshold models (higher CP liability among male CP relatives) must be reconsidered, if other investigations confirm that a CP sex-ratio reversal to male predominance occurs when high ascertainment is achieved. 24 refs., 1 fig., 4 tabs.

  1. Exploring Selective Exposure and Confirmation Bias as Processes Underlying Employee Work Happiness: An Intervention Study.

    Science.gov (United States)

    Williams, Paige; Kern, Margaret L; Waters, Lea

    2016-01-01

    Employee psychological capital (PsyCap), perceptions of organizational virtue (OV), and work happiness have been shown to be associated within and over time. This study examines selective exposure and confirmation bias as potential processes underlying PsyCap, OV, and work happiness associations. As part of a quasi-experimental study design, school staff (N = 69) completed surveys at three time points. After the first assessment, some staff (n = 51) completed a positive psychology training intervention. Results of descriptive statistics, correlation, and regression analyses on the intervention group provide some support for selective exposure and confirmation bias as explanatory mechanisms. In focusing on the processes through which employee attitudes may influence work happiness this study advances theoretical understanding, specifically of selective exposure and confirmation bias in a field study context.

  2. A field test of the extent of bias in selection estimates after accounting for emigration

    Science.gov (United States)

    Letcher, B.H.; Horton, G.E.; Dubreuil, T.L.; O'Donnell, M. J.

    2005-01-01

    Question: To what extent does trait-dependent emigration bias selection estimates in a natural system? Organisms: Two freshwater cohorts of Atlantic salmon (Salmo salar) juveniles. Field site: A 1 km stretch of a small stream (West Brook) in western Massachusetts. USA from which emigration could be detected continuously. Methods: Estimated viability selection differentials for body size either including or ignoring emigration (include = emigrants survived interval, ignore = emigrants did not survive interval) for 12 intervals. Results: Seasonally variable size-related emigration from our study site generated variable levels of bias in selection estimates for body size. The magnitude of this bias was closely related with the extent of size-dependent emigration during each interval. Including or ignoring the effects of emigration changed the significance of selection estimates in 5 of the 12 intervals, and changed the estimated direction of selection in 4 of the 12 intervals. These results indicate the extent to which inferences about selection in a natural system can be biased by failing to account for trait-dependent emigration. ?? 2005 Benjamin H. Letcher.

  3. Biases of the wintertime Arctic Oscillation in CMIP5 models

    Science.gov (United States)

    Gong, Hainan; Wang, Lin; Chen, Wen; Chen, Xiaolong; Nath, Debashis

    2017-01-01

    Distinct biases are found in the pattern and teleconnections of the Arctic Oscillation (AO) in 32 climate models that participate the Coupled Model Intercomparison Project Phase 5 (CMIP5). Compared with observations, the Pacific (Atlantic) center of AO is excessively strong (weak) in most of the 32 CMIP5 models, and the AO-related surface air temperature anomalies are generally weak over the Eurasian continent and North America. These biases are closely tied to the excessively strong linkage, which is marginal in observations, between AO and the North Pacific mode (NPM)—the leading variability of the North Pacific sea level pressure. It implies that the AO in CMIP5 models may be compounded with some regional mode over the North Pacific. Accordingly, a bias-correction method was proposed via correcting the AO index (AOI) to improve the diagnostic estimates of the AO teleconnections. The results suggest that the biases in the pattern and teleconnections of AO can be significantly reduced when the NPM variability is linearly removed from the AOI.

  4. Local model uncertainty and incomplete-data bias

    NARCIS (Netherlands)

    Copas, John; Eguchi, Shinto; Ferguson, Claire; Henderson, Neil; Onabid, Mathias; Parker, Helen; Pritchard, Gareth; Sharif, Maarya; Zhu, Ximin; Wit, Ernst; McGrory, Clare; Barry, Sarah; Fearnside, Alastair; Nguyen, The Mahn; Conte, Rossella Lo; Weir, James; Miller, James; Recchia, Angela; Wit, Ernst; Purutçuoğlu, Vilda; Wit, Ernst

    2005-01-01

    Problems of the analysis of data with incomplete observations are all too familiar in statistics. They are doubly difficult if we are also uncertain about the choice of model. We propose a general formulation for the discussion of such problems and develop approximations to the resulting bias of

  5. Local model uncertainty and incomplete-data bias

    NARCIS (Netherlands)

    Copas, John; Eguchi, Shinto; Ferguson, Claire; Henderson, Neil; Onabid, Mathias; Parker, Helen; Pritchard, Gareth; Sharif, Maarya; Zhu, Ximin; Wit, Ernst; McGrory, Clare; Barry, Sarah; Fearnside, Alastair; Nguyen, The Mahn; Conte, Rossella Lo; Weir, James; Miller, James; Recchia, Angela; Wit, Ernst; Purutçuoğlu, Vilda; Wit, Ernst

    2005-01-01

    Problems of the analysis of data with incomplete observations are all too familiar in statistics. They are doubly difficult if we are also uncertain about the choice of model. We propose a general formulation for the discussion of such problems and develop approximations to the resulting bias of max

  6. Detecting Social Desirability Bias Using Factor Mixture Models

    Science.gov (United States)

    Leite, Walter L.; Cooper, Lou Ann

    2010-01-01

    Based on the conceptualization that social desirable bias (SDB) is a discrete event resulting from an interaction between a scale's items, the testing situation, and the respondent's latent trait on a social desirability factor, we present a method that makes use of factor mixture models to identify which examinees are most likely to provide…

  7. Selection Bias Due to Loss to Follow Up in Cohort Studies.

    Science.gov (United States)

    Howe, Chanelle J; Cole, Stephen R; Lau, Bryan; Napravnik, Sonia; Eron, Joseph J

    2016-01-01

    Selection bias due to loss to follow up represents a threat to the internal validity of estimates derived from cohort studies. Over the past 15 years, stratification-based techniques as well as methods such as inverse probability-of-censoring weighted estimation have been more prominently discussed and offered as a means to correct for selection bias. However, unlike correcting for confounding bias using inverse weighting, uptake of inverse probability-of-censoring weighted estimation as well as competing methods has been limited in the applied epidemiologic literature. To motivate greater use of inverse probability-of-censoring weighted estimation and competing methods, we use causal diagrams to describe the sources of selection bias in cohort studies employing a time-to-event framework when the quantity of interest is an absolute measure (e.g., absolute risk, survival function) or relative effect measure (e.g., risk difference, risk ratio). We highlight that whether a given estimate obtained from standard methods is potentially subject to selection bias depends on the causal diagram and the measure. We first broadly describe inverse probability-of-censoring weighted estimation and then give a simple example to demonstrate in detail how inverse probability-of-censoring weighted estimation mitigates selection bias and describe challenges to estimation. We then modify complex, real-world data from the University of North Carolina Center for AIDS Research HIV clinical cohort study and estimate the absolute and relative change in the occurrence of death with and without inverse probability-of-censoring weighted correction using the modified University of North Carolina data. We provide SAS code to aid with implementation of inverse probability-of-censoring weighted techniques.

  8. Detection of bias in animal model pedigree indices of heifers

    Directory of Open Access Journals (Sweden)

    M. LIDAUER

    2008-12-01

    Full Text Available The objective of the study was to test whether the pedigree indices (PI of heifers are biased, and if so, whether the magnitude of the bias varies in different groups of heifers. Therefore, two animal model evaluations with two different data sets were computed. Data with all the records from the national evaluation in December 1994 was used to obtain estimated breeding values (EBV for 305-days' milk yield and protein yield. In the second evaluation, the PIs were estimated for cows calving the first time in 1993 by excluding all their production records from the data. Three different statistics, a simple t-test, the linear regression of EBV on PI, and the polynomial regression of the difference in the predictions (EBV-PI on PI, were computed for three groups of first parity Ayrshire cows: daughters of proven sires, daughters of young sires, and daughters of bull dam candidates. A practically relevant bias was found only in the PIs for the daughters of young sires. On average their PIs were biased upwards by 0.20 standard deviations (78.8 kg for the milk yield and by 0.21 standard deviations (2.2 kg for the protein yield. The polynomial regression analysis showed that the magnitude of the bias in the PIs changed somewhat with the size of the PIs.;

  9. Correcting biased observation model error in data assimilation

    CERN Document Server

    Harlim, John

    2016-01-01

    While the formulation of most data assimilation schemes assumes an unbiased observation model error, in real applications, model error with nontrivial biases is unavoidable. A practical example is the error in the radiative transfer model (which is used to assimilate satellite measurements) in the presence of clouds. As a consequence, many (in fact 99\\%) of the cloudy observed measurements are not being used although they may contain useful information. This paper presents a novel nonparametric Bayesian scheme which is able to learn the observation model error distribution and correct the bias in incoming observations. This scheme can be used in tandem with any data assimilation forecasting system. The proposed model error estimator uses nonparametric likelihood functions constructed with data-driven basis functions based on the theory of kernel embeddings of conditional distributions developed in the machine learning community. Numerically, we show positive results with two examples. The first example is des...

  10. Accelerated failure time model under general biased sampling scheme.

    Science.gov (United States)

    Kim, Jane Paik; Sit, Tony; Ying, Zhiliang

    2016-07-01

    Right-censored time-to-event data are sometimes observed from a (sub)cohort of patients whose survival times can be subject to outcome-dependent sampling schemes. In this paper, we propose a unified estimation method for semiparametric accelerated failure time models under general biased estimating schemes. The proposed estimator of the regression covariates is developed upon a bias-offsetting weighting scheme and is proved to be consistent and asymptotically normally distributed. Large sample properties for the estimator are also derived. Using rank-based monotone estimating functions for the regression parameters, we find that the estimating equations can be easily solved via convex optimization. The methods are confirmed through simulations and illustrated by application to real datasets on various sampling schemes including length-bias sampling, the case-cohort design and its variants. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  11. Observational selection biases in time-delay strong lensing and their impact on cosmography

    CERN Document Server

    Collett, Thomas E

    2016-01-01

    Inferring cosmological parameters from time-delay strong lenses requires a significant investment of telescope time; it is therefore tempting to focus on the systems with the brightest sources, the highest image multiplicities and the widest image separations. We investigate if this selection bias can influence the properties of the lenses studied and the cosmological parameters that are inferred. Using a population of lenses with ellipsoidal powerlaw density profiles, we build a sample of double and quadruple image systems. Assuming reasonable thresholds on image separation and flux, based on current lens monitoring campaigns, we find that the typical density profile slopes of monitorable lenses are significantly shallower than the input ensemble. From a sample of quadruple image lenses we find that this selection function can introduce a 3.5% bias on the inferred time-delay distances if the ensemble of deflector properties is used as a prior for a cosmographical analysis. This bias remains at the 2.4% level...

  12. Variation in the Intensity of Selection on Codon Bias over Time Causes Contrasting Patterns of Base Composition Evolution in Drosophila

    Science.gov (United States)

    Jackson, Benjamin C.; Campos, José L.; Haddrill, Penelope R.; Charlesworth, Brian

    2017-01-01

    Four-fold degenerate coding sites form a major component of the genome, and are often used to make inferences about selection and demography, so that understanding their evolution is important. Despite previous efforts, many questions regarding the causes of base composition changes at these sites in Drosophila remain unanswered. To shed further light on this issue, we obtained a new whole-genome polymorphism data set from D. simulans. We analyzed samples from the putatively ancestral range of D. simulans, as well as an existing polymorphism data set from an African population of D. melanogaster. By using D. yakuba as an outgroup, we found clear evidence for selection on 4-fold sites along both lineages over a substantial period, with the intensity of selection increasing with GC content. Based on an explicit model of base composition evolution, we suggest that the observed AT-biased substitution pattern in both lineages is probably due to an ancestral reduction in selection intensity, and is unlikely to be the result of an increase in mutational bias towards AT alone. By using two polymorphism-based methods for estimating selection coefficients over different timescales, we show that the selection intensity on codon usage has been rather stable in D. simulans in the recent past, but the long-term estimates in D. melanogaster are much higher than the short-term ones, indicating a continuing decline in selection intensity, to such an extent that the short-term estimates suggest that selection is only active in the most GC-rich parts of the genome. Finally, we provide evidence for complex evolutionary patterns in the putatively neutral short introns, which cannot be explained by the standard GC-biased gene conversion model. These results reveal a dynamic picture of base composition evolution. PMID:28082609

  13. Biased Exposure-Health Effect Estimates from Selection in Cohort Studies: Are Environmental Studies at Particular Risk?

    Science.gov (United States)

    Weisskopf, Marc G; Sparrow, David; Hu, Howard; Power, Melinda C

    2015-11-01

    The process of creating a cohort or cohort substudy may induce misleading exposure-health effect associations through collider stratification bias (i.e., selection bias) or bias due to conditioning on an intermediate. Studies of environmental risk factors may be at particular risk. We aimed to demonstrate how such biases of the exposure-health effect association arise and how one may mitigate them. We used directed acyclic graphs and the example of bone lead and mortality (all-cause, cardiovascular, and ischemic heart disease) among 835 white men in the Normative Aging Study (NAS) to illustrate potential bias related to recruitment into the NAS and the bone lead substudy. We then applied methods (adjustment, restriction, and inverse probability of attrition weighting) to mitigate these biases in analyses using Cox proportional hazards models to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). Analyses adjusted for age at bone lead measurement, smoking, and education among all men found HRs (95% CI) for the highest versus lowest tertile of patella lead of 1.34 (0.90, 2.00), 1.46 (0.86, 2.48), and 2.01 (0.86, 4.68) for all-cause, cardiovascular, and ischemic heart disease mortality, respectively. After applying methods to mitigate the biases, the HR (95% CI) among the 637 men analyzed were 1.86 (1.12, 3.09), 2.47 (1.23, 4.96), and 5.20 (1.61, 16.8), respectively. Careful attention to the underlying structure of the observed data is critical to identifying potential biases and methods to mitigate them. Understanding factors that influence initial study participation and study loss to follow-up is critical. Recruitment of population-based samples and enrolling participants at a younger age, before the potential onset of exposure-related health effects, can help reduce these potential pitfalls. Weisskopf MG, Sparrow D, Hu H, Power MC. 2015. Biased exposure-health effect estimates from selection in cohort studies: are environmental studies at

  14. Co-evolution of black holes and galaxies: the role of selection biases

    Science.gov (United States)

    Portinari, Laura

    2016-10-01

    Quasars are tracers of the cosmological evolution of the Black Hole mass- host galaxy relation, and indicate that the formation of BHanticipated that of the host galaxies. We find that selection effects andstatistical biases dominate the interpretation of the observational results;and co-evolution (= constant BH/galaxy mass ratio) is still compatiblewith observations.

  15. The MOSDEF survey: AGN multi-wavelength identification, selection biases and host galaxy properties

    CERN Document Server

    Azadi, Mojegan; Aird, James; Reddy, Naveen; Shapley, Alice; Freeman, William R; Kriek, Mariska; Leung, Gene C K; Mobasher, Bahram; Price, Sedona H; Sanders, Ryan L; Shivaei, Irene; Siana, Brian

    2016-01-01

    We present results from the MOSFIRE Deep Evolution Field (MOSDEF) survey on the identification, selection biases and host galaxy properties of 55 X-ray, IR and optically-selected active galactic nuclei (AGN) at $1.4 < z < 3.8$. We obtain rest-frame optical spectra of galaxies and AGN and use the BPT diagram to identify optical AGN. We examine the uniqueness and overlap of the AGN identified at different wavelengths. There is a strong bias against identifying AGN at any wavelength in low mass galaxies, and an additional bias against identifying IR AGN in the most massive galaxies. AGN host galaxies span a wide range of star formation rate (SFR), similar to inactive galaxies once stellar mass selection effects are accounted for. However, we generally identify IR AGN in less dusty galaxies with relatively higher SFR and optical AGN in dusty galaxies with relatively lower SFR. X-ray AGN selection does not display a bias with host galaxy SFR. These results are consistent with those from larger studies at low...

  16. Educational Research with Real-World Data: Reducing Selection Bias with Propensity Scores

    Science.gov (United States)

    Adelson, Jill L.

    2013-01-01

    Often it is infeasible or unethical to use random assignment in educational settings to study important constructs and questions. Hence, educational research often uses observational data, such as large-scale secondary data sets and state and school district data, and quasi-experimental designs. One method of reducing selection bias in estimations…

  17. Squeezing the halo bispectrum: a test of bias models

    CERN Document Server

    Dizgah, Azadeh Moradinezhad; Noreña, Jorge; Biagetti, Matteo; Desjacques, Vincent

    2015-01-01

    We study the halo-matter cross bispectrum in the presence of primordial non-Gaussianity of the local type. We restrict ourselves to the squeezed limit, for which the calculation are straightforward, and perform the measurements in the initial conditions of N-body simulations, to mitigate the contamination induced by nonlinear gravitational evolution. Interestingly, the halo-matter cross bispectrum is not trivial even in this simple limit as it is strongly sensitive to the scale-dependence of the quadratic and third-order halo bias. Therefore, it can be used to test biasing prescriptions. We consider three different prescription for halo clustering: excursion set peaks (ESP), local bias and a model in which the halo bias parameters are explicitly derived from a peak-background split. In all cases, the model parameters are fully constrained with statistics other than the cross bispectrum. We measure the cross bispectrum involving one halo fluctuation field and two mass overdensity fields for various halo masses...

  18. Selection bias in family reports on end of life with dementia in nursing homes

    OpenAIRE

    Steen, van, M.; Deliens, L.; Ribbe, M.W.; Onwuteaka-Philipsen, B. D.

    2012-01-01

    Background: Selective participation in retrospective studies of families recruited after the patient's death may threaten generalizability of reports on end-of-life experiences. Objectives: To assess possible selection bias in retrospective study of dementia at the end of life using family reports. Methods: Two physician teams covering six nursing home facilities in the Netherlands reported on 117 of 119 consecutive decedents within two weeks after death unaware of after-death family ...

  19. Religious attendance after elevated depressive symptoms: is selection bias at work?

    Directory of Open Access Journals (Sweden)

    Lloyd Balbuena

    2014-03-01

    Full Text Available In an attempt to determine if selection bias could be a reason that religious attendance and depression are related, the predictive value of elevated depressive symptoms for a decrease in future attendance at religious services was examined in a longitudinal panel of 1,673 Dutch adults. Religious attendance was assessed yearly over five years using the single question, “how often do you attend religious gatherings nowadays?” Depressive symptoms were assessed four times within the first year using the Depression subscale of the Brief Symptom Inventory. Logistic regression models of change in attendance were created, stratifying by baseline attendance status. Attenders who developed elevated symptoms were less likely to subsequently decrease their attendance (relative risk ratio: 0.55, 95% CI [0.38–0.79] relative to baseline as compared to those without elevated symptoms. This inverse association remained significant after controlling for health and demographic covariates, and when using multiply imputed data to account for attrition. Non-attenders were unlikely to start attending after elevated depressive symptoms. This study provides counter evidence against previous findings that church attenders are a self-selected healthier group.

  20. Multigenerational response to artificial selection for biased clutch sex ratios in Tigriopus californicus populations.

    Science.gov (United States)

    Alexander, H J; Richardson, J M L; Anholt, B R

    2014-09-01

    Polygenic sex determination (PSD) is relatively rare and theoretically evolutionary unstable, yet has been reported across a range of taxa. Evidence for multilocus PSD is provided by (i) large between-family variance in sex ratio, (ii) paternal and maternal effects on family sex ratio and (iii) response to selection for family sex ratio. This study tests the polygenic hypothesis of sex determination in the harpacticoid copepod Tigriopus californicus using the criterion of response to selection. We report the first multigenerational quantitative evidence that clutch sex ratio responds to artificial selection in both directions (selection for male- and female-biased families) and in multiple populations of T. californicus. In the five of six lines that showed a response to selection, realized heritability estimated by multigenerational analysis ranged from 0.24 to 0.58. Divergence of clutch sex ratio between selection lines is rapid, with response to selection detectable within the first four generations of selection.

  1. The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models.

    Science.gov (United States)

    Syfert, Mindy M; Smith, Matthew J; Coomes, David A

    2013-01-01

    Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as "feature types" in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.

  2. Born at the wrong time: selection bias in the NHL draft.

    Directory of Open Access Journals (Sweden)

    Robert O Deaner

    Full Text Available Relative age effects (RAEs occur when those who are relatively older for their age group are more likely to succeed. RAEs occur reliably in some educational and athletic contexts, yet the causal mechanisms remain unclear. Here we provide the first direct test of one mechanism, selection bias, which can be defined as evaluators granting fewer opportunities to relatively younger individuals than is warranted by their latent ability. Because RAEs are well-established in hockey, we analyzed National Hockey League (NHL drafts from 1980 to 2006. Compared to those born in the first quarter (i.e., January-March, those born in the third and fourth quarters were drafted more than 40 slots later than their productivity warranted, and they were roughly twice as likely to reach career benchmarks, such as 400 games played or 200 points scored. This selection bias in drafting did not decrease over time, apparently continues to occur, and reduces the playing opportunities of relatively younger players. This bias is remarkable because it is exhibited by professional decision makers evaluating adults in a context where RAEs have been widely publicized. Thus, selection bias based on relative age may be pervasive.

  3. Thermospheric density model biases at the 23rd sunspot maximum

    Science.gov (United States)

    Pardini, C.; Moe, K.; Anselmo, L.

    2012-07-01

    Uncertainties in the neutral density estimation are the major source of aerodynamic drag errors and one of the main limiting factors in the accuracy of the orbit prediction and determination process at low altitudes. Massive efforts have been made over the years to constantly improve the existing operational density models, or to create even more precise and sophisticated tools. Special attention has also been paid to research more appropriate solar and geomagnetic indices. However, the operational models still suffer from weakness. Even if a number of studies have been carried out in the last few years to define the performance improvements, further critical assessments are necessary to evaluate and compare the models at different altitudes and solar activity conditions. Taking advantage of the results of a previous study, an investigation of thermospheric density model biases during the last sunspot maximum (October 1999 - December 2002) was carried out by analyzing the semi-major axis decay of four satellites: Cosmos 2265, Cosmos 2332, SNOE and Clementine. Six thermospheric density models, widely used in spacecraft operations, were analyzed: JR-71, MSISE-90, NRLMSISE-00, GOST-2004, JB2006 and JB2008. During the time span considered, for each satellite and atmospheric density model, a fitted drag coefficient was solved for and then compared with the calculated physical drag coefficient. It was therefore possible to derive the average density biases of the thermospheric models during the maximum of the 23rd solar cycle. Below 500 km, all the models overestimated the average atmospheric density by amounts varying between +7% and +20%. This was an inevitable consequence of constructing thermospheric models from density data obtained by assuming a fixed drag coefficient, independent of altitude. Because the uncertainty affecting the drag coefficient measurements was about 3% at both 200 km and 480 km of altitude, the calculated air density biases below 500 km were

  4. Appropriate microwave frequency selection for biasing superconducting hot electron bolometers as terahertz direct detectors

    Science.gov (United States)

    Jiang, S. L.; Li, X. F.; Jia, X. Q.; Kang, L.; Jin, B. B.; Xu, W. W.; Chen, J.; Wu, P. H.

    2017-04-01

    Terahertz (THz) direct detectors based on superconducting niobium nitride (NbN) hot electron bolometers (HEBs) and biased by a simple microwave (MW) source have been studied. The frequency and power of the MW are selected by measuring the MW responses of the current–voltage (I–V) curves and resistance–temperature (R–T) curves of the NbN HEBs. The non-uniform absorption theory is used to explain the current jumps in the I–V curves and the resistance jumps in the R–T curves. Compared to the thermal biasing, the MW biasing method can improve the sensitivity, make the readout system much easier and consumes less liquid helium, which is important for long lasting experiments. The noise equivalent power (NEP) of 1.6 pW Hz‑1/2 and the response time of 86 ps are obtained for the detectors working at 4.2 K and 0.65 THz.

  5. Exchange bias of patterned systems: Model and numerical simulation

    Energy Technology Data Exchange (ETDEWEB)

    Garcia, Griselda [Facultad de Fisica, P. Universidad Catolica de Chile, Casilla 306, Santiago 7820436 (Chile); Centro para el Desarrollo de la Nanociencia y la Nanotecnologia, CEDENNA, Avda. Ecuador 3493, Santiago (Chile); Kiwi, Miguel, E-mail: mkiwi@puc.c [Facultad de Fisica, P. Universidad Catolica de Chile, Casilla 306, Santiago 7820436 (Chile); Centro para el Desarrollo de la Nanociencia y la Nanotecnologia, CEDENNA, Avda. Ecuador 3493, Santiago (Chile); Mejia-Lopez, Jose; Ramirez, Ricardo [Facultad de Fisica, P. Universidad Catolica de Chile, Casilla 306, Santiago 7820436 (Chile); Centro para el Desarrollo de la Nanociencia y la Nanotecnologia, CEDENNA, Avda. Ecuador 3493, Santiago (Chile)

    2010-11-15

    The magnitude of the exchange bias field of patterned systems exhibits a notable increase in relation to the usual bilayer systems, where a continuous ferromagnetic film is deposited on an antiferromagnet insulator. Here we develop a model, and implement a Monte Carlo calculation, to interpret the experimental observations which is consistent with experimental results, on the basis of assuming a small fraction of spins pinned ferromagnetically in the antiferromagnetic interface layer.

  6. Cross-validation analysis of bias models in Bayesian multi-model projections of climate

    Science.gov (United States)

    Huttunen, J. M. J.; Räisänen, J.; Nissinen, A.; Lipponen, A.; Kolehmainen, V.

    2017-03-01

    Climate change projections are commonly based on multi-model ensembles of climate simulations. In this paper we consider the choice of bias models in Bayesian multimodel predictions. Buser et al. (Clim Res 44(2-3):227-241, 2010a) introduced a hybrid bias model which combines commonly used constant bias and constant relation bias assumptions. The hybrid model includes a weighting parameter which balances these bias models. In this study, we use a cross-validation approach to study which bias model or bias parameter leads to, in a specific sense, optimal climate change projections. The analysis is carried out for summer and winter season means of 2 m-temperatures spatially averaged over the IPCC SREX regions, using 19 model runs from the CMIP5 data set. The cross-validation approach is applied to calculate optimal bias parameters (in the specific sense) for projecting the temperature change from the control period (1961-2005) to the scenario period (2046-2090). The results are compared to the results of the Buser et al. (Clim Res 44(2-3):227-241, 2010a) method which includes the bias parameter as one of the unknown parameters to be estimated from the data.

  7. Quantifying selective reporting and the Proteus phenomenon for multiple datasets with similar bias.

    Directory of Open Access Journals (Sweden)

    Thomas Pfeiffer

    Full Text Available Meta-analyses play an important role in synthesizing evidence from diverse studies and datasets that address similar questions. A major obstacle for meta-analyses arises from biases in reporting. In particular, it is speculated that findings which do not achieve formal statistical significance are less likely reported than statistically significant findings. Moreover, the patterns of bias can be complex and may also depend on the timing of the research results and their relationship with previously published work. In this paper, we present an approach that is specifically designed to analyze large-scale datasets on published results. Such datasets are currently emerging in diverse research fields, particularly in molecular medicine. We use our approach to investigate a dataset on Alzheimer's disease (AD that covers 1167 results from case-control studies on 102 genetic markers. We observe that initial studies on a genetic marker tend to be substantially more biased than subsequent replications. The chances for initial, statistically non-significant results to be published are estimated to be about 44% (95% CI, 32% to 63% relative to statistically significant results, while statistically non-significant replications have almost the same chance to be published as statistically significant replications (84%; 95% CI, 66% to 107%. Early replications tend to be biased against initial findings, an observation previously termed Proteus phenomenon: The chances for non-significant studies going in the same direction as the initial result are estimated to be lower than the chances for non-significant studies opposing the initial result (73%; 95% CI, 55% to 96%. Such dynamic patterns in bias are difficult to capture by conventional methods, where typically simple publication bias is assumed to operate. Our approach captures and corrects for complex dynamic patterns of bias, and thereby helps generating conclusions from published results that are more robust

  8. Selection bias in evaluating of influenza vaccine effectiveness: a lesson from an observational study of elderly nursing home residents.

    Science.gov (United States)

    Fukushima, Wakaba; Hayashi, Yoshimitsu; Mizuno, Yaichi; Suzuki, Kanzo; Kase, Tetsuo; Ohfuji, Satoko; Fujieda, Megumi; Maeda, Akiko; Hirota, Yoshio

    2008-11-25

    Selection bias is of critical concern in the study of influenza vaccine effectiveness when using an observational study design. This bias is attributable to the inherently different characteristics between vaccinees and non-vaccinees. The differences, which are related both to vaccination and signs of clinical disease as an outcome, may lead to erroneous estimation of the effectiveness. In this report, we describe how selection bias among elderly nursing home residents may lead to a spurious interpretation of the protective effect of influenza vaccine. Our results should be a lesson in the importance of regarding selection bias when assessing influenza vaccine effectiveness.

  9. Reducing selection bias in case-control studies from rare disease registries

    Directory of Open Access Journals (Sweden)

    Mistry Pramod K

    2011-09-01

    Full Text Available Abstract Background In clinical research of rare diseases, where small patient numbers and disease heterogeneity limit study design options, registries are a valuable resource for demographic and outcome information. However, in contrast to prospective, randomized clinical trials, the observational design of registries is prone to introduce selection bias and negatively impact the validity of data analyses. The objective of the study was to demonstrate the utility of case-control matching and the risk-set method in order to control bias in data from a rare disease registry. Data from the International Collaborative Gaucher Group (ICGG Gaucher Registry were used as an example. Methods A case-control matching analysis using the risk-set method was conducted to identify two groups of patients with type 1 Gaucher disease in the ICGG Gaucher Registry: patients with avascular osteonecrosis (AVN and those without AVN. The frequency distributions of gender, decade of birth, treatment status, and splenectomy status were presented for cases and controls before and after matching. Odds ratios (and 95% confidence intervals were calculated for each variable before and after matching. Results The application of case-control matching methodology results in cohorts of cases (i.e., patients with AVN and controls (i.e., patients without AVN who have comparable distributions for four common parameters used in subject selection: gender, year of birth (age, treatment status, and splenectomy status. Matching resulted in odds ratios of approximately 1.00, indicating no bias. Conclusions We demonstrated bias in case-control selection in subjects from a prototype rare disease registry and used case-control matching to minimize this bias. Therefore, this approach appears useful to study cohorts of heterogeneous patients in rare disease registries.

  10. Quantifying the impact of selection bias caused by nonparticipation in a case-control study of mobile phone use

    DEFF Research Database (Denmark)

    Vrijheid, Martine; Richardson, Lesley; Armstrong, Bruce K

    2009-01-01

    To quantitatively assess the impact of selection bias caused by nonparticipation in a multinational case-control study of mobile phone use and brain tumor.......To quantitatively assess the impact of selection bias caused by nonparticipation in a multinational case-control study of mobile phone use and brain tumor....

  11. Selection bias in family reports on end of life with dementia in nursing homes.

    Science.gov (United States)

    van der Steen, Jenny T; Deliens, Luc; Ribbe, Miel W; Onwuteaka-Philipsen, Bregje D

    2012-12-01

    Selective participation in retrospective studies of families recruited after the patient's death may threaten generalizability of reports on end-of-life experiences. To assess possible selection bias in retrospective study of dementia at the end of life using family reports. Two physician teams covering six nursing home facilities in the Netherlands reported on 117 of 119 consecutive decedents within two weeks after death unaware of after-death family participation in the study. They reported on characteristics; treatment and care; overall patient outcomes such as comfort, nursing care, and outcomes; and their own perspectives on the experience. We compared results between decedents with and without family participation. The family response rate was 55%. There were no significant differences based on participation versus nonparticipation in demographics and other nursing home resident characteristics, treatment and care, or overall resident outcome. However, among participating families, physicians perceived higher-quality aspects of nursing care and outcome, better consensus between staff and family on treatment, and a more peaceful death. Participation was less likely with involvement of a new family member in the last month. Families may be more likely to participate in research with more harmonious teamwork in end-of-life caregiving. Where family participation is an enrollment criterion, comparing demographics alone may not capture possible selection bias, especially in more subjective measures. Selection bias toward more positive experiences, which may include the physician's and probably also the family's experiences, should be considered if representativeness is aimed for. Future work should address selection bias in other palliative settings and countries, and with prospective recruitment.

  12. Modelling large-scale halo bias using the bispectrum

    CERN Document Server

    Pollack, Jennifer E; Porciani, Cristiano

    2011-01-01

    We study the relation between the halo and matter density fields -- commonly termed bias -- in the LCDM framework. In particular, we examine the local model of biasing at quadratic order in matter density. This model is characterized by parameters b_1 and b_2. Using an ensemble of N-body simulations, we apply several statistical methods to estimate the parameters. We measure halo and matter fluctuations smoothed on various scales and find that the parameters vary with smoothing scale. We argue that, for real-space measurements, owing to the mixing of wavemodes, no scale can be found for which the parameters are independent of smoothing. However, this is not the case in Fourier space. We measure halo power spectra and construct estimates for an effective large-scale bias. We measure the configuration dependence of the halo bispectra B_hhh and reduced bispectra Q_hhh for very large-scale k-space triangles. From this we constrain b_1 and b_2. Using the lowest-order perturbation theory, we find that for B_hhh the...

  13. Evaluation of hydrologic components of community land model 4 and bias identification

    Science.gov (United States)

    Du, Enhao; Vittorio, Alan Di; Collins, William D.

    2016-06-01

    Runoff and soil moisture are two key components of the global hydrologic cycle that should be validated at local to global scales in Earth System Models (ESMs) used for climate projection. We have evaluated the runoff and surface soil moisture output by the Community Climate System Model (CCSM) along with 8 other models from the Coupled Model Intercomparison Project (CMIP5) repository using satellite soil moisture observations and stream gauge corrected runoff products. A series of Community Land Model (CLM) runs forced by reanalysis and coupled model outputs was also performed to identify atmospheric drivers of biases and uncertainties in the CCSM. Results indicate that surface soil moisture simulations tend to be positively biased in high latitude areas by most selected CMIP5 models except CCSM, FGOALS, and BCC, which share similar land surface model code. With the exception of GISS, runoff simulations by all selected CMIP5 models were overestimated in mountain ranges and in most of the Arctic region. In general, positive biases in CCSM soil moisture and runoff due to precipitation input error were offset by negative biases induced by temperature input error. Excluding the impact from atmosphere modeling, the global mean of seasonal surface moisture oscillation was out of phase compared to observations in many years during 1985-2004. The CLM also underestimated runoff in the Amazon, central Africa, and south Asia, where soils all have high clay content. We hypothesize that lack of a macropore flow mechanism is partially responsible for this underestimation. However, runoff was overestimated in the areas covered by volcanic ash soils (i.e., Andisols), which might be associated with poor soil porosity representation in CLM. Our results indicate that CCSM predictability of hydrology could be improved by addressing the compensating errors associated with precipitation and temperature and updating the CLM soil representation.

  14. Drought Duration Biases in Current Global Climate Models

    Science.gov (United States)

    Moon, Heewon; Gudmundsson, Lukas; Seneviratne, Sonia

    2016-04-01

    Several droughts in the recent past are characterized by their increased duration and intensity. In particular, substantially prolonged droughts have brought major societal and economic losses in certain regions, yet climate change projections of such droughts in terms of duration is subject to large uncertainties. This study analyzes the biases of drought duration in state-of-the-art global climate model (GCM) simulations from the 5th phase of Coupled Model Intercomparison Project (CMIP5). Drought durations are defined as negative precipitation anomalies and evaluated with three observation-based datasets in the period of 1901-2010. Large spread in biases of GCMs is commonly found in all regions, with particular strong biases in North East Brazil, Africa, Northern Australia, Central America, Central and Northern Europe, Sahel and Asia. Also in most regions, the interquartile range of bias lies below 0, meaning that the GCMs tend to underestimate drought durations. Meanwhile in some regions such as Western South America, the Amazon, Sahel, West and South Africa, and Asia, considerable inconsistency among the three observation-based datasets were found. These results indicate substantial uncertainties and errors in current GCMs for simulating drought durations as well as a large spread in observation-based datasets, both of which are found to be particularly strong in those regions that are often considered to be hot spots of projected future drying. The underlying sources of these uncertainties need to be identified in further study and will be applied to constrain GCM-based drought projections under climate change.

  15. Validation of models with constant bias: an applied approach

    Directory of Open Access Journals (Sweden)

    Salvador Medina-Peralta

    2014-06-01

    Full Text Available Objective. This paper presents extensions to the statistical validation method based on the procedure of Freese when a model shows constant bias (CB in its predictions and illustrate the method with data from a new mechanistic model that predict weight gain in cattle. Materials and methods. The extensions were the hypothesis tests and maximum anticipated error for the alternative approach, and the confidence interval for a quantile of the distribution of errors. Results. The model evaluated showed CB, once the CB is removed and with a confidence level of 95%, the magnitude of the error does not exceed 0.575 kg. Therefore, the validated model can be used to predict the daily weight gain of cattle, although it will require an adjustment in its structure based on the presence of CB to increase the accuracy of its forecasts. Conclusions. The confidence interval for the 1-α quantile of the distribution of errors after correcting the constant bias, allows determining the top limit for the magnitude of the error of prediction and use it to evaluate the evolution of the model in the forecasting of the system. The confidence interval approach to validate a model is more informative than the hypothesis tests for the same purpose.

  16. Selective drop-out in longitudinal studies and non-biased prediction of behaviour disorders

    OpenAIRE

    Wolke, Dieter; Waylen, Andrea; Samara, Muthanna; Steer, Colin; Goodman,Robert; Ford, Tamsin; Lamberts, Koen

    2009-01-01

    Background Participant drop-out occurs in all longitudinal studies, and if systematic, may lead to selection biases and erroneous conclusions being drawn from a study. Aims We investigated whether drop out in the Avon Longitudinal Study of Parents And Children (ALSPAC) was systematic or random, and if systematic, whether it had an impact on the prediction of disruptive behaviour disorders. Method Teacher reports of disruptive behaviour among currently participating, previously participating a...

  17. Student Sorting and Bias in Value-Added Estimation: Selection on Observables and Unobservables

    Science.gov (United States)

    Rothstein, Jesse

    2009-01-01

    Nonrandom assignment of students to teachers can bias value-added estimates of teachers' causal effects. Rothstein (2008, 2010) shows that typical value-added models indicate large counterfactual effects of fifth-grade teachers on students' fourth-grade learning, indicating that classroom assignments are far from random. This article quantifies…

  18. Twins like to be seen: Observational biases affecting spectroscopically selected binary stars

    CERN Document Server

    Cantrell, Andrew G

    2014-01-01

    Massive binary stars undergo qualitatively different evolution when the two components are similar in mass ('twins'), and the abundance of twin binaries is therefore important to understanding a wide range of astrophysical phenomena. We reconsider the results of Pinsonneault & Stanek (2006), who argue that a large proportion of binary stars have nearly equal-mass components; we find that their data imply a relatively small number of such 'twins.' We argue that samples of double-lined spectroscopic binaries are biased towards systems with nearly equal-brightness components. We present a Monte-Carlo model of this bias, which simultaneously explains the abundance of twins in the unevolved binaries of Pinsonneault & Stanek (2006), and the lack of twins in their evolved systems. After accounting for the bias, we find that their observed mass ratios may be consistent with a variety of intrinsic distributions, including either a flat distribution or a Salpeter distribution. We conclude that the observed over...

  19. The Properties of Model Selection when Retaining Theory Variables

    DEFF Research Database (Denmark)

    Hendry, David F.; Johansen, Søren

    Economic theories are often fitted directly to data to avoid possible model selection biases. We show that embedding a theory model that specifies the correct set of m relevant exogenous variables, x{t}, within the larger set of m+k candidate variables, (x{t},w{t}), then selection over the second...

  20. Evaluation of bias associated with capture maps derived from nonlinear groundwater flow models

    Science.gov (United States)

    Nadler, Cara; Allander, Kip K.; Pohll, Greg; Morway, Eric; Naranjo, Ramon C.; Huntington, Justin

    2017-01-01

    The impact of groundwater withdrawal on surface water is a concern of water users and water managers, particularly in the arid western United States. Capture maps are useful tools to spatially assess the impact of groundwater pumping on water sources (e.g., streamflow depletion) and are being used more frequently for conjunctive management of surface water and groundwater. Capture maps have been derived using linear groundwater flow models and rely on the principle of superposition to demonstrate the effects of pumping in various locations on resources of interest. However, nonlinear models are often necessary to simulate head-dependent boundary conditions and unconfined aquifers. Capture maps developed using nonlinear models with the principle of superposition may over- or underestimate capture magnitude and spatial extent. This paper presents new methods for generating capture difference maps, which assess spatial effects of model nonlinearity on capture fraction sensitivity to pumping rate, and for calculating the bias associated with capture maps. The sensitivity of capture map bias to selected parameters related to model design and conceptualization for the arid western United States is explored. This study finds that the simulation of stream continuity, pumping rates, stream incision, well proximity to capture sources, aquifer hydraulic conductivity, and groundwater evapotranspiration extinction depth substantially affect capture map bias. Capture difference maps demonstrate that regions with large capture fraction differences are indicative of greater potential capture map bias. Understanding both spatial and temporal bias in capture maps derived from nonlinear groundwater flow models improves their utility and defensibility as conjunctive-use management tools.

  1. Tilted dipole model for bias-dependent photoluminescence pattern

    Energy Technology Data Exchange (ETDEWEB)

    Fujieda, Ichiro, E-mail: fujieda@se.ritsumei.ac.jp; Suzuki, Daisuke; Masuda, Taishi [Department of Electrical and Electronic Engineering, Ritsumeikan University, Kusatsu 525-8577 (Japan)

    2014-12-14

    In a guest-host system containing elongated dyes and a nematic liquid crystal, both molecules are aligned to each other. An external bias tilts these molecules and the radiation pattern of the system is altered. A model is proposed to describe this bias-dependent photoluminescence patterns. It divides the liquid crystal/dye layer into sub-layers that contain electric dipoles with specific tilt angles. Each sub-layer emits linearly polarized light. Its radiation pattern is toroidal and is determined by the tilt angle. Its intensity is assumed to be proportional to the power of excitation light absorbed by the sub-layer. This is calculated by the Lambert-Beer's Law. The absorption coefficient is assumed to be proportional to the cross-section of the tilted dipole moment, in analogy to the ellipsoid of refractive index, to evaluate the cross-section for each polarized component of the excitation light. Contributions from all the sub-layers are added to give a final expression for the radiation pattern. Self-absorption is neglected. The model is simplified by reducing the number of sub-layers. Analytical expressions are derived for a simple case that consists of a single layer with tilted dipoles sandwiched by two layers with horizontally-aligned dipoles. All the parameters except for the tilt angle can be determined by measuring transmittance of the excitation light. The model roughly reproduces the bias-dependent photoluminescence patterns of a cell containing 0.5 wt. % coumarin 6. It breaks down at large emission angles. Measured spectral changes suggest that the discrepancy is due to self-absorption and re-emission.

  2. Climate model forecast biases assessed with a perturbed physics ensemble

    Science.gov (United States)

    Mulholland, David P.; Haines, Keith; Sparrow, Sarah N.; Wallom, David

    2017-09-01

    Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the `equivalent parameter perturbations', which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies.

  3. Climate model forecast biases assessed with a perturbed physics ensemble

    Science.gov (United States)

    Mulholland, David P.; Haines, Keith; Sparrow, Sarah N.; Wallom, David

    2016-10-01

    Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the `equivalent parameter perturbations', which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies.

  4. Mutation and selection cause codon usage and bias in mitochondrial genomes of ribbon worms (Nemertea).

    Science.gov (United States)

    Chen, Haixia; Sun, Shichun; Norenburg, Jon L; Sundberg, Per

    2014-01-01

    The phenomenon of codon usage bias is known to exist in many genomes and it is mainly determined by mutation and selection. To understand the patterns of codon usage in nemertean mitochondrial genomes, we use bioinformatic approaches to analyze the protein-coding sequences of eight nemertean species. Neutrality analysis did not find a significant correlation between GC12 and GC3. ENc-plot showed a few genes on or close to the expected curve, but the majority of points with low-ENc values are below it. ENc-plot suggested that mutational bias plays a major role in shaping codon usage. The Parity Rule 2 plot (PR2) analysis showed that GC and AT were not used proportionally and we propose that codons containing A or U at third position are used preferentially in nemertean species, regardless of whether corresponding tRNAs are encoded in the mitochondrial DNA. Context-dependent analysis indicated that the nucleotide at the second codon position slightly affects synonymous codon choices. These results suggested that mutational and selection forces are probably acting to codon usage bias in nemertean mitochondrial genomes.

  5. Mutation and selection cause codon usage and bias in mitochondrial genomes of ribbon worms (Nemertea.

    Directory of Open Access Journals (Sweden)

    Haixia Chen

    Full Text Available The phenomenon of codon usage bias is known to exist in many genomes and it is mainly determined by mutation and selection. To understand the patterns of codon usage in nemertean mitochondrial genomes, we use bioinformatic approaches to analyze the protein-coding sequences of eight nemertean species. Neutrality analysis did not find a significant correlation between GC12 and GC3. ENc-plot showed a few genes on or close to the expected curve, but the majority of points with low-ENc values are below it. ENc-plot suggested that mutational bias plays a major role in shaping codon usage. The Parity Rule 2 plot (PR2 analysis showed that GC and AT were not used proportionally and we propose that codons containing A or U at third position are used preferentially in nemertean species, regardless of whether corresponding tRNAs are encoded in the mitochondrial DNA. Context-dependent analysis indicated that the nucleotide at the second codon position slightly affects synonymous codon choices. These results suggested that mutational and selection forces are probably acting to codon usage bias in nemertean mitochondrial genomes.

  6. Discharge simulations performed with a hydrological model using bias corrected regional climate model input

    Directory of Open Access Journals (Sweden)

    S. C. van Pelt

    2009-12-01

    Full Text Available Studies have demonstrated that precipitation on Northern Hemisphere mid-latitudes has increased in the last decades and that it is likely that this trend will continue. This will have an influence on discharge of the river Meuse. The use of bias correction methods is important when the effect of precipitation change on river discharge is studied. The objective of this paper is to investigate the effect of using two different bias correction methods on output from a Regional Climate Model (RCM simulation. In this study a Regional Atmospheric Climate Model (RACMO2 run is used, forced by ECHAM5/MPIOM under the condition of the SRES-A1B emission scenario, with a 25 km horizontal resolution. The RACMO2 runs contain a systematic precipitation bias on which two bias correction methods are applied. The first method corrects for the wet day fraction and wet day average (WD bias correction and the second method corrects for the mean and coefficient of variance (MV bias correction. The WD bias correction initially corrects well for the average, but it appears that too many successive precipitation days were removed with this correction. The second method performed less well on average bias correction, but the temporal precipitation pattern was better. Subsequently, the discharge was calculated by using RACMO2 output as forcing to the HBV-96 hydrological model. A large difference was found between the simulated discharge of the uncorrected RACMO2 run, the WD bias corrected run and the MV bias corrected run. These results show the importance of an appropriate bias correction.

  7. Impact of marker ascertainment bias on genomic selection accuracy and estimates of genetic diversity.

    Directory of Open Access Journals (Sweden)

    Nicolas Heslot

    Full Text Available Genome-wide molecular markers are often being used to evaluate genetic diversity in germplasm collections and for making genomic selections in breeding programs. To accurately predict phenotypes and assay genetic diversity, molecular markers should assay a representative sample of the polymorphisms in the population under study. Ascertainment bias arises when marker data is not obtained from a random sample of the polymorphisms in the population of interest. Genotyping-by-sequencing (GBS is rapidly emerging as a low-cost genotyping platform, even for the large, complex, and polyploid wheat (Triticum aestivum L. genome. With GBS, marker discovery and genotyping occur simultaneously, resulting in minimal ascertainment bias. The previous platform of choice for whole-genome genotyping in many species such as wheat was DArT (Diversity Array Technology and has formed the basis of most of our knowledge about cereals genetic diversity. This study compared GBS and DArT marker platforms for measuring genetic diversity and genomic selection (GS accuracy in elite U.S. soft winter wheat. From a set of 365 breeding lines, 38,412 single nucleotide polymorphism GBS markers were discovered and genotyped. The GBS SNPs gave a higher GS accuracy than 1,544 DArT markers on the same lines, despite 43.9% missing data. Using a bootstrap approach, we observed significantly more clustering of markers and ascertainment bias with DArT relative to GBS. The minor allele frequency distribution of GBS markers had a deficit of rare variants compared to DArT markers. Despite the ascertainment bias of the DArT markers, GS accuracy for three traits out of four was not significantly different when an equal number of markers were used for each platform. This suggests that the gain in accuracy observed using GBS compared to DArT markers was mainly due to a large increase in the number of markers available for the analysis.

  8. Evaluation of bias correction methods for wave modeling output

    Science.gov (United States)

    Parker, K.; Hill, D. F.

    2017-02-01

    Models that seek to predict environmental variables invariably demonstrate bias when compared to observations. Bias correction (BC) techniques are common in the climate and hydrological modeling communities, but have seen fewer applications to the field of wave modeling. In particular there has been no investigation as to which BC methodology performs best for wave modeling. This paper introduces and compares a subset of BC methods with the goal of clarifying a "best practice" methodology for application of BC in studies of wave-related processes. Specific focus is paid to comparing parametric vs. empirical methods as well as univariate vs. bivariate methods. The techniques are tested on global WAVEWATCH III historic and future period datasets with comparison to buoy observations at multiple locations. Both wave height and period are considered in order to investigate BC effects on inter-variable correlation. Results show that all methods perform uniformly in terms of correcting statistical moments for individual variables with the exception of a copula based method underperforming for wave period. When comparing parametric and empirical methods, no difference is found. Between bivariate and univariate methods, results show that bivariate methods greatly improve inter-variable correlations. Of the bivariate methods tested the copula based method is found to be not as effective at correcting correlation while a "shuffling" method is unable to handle changes in correlation from historic to future periods. In summary, this study demonstrates that BC methods are effective when applied to wave model data and that it is essential to employ methods that consider dependence between variables.

  9. Indian Ocean SST Biases in a Flexible Regional Ocean Atmosphere Land System (FROALS) Model

    Institute of Scientific and Technical Information of China (English)

    HAN Zhen-Yu; ZHOU Tian-Jun; ZOU Li-Wei

    2012-01-01

    The authors examine the Indian Ocean sea surface temperature (SST) biases simulated by a Flexible Regional Ocean Atmosphere Land System (FROALS) model. The regional coupled model exhibits pronounced cold SST biases in a large portion of the Indian Ocean warm pool. Negative biases in the net surface heat fluxes are evident in the model, leading to the cold biases of the SST. Further analysis indicates that the negative biases in the net surface heat fluxes are mainly contributed by the biases of sensible heat and latent heat flux. Near-surface meteorological variables that could contribute to the SST biases are also examined. It is found that the biases of sensible heat and latent heat flux are caused by the colder and dryer near-surface air in the model.

  10. Hydrological modeling as an evaluation tool of EURO-CORDEX climate projections and bias correction methods

    Science.gov (United States)

    Hakala, Kirsti; Addor, Nans; Seibert, Jan

    2017-04-01

    Streamflow stemming from Switzerland's mountainous landscape will be influenced by climate change, which will pose significant challenges to the water management and policy sector. In climate change impact research, the determination of future streamflow is impeded by different sources of uncertainty, which propagate through the model chain. In this research, we explicitly considered the following sources of uncertainty: (1) climate models, (2) downscaling of the climate projections to the catchment scale, (3) bias correction method and (4) parameterization of the hydrological model. We utilize climate projections at the 0.11 degree 12.5 km resolution from the EURO-CORDEX project, which are the most recent climate projections for the European domain. EURO-CORDEX is comprised of regional climate model (RCM) simulations, which have been downscaled from global climate models (GCMs) from the CMIP5 archive, using both dynamical and statistical techniques. Uncertainties are explored by applying a modeling chain involving 14 GCM-RCMs to ten Swiss catchments. We utilize the rainfall-runoff model HBV Light, which has been widely used in operational hydrological forecasting. The Lindström measure, a combination of model efficiency and volume error, was used as an objective function to calibrate HBV Light. Ten best sets of parameters are then achieved by calibrating using the genetic algorithm and Powell optimization (GAP) method. The GAP optimization method is based on the evolution of parameter sets, which works by selecting and recombining high performing parameter sets with each other. Once HBV is calibrated, we then perform a quantitative comparison of the influence of biases inherited from climate model simulations to the biases stemming from the hydrological model. The evaluation is conducted over two time periods: i) 1980-2009 to characterize the simulation realism under the current climate and ii) 2070-2099 to identify the magnitude of the projected change of

  11. The mouse X chromosome is enriched for sex-biased genes not subject to selection by meiotic sex chromosome inactivation.

    Science.gov (United States)

    Khil, Pavel P; Smirnova, Natalya A; Romanienko, Peter J; Camerini-Otero, R Daniel

    2004-06-01

    Sex chromosomes are subject to sex-specific selective evolutionary forces. One model predicts that genes with sex-biased expression should be enriched on the X chromosome. In agreement with Rice's hypothesis, spermatogonial genes are over-represented on the X chromosome of mice and sex- and reproduction-related genes are over-represented on the human X chromosome. Male-biased genes are under-represented on the X chromosome in worms and flies, however. Here we show that mouse spermatogenesis genes are relatively under-represented on the X chromosome and female-biased genes are enriched on it. We used Spo11(-/-) mice blocked in spermatogenesis early in meiosis to evaluate the temporal pattern of gene expression in sperm development. Genes expressed before the Spo11 block are enriched on the X chromosome, whereas those expressed later in spermatogenesis are depleted. Inactivation of the X chromosome in male meiosis may be a universal driving force for X-chromosome demasculinization.

  12. Complexity regularized hydrological model selection

    NARCIS (Netherlands)

    Pande, S.; Arkesteijn, L.; Bastidas, L.A.

    2014-01-01

    This paper uses a recently proposed measure of hydrological model complexity in a model selection exercise. It demonstrates that a robust hydrological model is selected by penalizing model complexity while maximizing a model performance measure. This especially holds when limited data is available.

  13. Complexity regularized hydrological model selection

    NARCIS (Netherlands)

    Pande, S.; Arkesteijn, L.; Bastidas, L.A.

    2014-01-01

    This paper uses a recently proposed measure of hydrological model complexity in a model selection exercise. It demonstrates that a robust hydrological model is selected by penalizing model complexity while maximizing a model performance measure. This especially holds when limited data is available.

  14. Individual Influence on Model Selection

    Science.gov (United States)

    Sterba, Sonya K.; Pek, Jolynn

    2012-01-01

    Researchers in psychology are increasingly using model selection strategies to decide among competing models, rather than evaluating the fit of a given model in isolation. However, such interest in model selection outpaces an awareness that one or a few cases can have disproportionate impact on the model ranking. Though case influence on the fit…

  15. Orientation Bias of Optically Selected Galaxy Clusters and Its Impact on Stacked Weak Lensing Analyses

    CERN Document Server

    Dietrich, Jörg P; Song, Jeeseon; McKay, Christopher P Davis Timothy A; Baruah, Leon; Becker, Matthew; Benoist, Christophe; Busha, Michael; da Costa, Luiz A N; Hao, Jiangang; Maia, Marcio A G; Miller, Christopher J; Ogando, Ricardo; Romer, A Kathy; Rozo, Eduardo; Rykoff, Eli; Wechsler, Risa

    2014-01-01

    Weak-lensing measurements of the averaged shear profiles of galaxy clusters binned by some proxy for cluster mass are commonly converted to cluster mass estimates under the assumption that these cluster stacks have spherical symmetry. In this paper we test whether this assumption holds for optically selected clusters binned by estimated optical richness. Using mock catalogues created from N-body simulations populated realistically with galaxies, we ran a suite of optical cluster finders and estimated their optical richness. We binned galaxy clusters by true cluster mass and estimated optical richness and measure the ellipticity of these stacks. We find that the processes of optical cluster selection and richness estimation are biased, leading to stacked structures that are elongated along the line-of-sight. We show that weak-lensing alone cannot measure the size of this orientation bias. Weak lensing masses of stacked optically selected clusters are overestimated by up to 3-6 per cent when clusters can be uni...

  16. Real-time Analysis and Selection Biases in the Supernova Legacy Survey

    CERN Document Server

    Perrett, K; Sullivan, M; Pritchet, C; Conley, A; Carlberg, R; Astier, P; Balland, C; Basa, S; Fouchez, D; Guy, J; Hardin, D; Hook, I M; Howell, D A; Pain, R; Regnault, N

    2010-01-01

    The Supernova Legacy Survey (SNLS) has produced a high-quality, homogeneous sample of Type Ia supernovae (SNe Ia) out to redshifts greater than z=1. In its first four years of full operation (to June 2007), the SNLS discovered more than 3000 transient candidates, 373 of which have been confirmed spectroscopically as SNe Ia. Use of these SNe Ia in precision cosmology critically depends on an analysis of the observational biases incurred in the SNLS survey due to the incomplete sampling of the underlying SN Ia population. This paper describes our real-time supernova detection and analysis procedures, and uses detailed Monte Carlo simulations to examine the effects of Malmquist bias and spectroscopic sampling. Such sampling effects are found to become apparent at z~0.6, with a significant shift in the average magnitude of the spectroscopically confirmed SN Ia sample towards brighter values for z>0.75. We describe our approach to correct for these selection biases in our three-year SNLS cosmological analysis (SNL...

  17. Selective drop-out in longitudinal studies and non-biased prediction of behaviour disorders

    OpenAIRE

    Wolke, Dieter; Waylen , Andrea E.; Samara, Muthanna; Steer, Colin; Goodman,Robert; Ford, Tamsin; Lamberts, Koen

    2009-01-01

    Background\\ud \\ud Participant drop-out occurs in all longitudinal studies, and if systematic, may lead to selection biases and erroneous conclusions being drawn from a study.\\ud \\ud Aims\\ud \\ud We investigated whether drop out in the Avon Longitudinal Study of Parents And Children (ALSPAC) was systematic or random, and if systematic, whether it had an impact on the prediction of disruptive behaviour disorders.\\ud \\ud Method\\ud \\ud Teacher reports of disruptive behaviour among currently partic...

  18. The effect of GCM biases on global runoff simulations of a land surface model

    Science.gov (United States)

    Papadimitriou, Lamprini V.; Koutroulis, Aristeidis G.; Grillakis, Manolis G.; Tsanis, Ioannis K.

    2017-09-01

    Global climate model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue, many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However, most state-of-the-art hydrological models require more forcing variables, in addition to precipitation and temperature, such as radiation, humidity, air pressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface model set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global-scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial-scale information is also provided

  19. Objects in Kepler's Mirror May be Larger Than They Appear: Bias and Selection Effects in Transiting Planet Surveys

    CERN Document Server

    Gaidos, Eric

    2012-01-01

    Statistical analyses of large surveys for transiting planets such as the Kepler mission must account for systematic errors and biases. Transit detection depends not only on the planet's radius and orbital period, but also on host star properties. Thus, a sample of stars with transiting planets may not accurately represent the target population. Moreover, targets are selected using criteria such as a limiting apparent magnitude. These selection effects, combined with uncertainties in stellar radius, lead to biases in the properties of transiting planets and their host stars. We quantify possible biases in the Kepler survey. First, Eddington bias produced by a steep planet radius distribution and uncertainties in stellar radius results in a 15-20% overestimate of planet occurrence. Second, the magnitude limit of the Kepler target catalog induces Malmquist bias towards large, more luminous stars and underestimation of the radii of about one third of candidate planets, especially those larger than Neptune. Third,...

  20. Doped carbon nanotubes as a model system of biased graphene

    Science.gov (United States)

    Szirmai, P.; Márkus, B. G.; Dóra, B.; Fábián, G.; Koltai, J.; Zólyomi, V.; Kürti, J.; Náfrádi, B.; Forró, L.; Pichler, T.; Simon, F.

    2017-08-01

    Albeit difficult to access experimentally, the density of states (DOS) is a key parameter in solid-state systems, which governs several important phenomena including transport, magnetism, thermal, and thermoelectric properties. We study DOS in an ensemble of potassium intercalated single-wall carbon nanotubes and show, using electron spin resonance spectroscopy, that a sizable number of electron states are present, which gives rise to a Fermi-liquid behavior in this material. A comparison between theoretical and the experimental DOS indicates that it does not display significant correlation effects, even though the pristine nanotube material shows a Luttinger-liquid behavior. We argue that the carbon nanotube ensemble essentially maps out the whole Brillouin zone of graphene, thus it acts as a model system of biased graphene.

  1. Biases in human sequential predictions as a consequence of incorrect world models, noise and limited memory

    NARCIS (Netherlands)

    Narain, D.; Beers, R.J. van; Smeets, J.B.J.

    2016-01-01

    Recent studies demonstrate that biases found in human behavior can be explained by rational agents that make incorrect generative-model assumptions. While predicting a sequence of uncorrelated events, humans are biased towards overestimating its serial correlation. We demonstrate how such biases may

  2. Microcredit and domestic violence in Bangladesh: an exploration of selection bias influences.

    Science.gov (United States)

    Bajracharya, Ashish; Amin, Sajeda

    2013-10-01

    This article explores the relationship between women's participation in microcredit groups and domestic violence in Bangladesh. Several recent studies have raised concern about microcredit programs by reporting higher levels of violence among women who are members. These results, however, may be attributable to selection bias because members might differ from nonmembers in ways that make them more susceptible to violence to begin with. Using a sample of currently married women from the 2007 Bangladesh Demographic Health Survey (BDHS) (N = 4,195), we use propensity score matching (PSM) as a way of exploring selection bias in this relationship. Results suggest that the previously seen strong positive association between membership and violence does not hold when an appropriate comparison group, generated using PSM, is used in the analyses. Additional analyses also suggest that levels of violence do not differ significantly between members and nonmembers and instead could depend on context-specific factors related to poverty. Members for whom a match is not found report considerably higher levels of violence relative to nonmembers in the unmatched group. The background characteristics of members and nonmembers who do not match suggest that they are more likely to be younger and from relatively well-to-do households.

  3. Model biases in high-burnup fast reactor simulations

    Energy Technology Data Exchange (ETDEWEB)

    Touran, N.; Cheatham, J.; Petroski, R. [TerraPower LLC, 11235 S.E. 6th St, Bellevue, WA 98004 (United States)

    2012-07-01

    A new code system called the Advanced Reactor Modeling Interface (ARMI) has been developed that loosely couples multiscale, multiphysics nuclear reactor simulations to provide rapid, user-friendly, high-fidelity full systems analysis. Incorporating neutronic, thermal-hydraulic, safety/transient, fuel performance, core mechanical, and economic analyses, ARMI provides 'one-click' assessments of many multi-disciplined performance metrics and constraints that historically require iterations between many diverse experts. The capabilities of ARMI are implemented in this study to quantify neutronic biases of various modeling approximations typically made in fast reactor analysis at an equilibrium condition, after many repetitive shuffles. Sensitivities at equilibrium that result in very high discharge burnup are considered ( and >20% FIMA), as motivated by the development of the Traveling Wave Reactor. Model approximations discussed include homogenization, neutronic and depletion mesh resolution, thermal-hydraulic coupling, explicit control rod insertion, burnup-dependent cross sections, fission product model, burn chain truncation, and dynamic fuel performance. The sensitivities of these approximations on equilibrium discharge burnup, k{sub eff}, power density, delayed neutron fraction, and coolant temperature coefficient are discussed. (authors)

  4. Data assimilation in integrated hydrological modelling in the presence of observation bias

    Science.gov (United States)

    Rasmussen, Jørn; Madsen, Henrik; Høgh Jensen, Karsten; Refsgaard, Jens Christian

    2016-05-01

    The use of bias-aware Kalman filters for estimating and correcting observation bias in groundwater head observations is evaluated using both synthetic and real observations. In the synthetic test, groundwater head observations with a constant bias and unbiased stream discharge observations are assimilated in a catchment-scale integrated hydrological model with the aim of updating stream discharge and groundwater head, as well as several model parameters relating to both streamflow and groundwater modelling. The coloured noise Kalman filter (ColKF) and the separate-bias Kalman filter (SepKF) are tested and evaluated for correcting the observation biases. The study found that both methods were able to estimate most of the biases and that using any of the two bias estimation methods resulted in significant improvements over using a bias-unaware Kalman filter. While the convergence of the ColKF was significantly faster than the convergence of the SepKF, a much larger ensemble size was required as the estimation of biases would otherwise fail. Real observations of groundwater head and stream discharge were also assimilated, resulting in improved streamflow modelling in terms of an increased Nash-Sutcliffe coefficient while no clear improvement in groundwater head modelling was observed. Both the ColKF and the SepKF tended to underestimate the biases, which resulted in drifting model behaviour and sub-optimal parameter estimation, but both methods provided better state updating and parameter estimation than using a bias-unaware filter.

  5. Modelling the post-reionization neutral Hydrogen (\\HI) bias

    CERN Document Server

    S., Anathpindika

    2016-01-01

    Observations of the neutral Hydrogen (\\HI ) 21-cm signal hold the potential of allowing us to map out the cosmological large scale structures (LSS) across the entire post-reionization era ($z \\leq 6$). Several experiments are planned to map the LSS over a large range of redshifts and angular scales, many of these targeting the Baryon Acoustic Oscillations. It is important to model the \\HI distribution in order to correctly predict the expected signal, and more so to correctly interpret the results after the signal is detected. In this paper we have carried out semi-numerical simulations to model the \\HI distribution and study the \\HI power spectrum $P_{\\HI}(k,z)$ across the redshift range $1 \\le z \\le 6$. We have modelled the \\HI bias as a complex quantity $\\tilde{b}(k,z)$ whose modulus squared $b^2(k,z)$ relates $P_{\\HI}(k,z)$ to the matter power spectrum $P(k,z)$, and whose real part $b_r(k,z)$ quantifies the cross-correlation between the \\HI and the matter distribution. We study the $z$ and $k$ dependence ...

  6. Bias-correction in vector autoregressive models: A simulation study

    DEFF Research Database (Denmark)

    Engsted, Tom; Pedersen, Thomas Quistgaard

    We analyze and compare the properties of various methods for bias-correcting parameter estimates in vector autoregressions. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that this simple and...

  7. Preoperative Breast MRI: Surgeons' Patient Selection Patterns and Potential Bias in Outcomes Analyses.

    Science.gov (United States)

    Lee, Jiyon; Tanaka, Elaine; Eby, Peter R; Zhou, Shouhao; Wei, Wei; Eppelheimer, Christine; Loving, Vilert A

    2017-04-01

    = 0.01) and tumors smaller than 1 cm (mean Likert score, 3.84 vs 3.40; p = 0.002). Breast surgeons referred less often than did general surgeons for multifocal or multicentric disease (mean Likert score, 5.02 vs 5.44; p = 0.001). Breast surgeons and general surgeons similarly weighed other variables. Preoperative breast MRI referral trended with certain higher risk patient- and tumor-related and clinical variables and were nonuniform between the breast surgeons and general surgeon cohorts. Selection bias could affect outcomes analyses for preoperative breast MRI.

  8. Complex Mutation and Weak Selection together Determined the Codon Usage Bias in Bryophyte Mitochondrial Genomes

    Institute of Scientific and Technical Information of China (English)

    Bin Wang; Jing Liu; Liang Jin; Xue-Ying Feng; Jian-Qun Chen

    2010-01-01

    Mutation and selection are two major forces causing codon usage biases. How these two forces influence the codon usages in green plant mitochondrial genomes has not been well investigated. In the present study, we surveyed five bryophyte mitochondrial genomes to reveal their codon usagepatterns as well as the determining forces. Three interesting findings were made. First, comparing to Chara vulgaris, an algal species sister to all extant land plants, bryophytes have more G, C-ending codon usages in their mitochondrial genes. This is consistent with the generally higher genomic GC content in bryophyte mitochondria, suggesting an increased mutational pressure toward GC. Second, as indicated by Wright's Nc-GC3s plot, mutation, not selection, is the major force affecting codon usages of bryophyte mitochondrial genes. However, the real mutational dynamics seem very complex. Context-dependent analysis indicated that nucleotide at the 2nd codon position would slightly affect synonymous codon choices. Finally, in bryophyte mitochondria, tRNA genes would apply a weak selection force to finetune the synonymous codon frequencies, as revealed by data of Ser4-Pro-Thr-Val families. In summary,complex mutation and weak selection together determined the codon usages in bryophyte mitochondrial genomes.

  9. Using decision models to decompose anxiety-related bias in threat classification.

    Science.gov (United States)

    White, Corey N; Skokin, Kimberly; Carlos, Brandon; Weaver, Alexandria

    2016-03-01

    Individuals with high levels of anxiety show preferential processing of threatening information, and this cognitive bias is thought to be an integral component of anxiety disorders. In threat classification tasks, this bias manifests as high-anxiety participants being more likely to classify stimuli as threatening than their low-anxiety counterparts. However, it is unclear which cognitive mechanisms drive this bias in threat classification. To better understand this phenomenon, threat classification data were analyzed with 2 decision models: a signal detection model and a drift-diffusion model. Signal detection models can dissociate measures of discriminability and bias, and diffusion models can further dissociate bias due to response preparation from bias due to stimulus evaluation. Individuals in the study completed a trait anxiety measure and classified threatening and neutral words based on whether they deemed them threatening. Signal detection analysis showed that high-anxiety participants had a bias driven by a weaker threat criterion than low-anxiety participants, but no differences in discriminability. Drift-diffusion analysis further decomposed the threat bias to show that it is driven by both an expectation bias that the threat response was more likely to be correct, and a stimulus bias driven by a weaker criterion for evaluating the stimuli under consideration. These model-based analyses provide valuable insight and show that multiple cognitive mechanisms underlie differential threat processing in anxiety. Implications for theories of anxiety are discussed.

  10. How evidence-based medicine is failing due to biased trials and selective publication.

    Science.gov (United States)

    Every-Palmer, Susanna; Howick, Jeremy

    2014-12-01

    Evidence-based medicine (EBM) was announced in the early 1990s as a 'new paradigm' for improving patient care. Yet there is currently little evidence that EBM has achieved its aim. Since its introduction, health care costs have increased while there remains a lack of high-quality evidence suggesting EBM has resulted in substantial population-level health gains. In this paper we suggest that EBM's potential for improving patients' health care has been thwarted by bias in the choice of hypotheses tested, manipulation of study design and selective publication. Evidence for these flaws is clearest in industry-funded studies. We argue EBM's indiscriminate acceptance of industry-generated 'evidence' is akin to letting politicians count their own votes. Given that most intervention studies are industry funded, this is a serious problem for the overall evidence base. Clinical decisions based on such evidence are likely to be misinformed, with patients given less effective, harmful or more expensive treatments. More investment in independent research is urgently required. Independent bodies, informed democratically, need to set research priorities. We also propose that evidence rating schemes are formally modified so research with conflict of interest bias is explicitly downgraded in value. © 2014 John Wiley & Sons, Ltd.

  11. A model for attentional information routing through coherence predicts biased competition and multistable perception.

    Science.gov (United States)

    Harnack, Daniel; Ernst, Udo Alexander; Pawelzik, Klaus Richard

    2015-09-01

    Selective attention allows to focus on relevant information and to ignore distracting features of a visual scene. These principles of information processing are reflected in response properties of neurons in visual area V4: if a neuron is presented with two stimuli in its receptive field, and one is attended, it responds as if the nonattended stimulus was absent (biased competition). In addition, when the luminance of the two stimuli is temporally and independently varied, local field potentials are correlated with the modulation of the attended stimulus and not, or much less, correlated with the nonattended stimulus (information routing). To explain these results in one coherent framework, we present a two-layer spiking cortical network model with distance-dependent lateral connectivity and converging feed-forward connections. With oscillations arising inherently from the network structure, our model reproduces both experimental observations. Hereby, lateral interactions and shifts of relative phases between sending and receiving layers (communication through coherence) are identified as the main mechanisms underlying both biased competition as well as selective routing. Exploring the parameter space, we show that the effects are robust and prevalent over a broad range of parameters. In addition, we identify the strength of lateral inhibition in the first model layer as crucial for determining the working regime of the system: increasing lateral inhibition allows a transition from a network configuration with mixed representations to one with bistable representations of the competing stimuli. The latter is discussed as a possible neural correlate of multistable perception phenomena such as binocular rivalry.

  12. Bias and Causation Models and Judgment for Valid Comparisons

    CERN Document Server

    Weisberg, Herbert I

    2010-01-01

    A one-of-a-kind resource on identifying and dealing with bias in statistical research on causal effects. Do cell phones cause cancer? Can a new curriculum increase student achievement? Determining what the real causes of such problems are, and how powerful their effects may be, are central issues in research across various fields of study. Some researchers are highly skeptical of drawing causal conclusions except in tightly controlled randomized experiments, while others discount the threats posed by different sources of bias, even in less rigorous observational studies. Bias and Causation pre

  13. Educational Research with Real-World Data: Reducing Selection Bias with Propensity Scores

    Directory of Open Access Journals (Sweden)

    Jill L. Adelson

    2013-12-01

    Full Text Available Often it is infeasible or unethical to use random assignment in educational settings to study important constructs and questions. Hence, educational research often uses observational data, such as large-scale secondary data sets and state and school district data, and quasi-experimental designs. One method of reducing selection bias in estimations of treatment effects is propensity score analysis. This method reduces a large number of pretreatment covariates to a single scalar function and allows researchers to compare subjects with similar probability to receive the treatment. This article provides an introduction to propensity score analysis and stratification, an example illustrating its use, and suggestions for using propensity score analysis in educational research.

  14. Selection bias in follow-up interviews with individuals attending the emergency department for occupational injuries

    DEFF Research Database (Denmark)

    Oesterlund, Anna H; Lander, Flemming; Rytter, Søren;

    2017-01-01

    OBJECTIVE: To examine whether supplementary interview participation was comparable for occupationally injured patients attending two hospital emergency departments and to investigate the magnitude of selection bias in relation to sex, age, severity, job tasks and industry risk level. METHODS...... were compared for study recruitment by age and sex. Respondents and non-respondents to the interview were compared for age, sex, injury severity, job tasks and industry risk level. RESULTS: Of 4002 patients attending the two hospitals, 1693 (42%) participated in the interview. One hospital had...... a markedly higher response rate to the questionnaire, but the proportions of participation in the interview were similar in the two hospitals. Patients aged job task and industry risk level were not significantly different...

  15. Selection Bias in Students' Evaluation of Teaching: Causes of Student Absenteeism and Its Consequences for Course Ratings and Rankings

    Science.gov (United States)

    Wolbring, Tobias; Treischl, Edgar

    2016-01-01

    Systematic sampling error due to self-selection is a common topic in methodological research and a key challenge for every empirical study. Since selection bias is often not sufficiently considered as a potential flaw in research on and evaluations in higher education, the aim of this paper is to raise awareness for the topic using the case of…

  16. Limitation of Inverse Probability-of-Censoring Weights in Estimating Survival in the Presence of Strong Selection Bias

    OpenAIRE

    Howe, Chanelle J.; Cole, Stephen R.; Chmiel, Joan S.; Muñoz, Alvaro

    2011-01-01

    In time-to-event analyses, artificial censoring with correction for induced selection bias using inverse probability-of-censoring weights can be used to 1) examine the natural history of a disease after effective interventions are widely available, 2) correct bias due to noncompliance with fixed or dynamic treatment regimens, and 3) estimate survival in the presence of competing risks. Artificial censoring entails censoring participants when they meet a predefined study criterion, such as exp...

  17. Selected System Models

    Science.gov (United States)

    Schmidt-Eisenlohr, F.; Puñal, O.; Klagges, K.; Kirsche, M.

    Apart from the general issue of modeling the channel, the PHY and the MAC of wireless networks, there are specific modeling assumptions that are considered for different systems. In this chapter we consider three specific wireless standards and highlight modeling options for them. These are IEEE 802.11 (as example for wireless local area networks), IEEE 802.16 (as example for wireless metropolitan networks) and IEEE 802.15 (as example for body area networks). Each section on these three systems discusses also at the end a set of model implementations that are available today.

  18. The Success Of Stock Selection Strategies In Emerging Markets: Is It Risk Or Behavioral Bias?

    NARCIS (Netherlands)

    J. van der Hart; G.J. de Zwart (Gerben); D.J.C. van Dijk (Dick)

    2005-01-01

    textabstractWe examine competing explanations, based on risk and behavioral models, for the profitability of stock selection strategies in emerging markets. We document that both emerging market risk and global risk factors cannot account for the significant excess returns of selection strategies ba

  19. Selection biases in empirical p(z) methods for weak lensing

    CERN Document Server

    Gruen, Daniel

    2016-01-01

    To measure the mass of foreground objects with weak gravitational lensing, one needs to estimate the redshift distribution of lensed background sources. This is commonly done in an empirical fashion, i.e. with a reference sample of galaxies of known spectroscopic redshift, matched to the source population. In this work, we develop a simple decision tree framework that, under the ideal conditions of a large, purely magnitude-limited reference sample, allows an unbiased recovery of the source redshift probability density function p(z), as a function of magnitude and color. We use this framework to quantify biases in empirically estimated p(z) caused by selection effects present in realistic reference and weak lensing source catalogs, namely (1) complex selection of reference objects by the targeting strategy and success rate of existing spectroscopic surveys and (2) selection of background sources by the success of object detection and shape measurement at low signal-to-noise. For intermediate-to-high redshift ...

  20. Cognitive bias in the chick anxiety-depression model.

    Science.gov (United States)

    Salmeto, Amy L; Hymel, Kristen A; Carpenter, Erika C; Brilot, Ben O; Bateson, Melissa; Sufka, Kenneth J

    2011-02-10

    Cognitive bias is a phenomenon that presents in clinical populations where anxious individuals tend to adopt a more pessimistic-like interpretation of ambiguous aversive stimuli whereas depressed individuals tend to adopt a less optimistic-like interpretation of ambiguous appetitive stimuli. To further validate the chick anxiety-depression model as a neuropsychiatric simulation we sought to quantify this cognitive endophenotype. Chicks exposed to an isolation stressor of 5m to induce an anxiety-like or 60 m to induce a depressive-like state were then tested in a straight alley maze to a series of morphed ambiguous appetitive (chick silhouette) to aversive (owl silhouette) cues. In non-isolated controls, runway start and goal latencies generally increased as a function of greater amounts of aversive characteristics in the cues. In chicks in the anxiety-like state, runway latencies were increased to aversive ambiguous cues, reflecting more pessimistic-like behavior. In chicks in the depression-like state, runway latencies were increased to both aversive and appetitive ambiguous cues, reflecting more pessimistic-like and less optimistic-like behavior, respectively.

  1. Impacts of WRF Physics and Measurement Uncertainty on California Wintertime Model Wet Bias

    Energy Technology Data Exchange (ETDEWEB)

    Chin, H S; Caldwell, P M; Bader, D C

    2009-07-22

    The Weather and Research Forecast (WRF) model version 3.0.1 is used to explore California wintertime model wet bias. In this study, two wintertime storms are selected from each of four major types of large-scale conditions; Pineapple Express, El Nino, La Nina, and synoptic cyclones. We test the impacts of several model configurations on precipitation bias through comparison with three sets of gridded surface observations; one from the National Oceanographic and Atmospheric Administration, and two variations from the University of Washington (without and with long-term trend adjustment; UW1 and UW2, respectively). To simplify validation, California is divided into 4 regions (Coast, Central Valley, Mountains, and Southern California). Simulations are driven by North American Regional Reanalysis data to minimize large-scale forcing error. Control simulations are conducted with 12-km grid spacing (low resolution) but additional experiments are performed at 2-km (high) resolution to evaluate the robustness of microphysics and cumulus parameterizations to resolution changes. We find that the choice of validation dataset has a significant impact on the model wet bias, and the forecast skill of model precipitation depends strongly on geographic location and storm type. Simulations with right physics options agree better with UW1 observations. In 12-km resolution simulations, the Lin microphysics and the Kain-Fritsch cumulus scheme have better forecast skill in the coastal region while Goddard, Thompson, and Morrison microphysics, and the Grell-Devenyi cumulus scheme perform better in the rest of California. The effect of planetary boundary layer, soil-layer, and radiation physics on model precipitation is weaker than that of microphysics and cumulus processes for short- to medium-range low-resolution simulations. Comparison of 2-km and 12-km resolution runs suggests a need for improvement of cumulus schemes, and supports the use of microphysics schemes in coarser

  2. Potential sources of analytical bias and error in selected trace element data-quality analyses

    Science.gov (United States)

    Paul, Angela P.; Garbarino, John R.; Olsen, Lisa D.; Rosen, Michael R.; Mebane, Christopher A.; Struzeski, Tedmund M.

    2016-09-28

    Potential sources of analytical bias and error associated with laboratory analyses for selected trace elements where concentrations were greater in filtered samples than in paired unfiltered samples were evaluated by U.S. Geological Survey (USGS) Water Quality Specialists in collaboration with the USGS National Water Quality Laboratory (NWQL) and the Branch of Quality Systems (BQS).Causes for trace-element concentrations in filtered samples to exceed those in associated unfiltered samples have been attributed to variability in analytical measurements, analytical bias, sample contamination either in the field or laboratory, and (or) sample-matrix chemistry. These issues have not only been attributed to data generated by the USGS NWQL but have been observed in data generated by other laboratories. This study continues the evaluation of potential analytical bias and error resulting from matrix chemistry and instrument variability by evaluating the performance of seven selected trace elements in paired filtered and unfiltered surface-water and groundwater samples collected from 23 sampling sites of varying chemistries from six States, matrix spike recoveries, and standard reference materials.Filtered and unfiltered samples have been routinely analyzed on separate inductively coupled plasma-mass spectrometry instruments. Unfiltered samples are treated with hydrochloric acid (HCl) during an in-bottle digestion procedure; filtered samples are not routinely treated with HCl as part of the laboratory analytical procedure. To evaluate the influence of HCl on different sample matrices, an aliquot of the filtered samples was treated with HCl. The addition of HCl did little to differentiate the analytical results between filtered samples treated with HCl from those samples left untreated; however, there was a small, but noticeable, decrease in the number of instances where a particular trace-element concentration was greater in a filtered sample than in the associated

  3. Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping

    Directory of Open Access Journals (Sweden)

    B. Thrasher

    2012-09-01

    Full Text Available When applying a quantile mapping-based bias correction to daily temperature extremes simulated by a global climate model (GCM, the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values.

  4. Launch vehicle selection model

    Science.gov (United States)

    Montoya, Alex J.

    1990-01-01

    Over the next 50 years, humans will be heading for the Moon and Mars to build scientific bases to gain further knowledge about the universe and to develop rewarding space activities. These large scale projects will last many years and will require large amounts of mass to be delivered to Low Earth Orbit (LEO). It will take a great deal of planning to complete these missions in an efficient manner. The planning of a future Heavy Lift Launch Vehicle (HLLV) will significantly impact the overall multi-year launching cost for the vehicle fleet depending upon when the HLLV will be ready for use. It is desirable to develop a model in which many trade studies can be performed. In one sample multi-year space program analysis, the total launch vehicle cost of implementing the program reduced from 50 percent to 25 percent. This indicates how critical it is to reduce space logistics costs. A linear programming model has been developed to answer such questions. The model is now in its second phase of development, and this paper will address the capabilities of the model and its intended uses. The main emphasis over the past year was to make the model user friendly and to incorporate additional realistic constraints that are difficult to represent mathematically. We have developed a methodology in which the user has to be knowledgeable about the mission model and the requirements of the payloads. We have found a representation that will cut down the solution space of the problem by inserting some preliminary tests to eliminate some infeasible vehicle solutions. The paper will address the handling of these additional constraints and the methodology for incorporating new costing information utilizing learning curve theory. The paper will review several test cases that will explore the preferred vehicle characteristics and the preferred period of construction, i.e., within the next decade, or in the first decade of the next century. Finally, the paper will explore the interaction

  5. Correcting circulation biases in a lower-resolution global general circulation model with data assimilation

    Science.gov (United States)

    Canter, Martin; Barth, Alexander; Beckers, Jean-Marie

    2016-12-01

    In this study, we aim at developing a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias by directly adding an additional source term into the model equations. This method is presented and tested first with a twin experiment on a fully controlled Lorenz '96 model. It is then applied to the lower-resolution global circulation NEMO-LIM2 model, with both a twin experiment and a real case experiment. Sea surface height observations are used to create a forcing to correct the poorly located and estimated currents. Validation is then performed throughout the use of other variables such as sea surface temperature and salinity. Results show that the method is able to consistently correct part of the model bias. The bias correction term is presented and is consistent with the limitations of the global circulation model causing bias on the oceanic currents.

  6. Correcting circulation biases in a lower-resolution global general circulation model with data assimilation

    Science.gov (United States)

    Canter, Martin; Barth, Alexander; Beckers, Jean-Marie

    2017-02-01

    In this study, we aim at developing a new method of bias correction using data assimilation. This method is based on the stochastic forcing of a model to correct bias by directly adding an additional source term into the model equations. This method is presented and tested first with a twin experiment on a fully controlled Lorenz '96 model. It is then applied to the lower-resolution global circulation NEMO-LIM2 model, with both a twin experiment and a real case experiment. Sea surface height observations are used to create a forcing to correct the poorly located and estimated currents. Validation is then performed throughout the use of other variables such as sea surface temperature and salinity. Results show that the method is able to consistently correct part of the model bias. The bias correction term is presented and is consistent with the limitations of the global circulation model causing bias on the oceanic currents.

  7. Potential sources of analytical bias and error in selected trace element data-quality analyses

    Science.gov (United States)

    Paul, Angela P.; Garbarino, John R.; Olsen, Lisa D.; Rosen, Michael R.; Mebane, Christopher A.; Struzeski, Tedmund M.

    2016-09-28

    Potential sources of analytical bias and error associated with laboratory analyses for selected trace elements where concentrations were greater in filtered samples than in paired unfiltered samples were evaluated by U.S. Geological Survey (USGS) Water Quality Specialists in collaboration with the USGS National Water Quality Laboratory (NWQL) and the Branch of Quality Systems (BQS).Causes for trace-element concentrations in filtered samples to exceed those in associated unfiltered samples have been attributed to variability in analytical measurements, analytical bias, sample contamination either in the field or laboratory, and (or) sample-matrix chemistry. These issues have not only been attributed to data generated by the USGS NWQL but have been observed in data generated by other laboratories. This study continues the evaluation of potential analytical bias and error resulting from matrix chemistry and instrument variability by evaluating the performance of seven selected trace elements in paired filtered and unfiltered surface-water and groundwater samples collected from 23 sampling sites of varying chemistries from six States, matrix spike recoveries, and standard reference materials.Filtered and unfiltered samples have been routinely analyzed on separate inductively coupled plasma-mass spectrometry instruments. Unfiltered samples are treated with hydrochloric acid (HCl) during an in-bottle digestion procedure; filtered samples are not routinely treated with HCl as part of the laboratory analytical procedure. To evaluate the influence of HCl on different sample matrices, an aliquot of the filtered samples was treated with HCl. The addition of HCl did little to differentiate the analytical results between filtered samples treated with HCl from those samples left untreated; however, there was a small, but noticeable, decrease in the number of instances where a particular trace-element concentration was greater in a filtered sample than in the associated

  8. Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix

    NARCIS (Netherlands)

    Bun, M.J.G.

    2003-01-01

    Approximation formulae are developed for the bias of ordinary and generalized Least Squares Dummy Variable (LSDV) estimators in dynamic panel data models. Results from Kiviet [Kiviet, J. F. (1995), on bias, inconsistency, and efficiency of various estimators in dynamic panel data models, J.

  9. Hybrid Predictor and Field-Biased Context Pixel Selection Based on PPVO

    Directory of Open Access Journals (Sweden)

    Hongyin Xiang

    2016-01-01

    Full Text Available Most pixel-value-ordering (PVO predictors generated prediction-errors including −1 and 1 in a block-by-block manner. Pixel-based PVO (PPVO method provided a novel pixel scan strategy in a pixel-by-pixel way. Prediction-error bin 0 is expanded for embedding with the help of equalizing context pixels for prediction. In this paper, a PPVO-based hybrid predictor (HPPVO is proposed as an extension. HPPVO predicts pixel in both positive and negative orientations. Assisted by expansion bins selection technique, this hybrid predictor presents an optimized prediction-error expansion strategy including bin 0. Furthermore, a novel field-biased context pixel selection is already developed, with which detailed correlations of around pixels are better exploited more than equalizing scheme merely. Experiment results show that the proposed HPPVO improves embedding capacity and enhances marked image fidelity. It also outperforms some other state-of-the-art methods of reversible data hiding, especially for moderate and large payloads.

  10. Kin-bias, breeding site selection and female fitness in a cannibalistic Neotropical frog.

    Science.gov (United States)

    Muralidhar, P; de Sá, F P; Haddad, C F B; Zamudio, K R

    2014-02-01

    Resource availability influences sexual selection within populations and determines whether behaviours such as territoriality or resource sharing are adaptive. In Thoropa taophora, a frog endemic to the Atlantic Coastal Rainforest of Brazil, males compete for and defend limited breeding sites while females often share breeding sites with other females; however, sharing breeding sites may involve costs due to cannibalism by conspecific tadpoles. We studied a breeding population of T. taophora to determine (i) whether this species exhibits polygynous mating involving female choice for territorial males and limited breeding resources; (ii) whether limited breeding resources create the potential for male-male cooperation in defence of neighbouring territories; and (iii) whether females sharing breeding sites exhibit kin-biased breeding site choice, possibly driven by fitness losses due to cannibalism among offspring of females sharing sites. We used microsatellites to reconstruct parentage and quantify relatedness at eight breeding sites in our focal population, where these sites are scarce, and in a second population, where sites are abundant. We found that at localities where the appropriate sites for reproduction are spatially limited, the mating system for this species is polygynous, with typically two females sharing a breeding site with a male. We also found that females exhibit negative kin-bias in their choice of breeding sites, potentially to maximize their inclusive fitness by avoiding tadpole cannibalism of highly related kin. Our results indicate that male territorial defence and female site sharing are likely important components of this mating system, and we propose that kinship-dependent avoidance in mating strategies may be more general than previously realized. © 2013 John Wiley & Sons Ltd.

  11. Model Selection Principles in Misspecified Models

    CERN Document Server

    Lv, Jinchi

    2010-01-01

    Model selection is of fundamental importance to high dimensional modeling featured in many contemporary applications. Classical principles of model selection include the Kullback-Leibler divergence principle and the Bayesian principle, which lead to the Akaike information criterion and Bayesian information criterion when models are correctly specified. Yet model misspecification is unavoidable when we have no knowledge of the true model or when we have the correct family of distributions but miss some true predictor. In this paper, we propose a family of semi-Bayesian principles for model selection in misspecified models, which combine the strengths of the two well-known principles. We derive asymptotic expansions of the semi-Bayesian principles in misspecified generalized linear models, which give the new semi-Bayesian information criteria (SIC). A specific form of SIC admits a natural decomposition into the negative maximum quasi-log-likelihood, a penalty on model dimensionality, and a penalty on model miss...

  12. Bayesian Model Selection and Statistical Modeling

    CERN Document Server

    Ando, Tomohiro

    2010-01-01

    Bayesian model selection is a fundamental part of the Bayesian statistical modeling process. The quality of these solutions usually depends on the goodness of the constructed Bayesian model. Realizing how crucial this issue is, many researchers and practitioners have been extensively investigating the Bayesian model selection problem. This book provides comprehensive explanations of the concepts and derivations of the Bayesian approach for model selection and related criteria, including the Bayes factor, the Bayesian information criterion (BIC), the generalized BIC, and the pseudo marginal lik

  13. A hybrid model of radio frequency biased inductively coupled plasma discharges: description of model and experimental validation in argon

    Science.gov (United States)

    Wen, De-Qi; Liu, Wei; Gao, Fei; Lieberman, M. A.; Wang, You-Nian

    2016-08-01

    A hybrid model, i.e. a global model coupled bidirectionally with a parallel Monte-Carlo collision (MCC) sheath model, is developed to investigate an inductively coupled discharge with a bias source. This hybrid model can self-consistently reveal the interaction between the bulk plasma and the radio frequency (rf) bias sheath. More specifically, the plasma parameters affecting characteristics of rf bias sheath (sheath length and self-bias) are calculated by a global model and the effect of the rf bias sheath on the bulk plasma is determined by the voltage drop of the rf bias sheath. Moreover, specific numbers of ions are tracked in the rf bias sheath and ultimately the ion energy distribution function (IEDF) incident on the bias electrode is obtained. To validate this model, both bulk plasma density and IEDF on the bias electrode in an argon discharge are compared with experimental measurements, and a good agreement is obtained. The advantage of this model is that it can quickly calculate the bulk plasma density and IEDF on the bias electrode, which are of practical interest in industrial plasma processing, and the model could be easily extended to serve for industrial gases.

  14. Bias associated with failing to incorporate dependence on event history in Markov models.

    Science.gov (United States)

    Bentley, Tanya G K; Kuntz, Karen M; Ringel, Jeanne S

    2010-01-01

    When using state-transition Markov models to simulate risk of recurrent events over time, incorporating dependence on higher numbers of prior episodes can increase model complexity, yet failing to capture this event history may bias model outcomes. This analysis assessed the tradeoffs between model bias and complexity when evaluating risks of recurrent events in Markov models. The authors developed a generic episode/relapse Markov cohort model, defining bias as the percentage change in events prevented with 2 hypothetical interventions (prevention and treatment) when incorporating 0 to 9 prior episodes in relapse risk versus a model with 10 such episodes. Magnitude and sign of bias were evaluated as a function of event and recovery risks, disease-specific mortality, and risk function. Bias was positive in the base case for a prevention strategy, indicating that failing to fully incorporate dependence on event history overestimated the prevention's predicted impact. For treatment, the bias was negative, indicating an underestimated benefit. Bias approached zero as the number of tracked prior episodes increased, and the average bias over 10 tracked episodes was greater with the exponential compared with linear functions of relapse risk and with treatment compared with prevention strategies. With linear and exponential risk functions, absolute bias reached 33% and 78%, respectively, in prevention and 52% and 85% in treatment. Failing to incorporate dependence on prior event history in subsequent relapse risk in Markov models can greatly affect model outcomes, overestimating the impact of prevention and treatment strategies by up to 85% and underestimating the impact in some treatment models by up to 20%. When at least 4 prior episodes are incorporated, bias does not exceed 26% in prevention or 11% in treatment.

  15. Volunteerism and self-selection bias in human positron emission tomography neuroimaging research.

    Science.gov (United States)

    Oswald, Lynn M; Wand, Gary S; Zhu, Shijun; Selby, Victoria

    2013-06-01

    Scientists have known for decades that persons who volunteer for behavioral research may be different from those who decline participation and that characteristics differentiating volunteers from non-volunteers may vary depending on the nature of the research. There is evidence that volunteer self-selection can impact representativeness of samples in studies involving physically or psychologically stressful procedures, such as electric shocks, sensory isolation, or drug effects. However, the degree to which self-selection influences sample characteristics in "stressful" studies involving positron emission tomography (PET) has not been evaluated. Since estimation of population parameters, robustness of findings, and validity of inferred relationships can all be impacted by volunteer bias, it is important to determine if self-selection may act as an unrecognized confound in such studies. In the present investigation, we obtained baseline data on 114 (56M, 58F) subjects who participated in a study involving completion of several self-report questionnaires and behavioral performance tasks. Participants were later given the opportunity to enroll in an [11C]raclopride PET study involving intravenous amphetamine (AMPH) administration. Demographic characteristics, personality traits, and task performance of subjects who consented to the latter study were compared with those who declined participation. Findings showed that the principal personality trait that distinguished the two groups was sensation-seeking; volunteers scored significantly higher on this dimension than non-volunteers. Males were more likely to volunteer than females. However, results of mediation analysis suggested that the relationship between gender and volunteer status was mediated by greater sensation-seeking traits in the males. Implications of these findings are discussed.

  16. On Inferring Demand for Health Care in the Presence of Anchoring, Acquiescence, and Selection Biases

    OpenAIRE

    Jay Bhattacharya; Adam Isen

    2008-01-01

    In the contingent valuation literature, both anchoring and acquiescence biases pose problems when using an iterative bidding game to infer willingness to pay. Anchoring bias occurs when the willingness to pay estimate is sensitive to the initially presented starting value. Acquiescence bias occurs when survey respondents exhibit a tendency to answer 'yes' to questions, regardless of their true preferences. More generally, whenever a survey format is used and not all of those contacted partici...

  17. Bias-corrected estimation in potentially mildly explosive autoregressive models

    DEFF Research Database (Denmark)

    Haufmann, Hendrik; Kruse, Robinson

    that the indirect inference approach oers a valuable alternative to other existing techniques. Its performance (measured by its bias and root mean squared error) is balanced and highly competitive across many different settings. A clear advantage is its applicability for mildly explosive processes. In an empirical...

  18. Investigating the mechanisms of seasonal ENSO phase locking bias in the ACCESS coupled model

    Science.gov (United States)

    Rashid, Harun A.; Hirst, Anthony C.

    2016-02-01

    The mechanisms of coupled model bias in seasonal ENSO phase locking are investigated using versions 1.0 and 1.3 of the CSIRO-BOM ACCESS coupled model (hereafter, ACCESS1.0 and ACCESS1.3, respectively). The two ACCESS coupled models are mostly similar in construction except for some differences, the most notable of which are in the cloud and land surface schemes used in the models. ACCESS1.0 simulates a realistic seasonal phase locking, with the ENSO variability peaking in December as in observations. On the other hand, the simulated ENSO variability in ACCESS1.3 peaks in March, a bias shown to be shared by many other CMIP5 models. To explore the mechanisms of this model bias, we contrast the atmosphere-ocean feedbacks associated with ENSO in both ACCESS model simulations and also compare the key feedbacks with those in other CMIP5 models. We find evidence that the ENSO phase locking bias in ACCESS1.3 is primarily caused by incorrect simulations of the shortwave feedback and the thermocline feedback in this model. The bias in the shortwave feedback is brought about by unrealistic SST-cloud interactions leading to a positive cloud feedback bias that is largest around March, in contrast to the strongest negative cloud feedback found in ACCESS1.0 simulations and observations at that time. The positive cloud feedback bias in ACCESS1.3 is the result of a dominant role played by the low-level clouds in its modeled SST-cloud interactions in the tropical eastern Pacific. Two factors appear to contribute to the dominance of low-level clouds in ACCESS1.3: the occurrence of a stronger mean descending motion bias and, to a lesser extent, a larger mean SST cold bias during March-April in ACCESS1.3 than in ACCESS1.0. A similar association is found between the positive cloud feedback bias and the biases in spring-time mean descending motion and SST for a group of CMIP5 models that show a seasonal phase locking bias similar to ACCESS1.3. Significant differences are also found

  19. The Ouroboros Model, selected facets.

    Science.gov (United States)

    Thomsen, Knud

    2011-01-01

    The Ouroboros Model features a biologically inspired cognitive architecture. At its core lies a self-referential recursive process with alternating phases of data acquisition and evaluation. Memory entries are organized in schemata. The activation at a time of part of a schema biases the whole structure and, in particular, missing features, thus triggering expectations. An iterative recursive monitor process termed 'consumption analysis' is then checking how well such expectations fit with successive activations. Mismatches between anticipations based on previous experience and actual current data are highlighted and used for controlling the allocation of attention. A measure for the goodness of fit provides feedback as (self-) monitoring signal. The basic algorithm works for goal directed movements and memory search as well as during abstract reasoning. It is sketched how the Ouroboros Model can shed light on characteristics of human behavior including attention, emotions, priming, masking, learning, sleep and consciousness.

  20. Introduction. Modelling natural action selection.

    Science.gov (United States)

    Prescott, Tony J; Bryson, Joanna J; Seth, Anil K

    2007-09-29

    Action selection is the task of resolving conflicts between competing behavioural alternatives. This theme issue is dedicated to advancing our understanding of the behavioural patterns and neural substrates supporting action selection in animals, including humans. The scope of problems investigated includes: (i) whether biological action selection is optimal (and, if so, what is optimized), (ii) the neural substrates for action selection in the vertebrate brain, (iii) the role of perceptual selection in decision-making, and (iv) the interaction of group and individual action selection. A second aim of this issue is to advance methodological practice with respect to modelling natural action section. A wide variety of computational modelling techniques are therefore employed ranging from formal mathematical approaches through to computational neuroscience, connectionism and agent-based modelling. The research described has broad implications for both natural and artificial sciences. One example, highlighted here, is its application to medical science where models of the neural substrates for action selection are contributing to the understanding of brain disorders such as Parkinson's disease, schizophrenia and attention deficit/hyperactivity disorder.

  1. A General Framework for Considering Selection Bias in EHR-Based Studies: What Data Are Observed and Why?

    Science.gov (United States)

    Haneuse, Sebastien; Daniels, Michael

    2016-01-01

    Electronic health records (EHR) data are increasingly seen as a resource for cost-effective comparative effectiveness research (CER). Since EHR data are collected primarily for clinical and/or billing purposes, their use for CER requires consideration of numerous methodologic challenges including the potential for confounding bias, due to a lack of randomization, and for selection bias, due to missing data. In contrast to the recent literature on confounding bias in EHR-based CER, virtually no attention has been paid to selection bias possibly due to the belief that standard methods for missing data can be readily-applied. Such methods, however, hinge on an overly simplistic view of the available/missing EHR data, so that their application in the EHR setting will often fail to completely control selection bias. Motivated by challenges we face in an on-going EHR-based comparative effectiveness study of choice of antidepressant treatment and long-term weight change, we propose a new general framework for selection bias in EHR-based CER. Crucially, the framework provides structure within which researchers can consider the complex interplay between numerous decisions, made by patients and health care providers, which give rise to health-related information being recorded in the EHR system, as well as the wide variability across EHR systems themselves. This, in turn, provides structure within which: (i) the transparency of assumptions regarding missing data can be enhanced, (ii) factors relevant to each decision can be elicited, and (iii) statistical methods can be better aligned with the complexity of the data. PMID:27668265

  2. Robust inference in sample selection models

    KAUST Repository

    Zhelonkin, Mikhail

    2015-11-20

    The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of Heckman\\'s two-stage estimator, and we demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the model assumed. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and we compare the performance of the classical and robust methods in a Monte Carlo simulation study.

  3. N3 Bias Field Correction Explained as a Bayesian Modeling Method

    DEFF Research Database (Denmark)

    Larsen, Christian Thode; Iglesias, Juan Eugenio; Van Leemput, Koen

    2014-01-01

    Although N3 is perhaps the most widely used method for MRI bias field correction, its underlying mechanism is in fact not well understood. Specifically, the method relies on a relatively heuristic recipe of alternating iterative steps that does not optimize any particular objective function....... In this paper we explain the successful bias field correction properties of N3 by showing that it implicitly uses the same generative models and computational strategies as expectation maximization (EM) based bias field correction methods. We demonstrate experimentally that purely EM-based methods are capable...... of producing bias field correction results comparable to those of N3 in less computation time....

  4. Association between mean and interannual equatorial Indian Ocean subsurface temperature bias in a coupled model

    Science.gov (United States)

    Srinivas, G.; Chowdary, Jasti S.; Gnanaseelan, C.; Prasad, K. V. S. R.; Karmakar, Ananya; Parekh, Anant

    2017-05-01

    In the present study the association between mean and interannual subsurface temperature bias over the equatorial Indian Ocean (EIO) is investigated during boreal summer (June through September; JJAS) in the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFSv2) hindcast. Anomalously high subsurface warm bias (greater than 3 °C) over the eastern EIO (EEIO) region is noted in CFSv2 during summer, which is higher compared to other parts of the tropical Indian Ocean. Prominent eastward current bias in the upper 100 m over the EIO region induced by anomalous westerly winds is primarily responsible for subsurface temperature bias. The eastward currents transport warm water to the EEIO and is pushed down to subsurface due to downwelling. Thus biases in both horizontal and vertical currents over the EIO region support subsurface warm bias. The evolution of systematic subsurface warm bias in the model shows strong interannual variability. These maximum subsurface warming episodes over the EEIO are mainly associated with La Niña like forcing. Strong convergence of low level winds over the EEIO and Maritime continent enhanced the westerly wind bias over the EIO during maximum warming years. This low level convergence of wind is induced by the bias in the gradient in the mean sea level pressure with positive bias over western EIO and negative bias over EEIO and parts of western Pacific. Consequently, changes in the atmospheric circulation associated with La Niña like conditions affected the ocean dynamics by modulating the current bias thereby enhancing the subsurface warm bias over the EEIO. It is identified that EEIO subsurface warming is stronger when La Niña co-occurred with negative Indian Ocean Dipole events as compared to La Niña only years in the model. Ocean general circulation model (OGCM) experiments forced with CFSv2 winds clearly support our hypothesis that ocean dynamics influenced by westerly winds bias is primarily

  5. Scale-Scale Correlation as Discriminant Among the Biased Galaxy Formation Models

    Institute of Scientific and Technical Information of China (English)

    FENG Long-Long; XIANG Shou-Ping

    2001-01-01

    Using the mock galaxy catalogues created from the N-body simulations, various biasing prescriptions for modelling the relative distribution between the galaxies and the underlying dark matter are statistically tested by using scale-scale correlation. We found that the scale-scale correlation is capable of breaking the model degeneracy indicated by the low-order clustering statistics, and could be taken as an effective discriminant among a variety of biasing models. Particularly, comparing with the APM bright galaxy catalogue, we infer that the two parameter Lagrangian biasing model gives the best fit to the observed clustering features.

  6. MLE's bias pathology, Model Updated Maximum Likelihood Estimates and Wallace's Minimum Message Length method

    OpenAIRE

    Yatracos, Yannis G.

    2013-01-01

    The inherent bias pathology of the maximum likelihood (ML) estimation method is confirmed for models with unknown parameters $\\theta$ and $\\psi$ when MLE $\\hat \\psi$ is function of MLE $\\hat \\theta.$ To reduce $\\hat \\psi$'s bias the likelihood equation to be solved for $\\psi$ is updated using the model for the data $Y$ in it. Model updated (MU) MLE, $\\hat \\psi_{MU},$ often reduces either totally or partially $\\hat \\psi$'s bias when estimating shape parameter $\\psi.$ For the Pareto model $\\hat...

  7. [Analysis of selection bias in the pilot study of a longitudinal study on aging in Spain].

    Science.gov (United States)

    Rodríguez Laso, Ángel; Urdaneta Artola, Elena; de la Fuente Sánchez, Miguel; Galindo Moreno, Eva; Yanguas Lezáun, José Javier; Rodríguez Rodríguez, Vicente

    2013-01-01

    To demonstrate that selection of a probabilistic sample at a national level for a study of aging in Spain is subject to selection bias. To quantify the losses produced after each phase of the administration of a questionnaire. We performed a cross-sectional study of the Spanish community-dwelling population aged 50 years or older between 2010 and 2011. Through multivariate logistic regressions, the characteristics of the census tract of the patients' residence were compared between those who agreed (n = 5,813) or refused (n = 7,023) to be included in the sampling frame and between those who agreed (n = 1,677) or refused (n = 2,875) to participate in the study. The individual characteristics of persons who responded (n = 1,398) or refused to respond (n = 346) to a face-to-face questionnaire administered after a telephone interview were also compared. In addition, the reasons for refusal were studied. The most frequent specific reasons for refusing to be included in the sampling frame or to participate in the study were poor health and disability (14.4% and 27.9%, respectively). In both cases, refusal was more frequent in the census tracts of districts with a lower socioeconomic level or those located in Catalonia, Guipúzcoa or Biscay. Individuals older than 81 participated less frequently in the face-to-face questionnaire. Between 8.6% and 18.4% of participants were lost at each stage of information retrieval. Probabilistic sampling in sampling points chosen by the researchers would allow more resources to be devoted to increasing response rates among the groups who are less likely to participate. Questions should be concentrated in only one shorter questionnaire, administered before blood extraction. Copyright © 2012 SESPAS. Published by Elsevier Espana. All rights reserved.

  8. Ion current rectification inversion in conic nanopores: nonequilibrium ion transport biased by ion selectivity and spatial asymmetry.

    Science.gov (United States)

    Yan, Yu; Wang, Lin; Xue, Jianming; Chang, Hsueh-Chia

    2013-01-28

    We show both theoretically and experimentally that the ion-selectivity of a conic nanopore, as defined by a normalized density of the surface charge, significantly affects ion current rectification across the pore. For weakly selective negatively charged pores, intra-pore ion transport controls the current and internal ion enrichment/depletion at positive/reverse biased voltage (current enters/leaves through the tip, respectively), which is responsible for current rectification. For strongly selective negatively charged pores under positive bias, the current can be reduced by external field focusing and concentration depletion at the tip at low ionic strengths and high voltages, respectively. These external phenomena produce a rectification inversion for highly selective pores at high (low) voltage (ionic strength). With an asymptotic analysis of the intra-pore and external ion transport, we derive simple scaling laws to quantitatively capture empirical and numerical data for ion current rectification and rectification inversion of conic nanopores.

  9. Objects in Kepler's Mirror May be Larger Than They Appear: Bias and Selection Effects in Transiting Planet Surveys

    Science.gov (United States)

    Gaidos, Eric; Mann, Andrew W.

    2013-01-01

    Statistical analyses of large surveys for transiting planets such as the Kepler mission must account for systematic errors and biases. Transit detection depends not only on the planet's radius and orbital period, but also on host star properties. Thus, a sample of stars with transiting planets may not accurately represent the target population. Moreover, targets are selected using criteria such as a limiting apparent magnitude. These selection effects, combined with uncertainties in stellar radius, lead to biases in the properties of transiting planets and their host stars. We quantify possible biases in the Kepler survey. First, Eddington bias produced by a steep planet radius distribution and uncertainties in stellar radius results in a 15%-20% overestimate of planet occurrence. Second, the magnitude limit of the Kepler target catalog induces Malmquist bias toward large, more luminous stars and underestimation of the radii of about one-third of candidate planets, especially those larger than Neptune. Third, because metal-poor stars are smaller, stars with detected planets will be very slightly (target average. Fourth, uncertainties in stellar radii produce correlated errors in planet radius and stellar irradiation. A previous finding, that highly irradiated giants are more likely to have "inflated" radii, remains significant, even accounting for this effect. In contrast, transit depth is negatively correlated with stellar metallicity even in the absence of any intrinsic correlation, and a previous claim of a negative correlation between giant planet transit depth and stellar metallicity is probably an artifact.

  10. Method for removing atomic-model bias in macromolecular crystallography

    Science.gov (United States)

    Terwilliger, Thomas C.

    2006-08-01

    Structure factor bias in an electron density map for an unknown crystallographic structure is minimized by using information in a first electron density map to elicit expected structure factor information. Observed structure factor amplitudes are combined with a starting set of crystallographic phases to form a first set of structure factors. A first electron density map is then derived and features of the first electron density map are identified to obtain expected distributions of electron density. Crystallographic phase probability distributions are established for possible crystallographic phases of reflection k, and the process is repeated as k is indexed through all of the plurality of reflections. An updated electron density map is derived from the crystallographic phase probability distributions for each one of the reflections. The entire process is then iterated to obtain a final set of crystallographic phases with minimum bias from known electron density maps.

  11. An investigation of tropical Atlantic bias in a high-resolution coupled regional climate model

    Energy Technology Data Exchange (ETDEWEB)

    Patricola, Christina M.; Saravanan, R.; Hsieh, Jen-Shan [Texas A and M University, Department of Atmospheric Sciences, College Station, TX (United States); Li, Mingkui; Xu, Zhao [Texas A and M University, Department of Oceanography, College Station, TX (United States); Ocean University of China, Key Laboratory of Physical Oceanography of Ministry of Education, Qingdao (China); Chang, Ping [Texas A and M University, Department of Oceanography, College Station, TX (United States); Ocean University of China, Key Laboratory of Physical Oceanography of Ministry of Education, Qingdao (China); Second Institute of Oceanography, State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou, Zhejiang (China)

    2012-11-15

    Coupled atmosphere-ocean general circulation models (AOGCMs) commonly fail to simulate the eastern equatorial Atlantic boreal summer cold tongue and produce a westerly equatorial trade wind bias. This tropical Atlantic bias problem is investigated with a high-resolution (27-km atmosphere represented by the Weather Research and Forecasting Model, 9-km ocean represented by the Regional Ocean Modeling System) coupled regional climate model. Uncoupled atmospheric simulations test climate sensitivity to cumulus, land-surface, planetary boundary layer, microphysics, and radiation parameterizations and reveal that the radiation scheme has a pronounced impact in the tropical Atlantic. The CAM radiation simulates a dry precipitation (up to -90%) and cold land-surface temperature (up to -8 K) bias over the Amazon related to an over-representation of low-level clouds and almost basin-wide westerly trade wind bias. The Rapid Radiative Transfer Model and Goddard radiation simulates doubled Amazon and Congo Basin precipitation rates and a weak eastern Atlantic trade wind bias. Season-long high-resolution coupled regional model experiments indicate that the initiation of the warm eastern equatorial Atlantic sea surface temperature (SST) bias is more sensitive to the local rather than basin-wide trade wind bias and to a wet Congo Basin instead of dry Amazon - which differs from AOGCM simulations. Comparisons between coupled and uncoupled simulations suggest a regional Bjerknes feedback confined to the eastern equatorial Atlantic amplifies the initial SST, wind, and deepened thermocline bias, while barrier layer feedbacks are relatively unimportant. The SST bias in some CRCM simulations resembles the typical AOGCM bias indicating that increasing resolution is unlikely a simple solution to this problem. (orig.)

  12. Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction

    Directory of Open Access Journals (Sweden)

    Boulesteix Anne-Laure

    2009-12-01

    Full Text Available Abstract Background In biometric practice, researchers often apply a large number of different methods in a "trial-and-error" strategy to get as much as possible out of their data and, due to publication pressure or pressure from the consulting customer, present only the most favorable results. This strategy may induce a substantial optimistic bias in prediction error estimation, which is quantitatively assessed in the present manuscript. The focus of our work is on class prediction based on high-dimensional data (e.g. microarray data, since such analyses are particularly exposed to this kind of bias. Methods In our study we consider a total of 124 variants of classifiers (possibly including variable selection or tuning steps within a cross-validation evaluation scheme. The classifiers are applied to original and modified real microarray data sets, some of which are obtained by randomly permuting the class labels to mimic non-informative predictors while preserving their correlation structure. Results We assess the minimal misclassification rate over the different variants of classifiers in order to quantify the bias arising when the optimal classifier is selected a posteriori in a data-driven manner. The bias resulting from the parameter tuning (including gene selection parameters as a special case and the bias resulting from the choice of the classification method are examined both separately and jointly. Conclusions The median minimal error rate over the investigated classifiers was as low as 31% and 41% based on permuted uninformative predictors from studies on colon cancer and prostate cancer, respectively. We conclude that the strategy to present only the optimal result is not acceptable because it yields a substantial bias in error rate estimation, and suggest alternative approaches for properly reporting classification accuracy.

  13. Introducing Decorated HODs: modeling assembly bias in the galaxy-halo connection

    CERN Document Server

    Hearin, Andrew P; Bosch, Frank C van den; Campbell, Duncan; Tollerud, Erik

    2015-01-01

    The connection between galaxies and dark matter halos is often inferred from data using probabilistic models, such as the Halo Occupation Distribution (HOD). Conventional HOD formulations assume that only halo mass governs the galaxy-halo connection. Violations of this assumption, known as galaxy assembly bias, threaten the HOD program. We introduce decorated HODs, a new, flexible class of models designed to account for assembly bias. Decorated HODs minimally expand the parameter space and maximize the independence between traditional and novel HOD parameters. We use decorated HODs to quantify the influence of assembly bias on clustering and lensing statistics. For SDSS-like samples, the impact of assembly bias on galaxy clustering can be as large as a factor of two on r ~ 200 kpc scales and ~15% in the linear regime. Assembly bias can either enhance or diminish clustering on large scales, but generally increases clustering on scales r <~ 1 Mpc. We performed our calculations with Halotools, an open-source,...

  14. Pangenome evidence for higher codon usage bias and stronger translational selection in core genes of Escherichia coli

    Directory of Open Access Journals (Sweden)

    Shixiang Sun

    2016-08-01

    Full Text Available Codon usage bias, as a combined interplay from mutation and selection, has been intensively studied in Escherichia coli. However, codon usage analysis in an E. coli pangenome remains unexplored and the relative importance of mutation and selection acting on core genes and strain-specific genes is unknown. Here we perform comprehensive codon usage analyses based on a collection of multiple complete genome sequences of E. coli. Our results show that core genes that are present in all strains have higher codon usage bias than strain-specific genes that are unique to single strains. We further explore the forces in influencing codon usage and investigate the difference of the major force between core and strain-specific genes. Our results demonstrate that although mutation may exert genome-wide influences on codon usage acting similarly in different gene sets, selection dominates as an important force to shape biased codon usage as genes are present in an increased number of strains. Together, our results provide important insights for better understanding genome plasticity and complexity as well as evolutionary mechanisms behind codon usage bias.

  15. Pangenome Evidence for Higher Codon Usage Bias and Stronger Translational Selection in Core Genes of Escherichia coli.

    Science.gov (United States)

    Sun, Shixiang; Xiao, Jingfa; Zhang, Huiyong; Zhang, Zhang

    2016-01-01

    Codon usage bias, as a combined interplay from mutation and selection, has been intensively studied in Escherichia coli. However, codon usage analysis in an E. coli pangenome remains unexplored and the relative importance of mutation and selection acting on core genes and strain-specific genes is unknown. Here we perform comprehensive codon usage analyses based on a collection of multiple complete genome sequences of E. coli. Our results show that core genes that are present in all strains have higher codon usage bias than strain-specific genes that are unique to single strains. We further explore the forces in influencing codon usage and investigate the difference of the major force between core and strain-specific genes. Our results demonstrate that although mutation may exert genome-wide influences on codon usage acting similarly in different gene sets, selection dominates as an important force to shape biased codon usage as genes are present in an increased number of strains. Together, our results provide important insights for better understanding genome plasticity and complexity as well as evolutionary mechanisms behind codon usage bias.

  16. Limitation of inverse probability-of-censoring weights in estimating survival in the presence of strong selection bias.

    Science.gov (United States)

    Howe, Chanelle J; Cole, Stephen R; Chmiel, Joan S; Muñoz, Alvaro

    2011-03-01

    In time-to-event analyses, artificial censoring with correction for induced selection bias using inverse probability-of-censoring weights can be used to 1) examine the natural history of a disease after effective interventions are widely available, 2) correct bias due to noncompliance with fixed or dynamic treatment regimens, and 3) estimate survival in the presence of competing risks. Artificial censoring entails censoring participants when they meet a predefined study criterion, such as exposure to an intervention, failure to comply, or the occurrence of a competing outcome. Inverse probability-of-censoring weights use measured common predictors of the artificial censoring mechanism and the outcome of interest to determine what the survival experience of the artificially censored participants would be had they never been exposed to the intervention, complied with their treatment regimen, or not developed the competing outcome. Even if all common predictors are appropriately measured and taken into account, in the context of small sample size and strong selection bias, inverse probability-of-censoring weights could fail because of violations in assumptions necessary to correct selection bias. The authors used an example from the Multicenter AIDS Cohort Study, 1984-2008, regarding estimation of long-term acquired immunodeficiency syndrome-free survival to demonstrate the impact of violations in necessary assumptions. Approaches to improve correction methods are discussed.

  17. Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model

    Directory of Open Access Journals (Sweden)

    Isaac Mugume

    2016-01-01

    Full Text Available Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE, the mean absolute error (MAE, mean error (ME, skewness, and the bias easy estimate (BES and nonparametric (the sign test, STM methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.

  18. Extra-tropical origin of equatorial Pacific cold bias in climate models

    Science.gov (United States)

    Burls, N.; Muir, L.; Vincent, E. M.; Fedorov, A. V.

    2015-12-01

    General circulation models frequently suffer from a substantial cold bias in equatorial Pacific sea surface temperatures (SSTs). For instance, the majority of the climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) have this particular problem (17 out of the 26 models evaluated in this project). Our study investigates the extent to which these equatorial cold biases are related to mean climate biases generated in the extra-tropics and then communicated to the equator via the oceanic subtropical cells (STCs). With an evident relationship across the CMIP5 models between equatorial SSTs and upper ocean temperatures in the extra-tropical subduction regions, our analysis confirms that cold SST biases within the extra-tropical Pacific translate into a cold equatorial SST bias via the STCs. An assessment of the relationship between these extra-tropical SST biases and surface heat flux components indicates a link to biases in the simulated shortwave fluxes. Further sensitivity studies with a climate model (CESM) in which extra-tropical cloud albedo is systematically varied illustrate the influence of cloud albedo perturbations, not only directly above the oceanic subduction regions but across the extended extra-tropical Pacific, on the equatorial bias. The CESM experiments reveal a quadratic relationship between extra-tropical albedo and the root-mean-square-error in equatorial SSTs - a relationship with which the CMIP5 models generally agree. Thus, our study suggests that one way to improve the equatorial cold bias is to improve the representation of cloud albedo in mid-latitudes.

  19. Intergroup bias.

    Science.gov (United States)

    Hewstone, Miles; Rubin, Mark; Willis, Hazel

    2002-01-01

    This chapter reviews the extensive literature on bias in favor of in-groups at the expense of out-groups. We focus on five issues and identify areas for future research: (a) measurement and conceptual issues (especially in-group favoritism vs. out-group derogation, and explicit vs. implicit measures of bias); (b) modern theories of bias highlighting motivational explanations (social identity, optimal distinctiveness, uncertainty reduction, social dominance, terror management); (c) key moderators of bias, especially those that exacerbate bias (identification, group size, status and power, threat, positive-negative asymmetry, personality and individual differences); (d) reduction of bias (individual vs. intergroup approaches, especially models of social categorization); and (e) the link between intergroup bias and more corrosive forms of social hostility.

  20. Inferential selection bias in a study of racial bias: Revisiting ‘Working twice as hard to get half as far’

    Directory of Open Access Journals (Sweden)

    LJ Zigerell

    2015-02-01

    Full Text Available This study revisits an important recent article about racial bias and finds that many of its inferences are weakened when we analyze the data more completely. DeSante in 2013 reported evidence from a survey experiment indicating that Americans reward Whites more than Blacks for hard work but penalize Blacks more than Whites for laziness. However, the present study demonstrates that these inferences were based on an unrepresentative selection of possible analyses: the original article does not include all possible equivalent or relevant analyses, and when results from these additional analyses are combined with the results reported in the original article, the strength of inferences is weakened. Moreover, newly-reported evidence reveals heterogeneity in racial bias: respondents given a direct choice between equivalent targets of different races favored the Black target over the White target. These results illustrate how the presence of researcher degrees of freedom can foster production of inferences that are not representative of all inferences that a set of data could produce. This study thus highlights the value of preregistering research design protocols and required public posting of data.

  1. A selective emotional decision-making bias elicited by facial expressions.

    Science.gov (United States)

    Furl, Nicholas; Gallagher, Shannon; Averbeck, Bruno B

    2012-01-01

    Emotional and social information can sway otherwise rational decisions. For example, when participants decide between two faces that are probabilistically rewarded, they make biased choices that favor smiling relative to angry faces. This bias may arise because facial expressions evoke positive and negative emotional responses, which in turn may motivate social approach and avoidance. We tested a wide range of pictures that evoke emotions or convey social information, including animals, words, foods, a variety of scenes, and faces differing in trustworthiness or attractiveness, but we found only facial expressions biased decisions. Our results extend brain imaging and pharmacological findings, which suggest that a brain mechanism supporting social interaction may be involved. Facial expressions appear to exert special influence over this social interaction mechanism, one capable of biasing otherwise rational choices. These results illustrate that only specific types of emotional experiences can best sway our choices.

  2. A collective opinion formation model under Bayesian updating and confirmation bias

    CERN Document Server

    Nishi, Ryosuke

    2013-01-01

    We propose a collective opinion formation model with a so-called confirmation bias. The confirmation bias is a psychological effect with which, in the context of opinion formation, an individual in favor of an opinion is prone to misperceive new incoming information as supporting the current belief of the individual. Our model modifies a Bayesian decision-making model for single individuals (Rabin and Schrag, Q. J. Econ. 114, 37 (1999)) to the case of a well-mixed population of interacting individuals in the absence of the external input. We numerically simulate the model to show that all the agents eventually agree on one of the two opinions only when the confirmation bias is weak. Otherwise, the stochastic population dynamics ends up creating a disagreement configuration (also called polarization), particularly for large system sizes. A strong confirmation bias allows various final disagreement configurations with different fractions of the individuals in favor of the opposite opinions.

  3. MODFLOW-NWT model of a hypothetical stream-aquifer system to assess capture map bias

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — A MODFLOW-NWT (version 1.0.9) model of a hypothetical stream-aquifer system is presented for the evaluation and characterization of capture map bias. The...

  4. Model parameter estimation bias induced by earthquake magnitude cut-off

    Science.gov (United States)

    Harte, D. S.

    2016-02-01

    We evaluate the bias in parameter estimates of the ETAS model. We show that when a simulated catalogue is magnitude-truncated there is considerable bias, whereas when it is not truncated there is no discernible bias. We also discuss two further implied assumptions in the ETAS and other self-exciting models. First, that the triggering boundary magnitude is equivalent to the catalogue completeness magnitude. Secondly, the assumption in the Gutenberg-Richter relationship that numbers of events increase exponentially as magnitude decreases. These two assumptions are confounded with the magnitude truncation effect. We discuss the effect of these problems on analyses of real earthquake catalogues.

  5. A computational study of how orientation bias in the lateral geniculate nucleus can give rise to orientation selectivity in primary visual cortex

    Directory of Open Access Journals (Sweden)

    Levin eKuhlmann

    2011-10-01

    Full Text Available Controversy remains about how orientation selectivity emerges in simple cells of the mammalian primary visual cortex. In this paper, we present a computational model of how the orientation-biased responses of cells in lateral geniculate nucleus can contribute to the orientation selectivity in simple cells in cats. We propose that simple cells are excited by lateral geniculate fields with an orientation-bias and disynaptically inhibited by unoriented lateral geniculate fields (or biased fields pooled across orientations, both at approximately the same retinotopic co-ordinates. This interaction, combined with recurrent cortical excitation and inhibition, helps to create the sharp orientation tuning seen in simple cell responses. Along with describing orientation selectivity, the model also accounts for the spatial frequency and length response functions in simple cells, in normal conditions as well as under the influence of the GABAA antagonist, bicuculline. In addition, the model captures the response properties of LGN and simple cells to simultaneous visual stimulation and electrical stimulation of the LGN. We show that the sharp selectivity for stimulus orientation seen in primary visual cortical cells can be achieved without the excitatory convergence of the lateral geniculate nucleus input cells with receptive fields along a line in visual space, which has been a core assumption in classical models of visual cortex. We have also simulated how the full range of orientations seen in the cortex can emerge from the activity among broadly tuned channels tuned to a limited number of optimum orientations, just as in the classical case of coding for colour in trichromatic primates.

  6. A Neurodynamical Model for Selective Visual Attention

    Institute of Scientific and Technical Information of China (English)

    QU Jing-Yi; WANG Ru-Bin; ZHANG Yuan; DU Ying

    2011-01-01

    attention is a traditional problem in computer vision and robotics.A number of investigations have been made to clarify how the problem of object selection and segmentation is solved by the brain.[1] Niebur and Koch have presented a model for the experimental data recorded from the striate and extrastriate areas of the neocortex.[2] Corchs and Deco developed a model of visual conjunction-feature search where attention bias was modulated by top-down signals,from memory that coded target feature values to feature processing structures in the primary areas of the visual cortex.[3

  7. How Do Biases in General Circulation Models Affect Projections of Aridity and Drought?

    Science.gov (United States)

    Ficklin, D. L.; Abatzoglou, J. T.; Robeson, S. M.; Dufficy, A. L.

    2015-12-01

    Unless corrected, biases in General Circulation Models (GCMs) can affect hydroclimatological applications and projections. Compared to a raw GCM ensemble (direct GCM output), bias-corrected GCM inputs correct for systematic errors and can produce high-resolution projections that are useful for impact analyses. By examining the difference between raw and bias-corrected GCMs for the continental United States, this work highlights how GCM biases can affect projections of aridity (defined as precipitation (P)/potential evapotranspiration (PET)) and drought (using the Palmer Drought Severity Index (PDSI)). At the annual time scale for spatial averages over the continental United States, the raw GCM ensemble median has a historical positive precipitation bias (+24%) and negative PET bias (-7%) compared to the bias-corrected output. While both GCM ensembles (raw and bias-corrected) result in drier conditions in the future, the bias-corrected GCMs produce enhanced aridity (number of months with PET>P) in the late 21st century (2070-2099) compared to the historical climate (1950-1979). For the western United States, the bias-corrected GCM ensemble estimates much less humid and sub-humid conditions (based on P/PET categorical values) than the raw GCM ensemble. However, using June, July, and August PDSI, the bias-corrected GCM ensemble projects less acute decreases for the southwest United States compared to the raw GCM ensemble (1 to 2 PDSI units higher) as a result of larger decreases in projected precipitation in the raw GCM ensemble. A number of examples and ecological implications of this work for the western United States will be presented.

  8. Hydrological Modeling in Northern Tunisia with Regional Climate Model Outputs: Performance Evaluation and Bias-Correction in Present Climate Conditions

    Directory of Open Access Journals (Sweden)

    Asma Foughali

    2015-07-01

    Full Text Available This work aims to evaluate the performance of a hydrological balance model in a watershed located in northern Tunisia (wadi Sejnane, 378 km2 in present climate conditions using input variables provided by four regional climate models. A modified version (MBBH of the lumped and single layer surface model BBH (Bucket with Bottom Hole model, in which pedo-transfer parameters estimated using watershed physiographic characteristics are introduced is adopted to simulate the water balance components. Only two parameters representing respectively the water retention capacity of the soil and the vegetation resistance to evapotranspiration are calibrated using rainfall-runoff data. The evaluation criterions for the MBBH model calibration are: relative bias, mean square error and the ratio of mean actual evapotranspiration to mean potential evapotranspiration. Daily air temperature, rainfall and runoff observations are available from 1960 to 1984. The period 1960–1971 is selected for calibration while the period 1972–1984 is chosen for validation. Air temperature and precipitation series are provided by four regional climate models (DMI, ARP, SMH and ICT from the European program ENSEMBLES, forced by two global climate models (GCM: ECHAM and ARPEGE. The regional climate model outputs (precipitation and air temperature are compared to the observations in terms of statistical distribution. The analysis was performed at the seasonal scale for precipitation. We found out that RCM precipitation must be corrected before being introduced as MBBH inputs. Thus, a non-parametric quantile-quantile bias correction method together with a dry day correction is employed. Finally, simulated runoff generated using corrected precipitation from the regional climate model SMH is found the most acceptable by comparison with runoff simulated using observed precipitation data, to reproduce the temporal variability of mean monthly runoff. The SMH model is the most accurate to

  9. Bayesian Evidence and Model Selection

    CERN Document Server

    Knuth, Kevin H; Malakar, Nabin K; Mubeen, Asim M; Placek, Ben

    2014-01-01

    In this paper we review the concept of the Bayesian evidence and its application to model selection. The theory is presented along with a discussion of analytic, approximate and numerical techniques. Application to several practical examples within the context of signal processing are discussed.

  10. Facial expression judgments support a socio-relational model, rather than a negativity bias model of political psychology.

    Science.gov (United States)

    Vigil, Jacob M; Strenth, Chance

    2014-06-01

    Self-reported opinions and judgments may be more rooted in expressive biases than in cognitive processing biases, and ultimately operate within a broader behavioral style for advertising the capacity - versus the trustworthiness - dimension of human reciprocity potential. Our analyses of facial expression judgments of likely voters are consistent with this thesis, and directly contradict one major prediction from the authors' "negativity-bias" model.

  11. Visual selective attention biases contribute to the other-race effect among 9-month-old infants.

    Science.gov (United States)

    Markant, Julie; Oakes, Lisa M; Amso, Dima

    2016-04-01

    During the first year of life, infants maintain their ability to discriminate faces from their own race but become less able to differentiate other-race faces. Though this is likely due to daily experience with own-race faces, the mechanisms linking repeated exposure to optimal face processing remain unclear. One possibility is that frequent experience with own-race faces generates a selective attention bias to these faces. Selective attention elicits enhancement of attended information and suppression of distraction to improve visual processing of attended objects. Thus attention biases to own-race faces may boost processing and discrimination of these faces relative to other-race faces. We used a spatial cueing task to bias attention to own- or other-race faces among Caucasian 9-month-old infants. Infants discriminated faces in the focus of the attention bias, regardless of race, indicating that infants remained sensitive to differences among other-race faces. Instead, efficacy of face discrimination reflected the extent of attention engagement.

  12. Selection Bias, Vote Counting, and Money-Priming Effects: A Comment on Rohrer, Pashler, and Harris (2015) and Vohs (2015)

    Science.gov (United States)

    2016-01-01

    When a series of studies fails to replicate a well-documented effect, researchers might be tempted to use a “vote counting” approach to decide whether the effect is reliable—that is, simply comparing the number of successful and unsuccessful replications. Vohs’s (2015) response to the absence of money priming effects reported by Rohrer, Pashler, and Harris (2015) provides an example of this approach. Unfortunately, vote counting is a poor strategy to assess the reliability of psychological findings because it neglects the impact of selection bias and questionable research practices. In the present comment, we show that a range of meta-analytic tools indicate irregularities in the money priming literature discussed by Rohrer et al. and Vohs, which all point to the conclusion that these effects are distorted by selection bias, reporting biases, or p-hacking. This could help to explain why money-priming effects have proven unreliable in a number of direct replication attempts in which biases have been minimized through preregistration or transparent reporting. Our major conclusion is that the simple proportion of significant findings is a poor guide to the reliability of research and that preregistered replications are an essential means to assess the reliability of money-priming effects. PMID:27077759

  13. Selection bias, vote counting, and money-priming effects: A comment on Rohrer, Pashler, and Harris (2015) and Vohs (2015).

    Science.gov (United States)

    Vadillo, Miguel A; Hardwicke, Tom E; Shanks, David R

    2016-05-01

    When a series of studies fails to replicate a well-documented effect, researchers might be tempted to use a "vote counting" approach to decide whether the effect is reliable-that is, simply comparing the number of successful and unsuccessful replications. Vohs's (2015) response to the absence of money priming effects reported by Rohrer, Pashler, and Harris (2015) provides an example of this approach. Unfortunately, vote counting is a poor strategy to assess the reliability of psychological findings because it neglects the impact of selection bias and questionable research practices. In the present comment, we show that a range of meta-analytic tools indicate irregularities in the money priming literature discussed by Rohrer et al. and Vohs, which all point to the conclusion that these effects are distorted by selection bias, reporting biases, or p-hacking. This could help to explain why money-priming effects have proven unreliable in a number of direct replication attempts in which biases have been minimized through preregistration or transparent reporting. Our major conclusion is that the simple proportion of significant findings is a poor guide to the reliability of research and that preregistered replications are an essential means to assess the reliability of money-priming effects.

  14. Interdependence of Model Systematic Biases in the Tropical Atlantic and the Tropical Pacific

    Science.gov (United States)

    Demissie, Teferi; Shonk, Jon; Toniazzo, Thomas; Woolnough, Steve Steve; Guilyardi, Eric

    2017-04-01

    The tropical climatology represented in simulations with General Circulation Models (GCMs) is affected by significant systematic biases despite the huge investments in model devlopment over the past 20 years. In this study, coupled seasonal hindcasts performed with EC-Earth and ECMWF System 4 are analyzed to understand the development of systematic biases in the tropical Atlantic and Pacific oceans. These models use similar atmosphere and ocean components (IFS and NEMO, respectively). We focus on hindcasts initialized in February and May. We discuss possible mechanisms for the evolution and origin of rapidly developing systematic biases over the tropical Atlantic during boreal spring. In addition, we look for evidence of the interrelation of systematic biases in the Atlantic and Pacific, and investigate if the errors in one ocean basin affect those in the other. We perform an upper-atmosphere wave analysis by Fourier filtering for certain ranges of temporal frequencies and zonal wavenumbers. Our results identicate common systematic biases in EC-Earth and System 4 purely attributable to the atmosphere component. Biases develop in the Atlantic basin independently of external influences, while a possible effect of such biases on the eastern Pacific is found.

  15. Iterative build OMIT maps: Map improvement by iterative model-building and refinement without model bias

    Energy Technology Data Exchange (ETDEWEB)

    Los Alamos National Laboratory, Mailstop M888, Los Alamos, NM 87545, USA; Lawrence Berkeley National Laboratory, One Cyclotron Road, Building 64R0121, Berkeley, CA 94720, USA; Department of Haematology, University of Cambridge, Cambridge CB2 0XY, England; Terwilliger, Thomas; Terwilliger, T.C.; Grosse-Kunstleve, Ralf Wilhelm; Afonine, P.V.; Moriarty, N.W.; Zwart, P.H.; Hung, L.-W.; Read, R.J.; Adams, P.D.

    2008-02-12

    A procedure for carrying out iterative model-building, density modification and refinement is presented in which the density in an OMIT region is essentially unbiased by an atomic model. Density from a set of overlapping OMIT regions can be combined to create a composite 'Iterative-Build' OMIT map that is everywhere unbiased by an atomic model but also everywhere benefiting from the model-based information present elsewhere in the unit cell. The procedure may have applications in the validation of specific features in atomic models as well as in overall model validation. The procedure is demonstrated with a molecular replacement structure and with an experimentally-phased structure, and a variation on the method is demonstrated by removing model bias from a structure from the Protein Data Bank.

  16. Mass modeling of galaxy clusters: quantifying hydrostatic bias and contribution from non-thermal pressure

    CERN Document Server

    Martizzi, Davide

    2016-01-01

    Galaxy cluster mass determinations achieved using X-ray and Sunyaev-Zeldovich data combined with the assumption of hydrostatic equilibrium are generally biased. The bias exists for two main reasons: non-thermal pressure forces are expected to contribute to the overall pressure balance and deviations from spherical symmetry and hydrostatic equilibrium can be present. In this paper, we use a sample of zoom-in hydrodynamical simulations of galaxy clusters to measure the magnitude of hydrostatic bias and the contribution from turbulence to the total pressure. We propose a new empirical model for turbulent pressure based on our simulations that can be applied to observations. We show that our model can be successfully applied to remove most of the bias related to neglection of turbulent pressure, which is usually not included in hydrostatic cluster mass profile reconstructions. The use of this model may significantly improve the calibration of cluster scaling relations that are a key tool for cluster cosmology.

  17. Influence of Referral Pathway on Ebola Virus Disease Case-Fatality Rate and Effect of Survival Selection Bias

    Science.gov (United States)

    Damkjær, Mads; Lunding, Suzanne; Dornonville de la Cour, Kenn; Young, Alyssa; Brooks, Tim; Sesay, Tom; Salam, Alex P.; Mishra, Sharmistha; Storgaard, Merete

    2017-01-01

    Case-fatality rates in Ebola treatment centers (ETCs) varied widely during the Ebola virus disease (EVD) outbreak in West Africa. We assessed the influence of referral pathway on ETC case-fatality rates with a retrospective cohort of 126 patients treated at the Mathaska ETC in Port Loko, Sierra Leone. The patients consisted of persons who had confirmed EVD when transferred to the ETC or who had been diagnosed onsite. The case-fatality rate for transferred patients was 46% versus 67% for patients diagnosed onsite (p = 0.02). The difference was mediated by Ebola viral load at diagnosis, suggesting a survival selection bias. Comparisons of case-fatality rates across ETCs and clinical management strategies should account for potential survival selection bias. PMID:28322693

  18. The threshold bias model: a mathematical model for the nomothetic approach of suicide.

    Directory of Open Access Journals (Sweden)

    Walter Sydney Dutra Folly

    Full Text Available BACKGROUND: Comparative and predictive analyses of suicide data from different countries are difficult to perform due to varying approaches and the lack of comparative parameters. METHODOLOGY/PRINCIPAL FINDINGS: A simple model (the Threshold Bias Model was tested for comparative and predictive analyses of suicide rates by age. The model comprises of a six parameter distribution that was applied to the USA suicide rates by age for the years 2001 and 2002. Posteriorly, linear extrapolations are performed of the parameter values previously obtained for these years in order to estimate the values corresponding to the year 2003. The calculated distributions agreed reasonably well with the aggregate data. The model was also used to determine the age above which suicide rates become statistically observable in USA, Brazil and Sri Lanka. CONCLUSIONS/SIGNIFICANCE: The Threshold Bias Model has considerable potential applications in demographic studies of suicide. Moreover, since the model can be used to predict the evolution of suicide rates based on information extracted from past data, it will be of great interest to suicidologists and other researchers in the field of mental health.

  19. HESS Opinions "Should we apply bias correction to global and regional climate model data?"

    Directory of Open Access Journals (Sweden)

    J. Liebert

    2012-04-01

    Full Text Available Despite considerable progress in recent years, output of both Global and Regional Circulation Models is still afflicted with biases to a degree that precludes its direct use, especially in climate change impact studies. This is well known, and to overcome this problem bias correction (BC, i.e. the correction of model output towards observations in a post processing step for its subsequent application in climate change impact studies has now become a standard procedure. In this paper we argue that bias correction, which has a considerable influence on the results of impact studies, is not a valid procedure in the way it is currently used: it impairs the advantages of Circulation Models which are based on established physical laws by altering spatiotemporal field consistency, relations among variables and by violating conservation principles. Bias correction largely neglects feedback mechanisms and it is unclear whether bias correction methods are time-invariant under climate change conditions. Applying bias correction increases agreement of Climate Model output with observations in hind casts and hence narrows the uncertainty range of simulations and predictions without, however, providing a satisfactory physical justification. This is in most cases not transparent to the end user. We argue that this masks rather than reduces uncertainty, which may lead to avoidable forejudging of end users and decision makers. We present here a brief overview of state-of-the-art bias correction methods, discuss the related assumptions and implications, draw conclusions on the validity of bias correction and propose ways to cope with biased output of Circulation Models in the short term and how to reduce the bias in the long term. The most promising strategy for improved future Global and Regional Circulation Model simulations is the increase in model resolution to the convection-permitting scale in combination with ensemble predictions based on sophisticated

  20. Biases in simulation of the rice phenology models when applied in warmer climates

    Science.gov (United States)

    Zhang, T.; Li, T.; Yang, X.; Simelton, E.

    2015-12-01

    The current model inter-comparison studies highlight the difference in projections between crop models when they are applied to warmer climates, but these studies do not provide results on how the accuracy of the models would change in these projections because the adequate observations under largely diverse growing season temperature (GST) are often unavailable. Here, we investigate the potential changes in the accuracy of rice phenology models when these models were applied to a significantly warmer climate. We collected phenology data from 775 trials with 19 cultivars in 5 Asian countries (China, India, Philippines, Bangladesh and Thailand). Each cultivar encompasses the phenology observations under diverse GST regimes. For a given rice cultivar in different trials, the GST difference reaches 2.2 to 8.2°C, which allows us to calibrate the models under lower GST and validate under higher GST (i.e., warmer climates). Four common phenology models representing major algorithms on simulations of rice phenology, and three model calibration experiments were conducted. The results suggest that the bilinear and beta models resulted in gradually increasing phenology bias (Figure) and double yield bias per percent increase in phenology bias, whereas the growing-degree-day (GDD) and exponential models maintained a comparatively constant bias when applied in warmer climates (Figure). Moreover, the bias of phenology estimated by the bilinear and beta models did not reduce with increase in GST when all data were used to calibrate models. These suggest that variations in phenology bias are primarily attributed to intrinsic properties of the respective phenology model rather than on the calibration dataset. Therefore we conclude that using the GDD and exponential models has more chances of predicting rice phenology correctly and thus, production under warmer climates, and result in effective agricultural strategic adaptation to and mitigation of climate change.

  1. Student Sorting and Bias in Value Added Estimation: Selection on Observables and Unobservables. NBER Working Paper No. 14666

    Science.gov (United States)

    Rothstein, Jesse

    2009-01-01

    Non-random assignment of students to teachers can bias value added estimates of teachers' causal effects. Rothstein (2008a, b) shows that typical value added models indicate large counter-factual effects of 5th grade teachers on students' 4th grade learning, indicating that classroom assignments are far from random. This paper quantifies the…

  2. Equilibrium dynamics of the sub-Ohmic spin-boson model under bias

    Science.gov (United States)

    Zheng, Da-Chuan; Tong, Ning-Hua

    2017-06-01

    Using the bosonic numerical renormalization group method, we studied the equilibrium dynamical correlation function C(ω) of the spin operator σ z for the biased sub-Ohmic spin-boson model. The small-ω behavior C(ω )\\propto {ω }s is found to be universal and independent of the bias ɛ and the coupling strength α (except at the quantum critical point α ={α }{{c}} and ɛ = 0). Our NRG data also show C(ω )\\propto {χ }2{ω }s for a wide range of parameters, including the biased strong coupling regime (\\varepsilon \

  3. Model Selection for Pion Photoproduction

    CERN Document Server

    Landay, J; Fernández-Ramírez, C; Hu, B; Molina, R

    2016-01-01

    Partial-wave analysis of meson and photon-induced reactions is needed to enable the comparison of many theoretical approaches to data. In both energy-dependent and independent parametrizations of partial waves, the selection of the model amplitude is crucial. Principles of the $S$-matrix are implemented to different degree in different approaches, but a many times overlooked aspect concerns the selection of undetermined coefficients and functional forms for fitting, leading to a minimal yet sufficient parametrization. We present an analysis of low-energy neutral pion photoproduction using the Least Absolute Shrinkage and Selection Operator (LASSO) in combination with criteria from information theory and $K$-fold cross validation. These methods are not yet widely known in the analysis of excited hadrons but will become relevant in the era of precision spectroscopy. The principle is first illustrated with synthetic data, then, its feasibility for real data is demonstrated by analyzing the latest available measu...

  4. Picture Novelty Influences Response Selection and Inhibition: The Role of the In-Group Bias and Task-Difficulty

    Science.gov (United States)

    Zinchenko, Artyom; Mahmud, Waich; Alam, Musrura Mefta; Kabir, Nadia; Al-Amin, Md. Mamun

    2016-01-01

    The human visual system prioritizes processing of novel information, leading to faster detection of novel stimuli. Novelty facilitates conflict resolution through the enhanced early perceptual processing. However, the role of novel information processing during the conflict-related response selection and inhibition remains unclear. Here, we used a face-gender classification version of the Simon task and manipulated task-difficulty and novelty of task-relevant information. The novel quality of stimuli was made task-irrelevant, and an in-group bias was tightly controlled by manipulation of a gender of picture stimuli. We found that the in-group bias modulated the role of novelty in executive control. Novel opposite-sex stimuli facilitated response inhibition only when the task was not demanding. By contrast, novelty enhanced response selection irrespective of the in-group factor when task-difficulty was increased. These findings support the in-group bias mechanism of visual processing, in cases when attentional resources are not limited by a demanding task. The results are further discussed along the lines of the attentional load theory and neural mechanisms of response-inhibition and locomotor activity. In conclusion, our data showed that processing of novel information may enhance executive control through facilitated response selection and inhibition. PMID:27788213

  5. Improved Model for Depth Bias Correction in Airborne LiDAR Bathymetry Systems

    Directory of Open Access Journals (Sweden)

    Jianhu Zhao

    2017-07-01

    Full Text Available Airborne LiDAR bathymetry (ALB is efficient and cost effective in obtaining shallow water topography, but often produces a low-accuracy sounding solution due to the effects of ALB measurements and ocean hydrological parameters. In bathymetry estimates, peak shifting of the green bottom return caused by pulse stretching induces depth bias, which is the largest error source in ALB depth measurements. The traditional depth bias model is often applied to reduce the depth bias, but it is insufficient when used with various ALB system parameters and ocean environments. Therefore, an accurate model that considers all of the influencing factors must be established. In this study, an improved depth bias model is developed through stepwise regression in consideration of the water depth, laser beam scanning angle, sensor height, and suspended sediment concentration. The proposed improved model and a traditional one are used in an experiment. The results show that the systematic deviation of depth bias corrected by the traditional and improved models is reduced significantly. Standard deviations of 0.086 and 0.055 m are obtained with the traditional and improved models, respectively. The accuracy of the ALB-derived depth corrected by the improved model is better than that corrected by the traditional model.

  6. Wind models for the NSTS ascent trajectory biasing for wind load alleviation

    Science.gov (United States)

    Smith, O. E.; Adelfang, S. I.; Batts, G. W.

    1990-01-01

    New concepts are presented for aerospace vehicle ascent wind profile biasing. The purpose for wind biasing the ascent trajectory is to provide ascent wind loads relief and thus decrease the probability for launch delays due to wind loads exceeding critical limits. Wind biasing trajectories to the the profile of monthly mean winds have been widely used for this purpose. The wind profile models presented give additional alternatives for wind biased trajectories. They are derived from the properties of the bivariate normal probability function using the available wind statistical parameters for the launch site. The analytical expressions are presented to permit generalizations. Specific examples are given to illustrate the procedures. The wind profile models can be used to establish the ascent trajectory steering commands to guide the vehicle through the first stage. For the National Space Transportation System (NSTS) program these steering commands are called I-loads.

  7. Entropic criterion for model selection

    Science.gov (United States)

    Tseng, Chih-Yuan

    2006-10-01

    Model or variable selection is usually achieved through ranking models according to the increasing order of preference. One of methods is applying Kullback-Leibler distance or relative entropy as a selection criterion. Yet that will raise two questions, why use this criterion and are there any other criteria. Besides, conventional approaches require a reference prior, which is usually difficult to get. Following the logic of inductive inference proposed by Caticha [Relative entropy and inductive inference, in: G. Erickson, Y. Zhai (Eds.), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP Conference Proceedings, vol. 707, 2004 (available from arXiv.org/abs/physics/0311093)], we show relative entropy to be a unique criterion, which requires no prior information and can be applied to different fields. We examine this criterion by considering a physical problem, simple fluids, and results are promising.

  8. A Selective Review of Group Selection in High Dimensional Models

    CERN Document Server

    Huang, Jian; Ma, Shuangge

    2012-01-01

    Grouping structures arise naturally in many statistical modeling problems. Several methods have been proposed for variable selection that respect grouping structure in variables. Examples include the group LASSO and several concave group selection methods. In this article, we give a selective review of group selection concerning methodological developments, theoretical properties, and computational algorithms. We pay particular attention to group selection methods involving concave penalties. We address both group selection and bi-level selection methods. We describe several applications of these methods in nonparametric additive models, semiparametric regression, seemingly unrelated regressions, genomic data analysis and genome wide association studies. We also highlight some issues that require further study.

  9. Estimating the efficacy of Alcoholics Anonymous without self-selection bias: an instrumental variables re-analysis of randomized clinical trials.

    Science.gov (United States)

    Humphreys, Keith; Blodgett, Janet C; Wagner, Todd H

    2014-11-01

    Observational studies of Alcoholics Anonymous' (AA) effectiveness are vulnerable to self-selection bias because individuals choose whether or not to attend AA. The present study, therefore, employed an innovative statistical technique to derive a selection bias-free estimate of AA's impact. Six data sets from 5 National Institutes of Health-funded randomized trials (1 with 2 independent parallel arms) of AA facilitation interventions were analyzed using instrumental variables models. Alcohol-dependent individuals in one of the data sets (n = 774) were analyzed separately from the rest of sample (n = 1,582 individuals pooled from 5 data sets) because of heterogeneity in sample parameters. Randomization itself was used as the instrumental variable. Randomization was a good instrument in both samples, effectively predicting increased AA attendance that could not be attributed to self-selection. In 5 of the 6 data sets, which were pooled for analysis, increased AA attendance that was attributable to randomization (i.e., free of self-selection bias) was effective at increasing days of abstinence at 3-month (B = 0.38, p = 0.001) and 15-month (B = 0.42, p = 0.04) follow-up. However, in the remaining data set, in which preexisting AA attendance was much higher, further increases in AA involvement caused by the randomly assigned facilitation intervention did not affect drinking outcome. For most individuals seeking help for alcohol problems, increasing AA attendance leads to short- and long-term decreases in alcohol consumption that cannot be attributed to self-selection. However, for populations with high preexisting AA involvement, further increases in AA attendance may have little impact. Copyright © 2014 by the Research Society on Alcoholism.

  10. Innovative Liner Concepts: Experiments and Impedance Modeling of Liners Including the Effect of Bias Flow

    Science.gov (United States)

    Kelly, Jeff; Betts, Juan Fernando; Fuller, Chris

    2000-01-01

    The study of normal impedance of perforated plate acoustic liners including the effect of bias flow was studied. Two impedance models were developed by modeling the internal flows of perforate orifices as infinite tubes with the inclusion of end corrections to handle finite length effects. These models assumed incompressible and compressible flows, respectively, between the far field and the perforate orifice. The incompressible model was used to predict impedance results for perforated plates with percent open areas ranging from 5% to 15%. The predicted resistance results showed better agreement with experiments for the higher percent open area samples. The agreement also tended to deteriorate as bias flow was increased. For perforated plates with percent open areas ranging from 1% to 5%, the compressible model was used to predict impedance results. The model predictions were closer to the experimental resistance results for the 2% to 3% open area samples. The predictions tended to deteriorate as bias flow was increased. The reactance results were well predicted by the models for the higher percent open area, but deteriorated as the percent open area was lowered (5%) and bias flow was increased. A fit was done on the incompressible model to the experimental database. The fit was performed using an optimization routine that found the optimal set of multiplication coefficients to the non-dimensional groups that minimized the least squares slope error between predictions and experiments. The result of the fit indicated that terms not associated with bias flow required a greater degree of correction than the terms associated with the bias flow. This model improved agreement with experiments by nearly 15% for the low percent open area (5%) samples when compared to the unfitted model. The fitted model and the unfitted model performed equally well for the higher percent open area (10% and 15%).

  11. Study of Bias in 2012-Placement Test through Rasch Model in Terms of Gender Variable

    Science.gov (United States)

    Turkan, Azmi; Cetin, Bayram

    2017-01-01

    Validity and reliability are among the most crucial characteristics of a test. One of the steps to make sure that a test is valid and reliable is to examine the bias in test items. The purpose of this study was to examine the bias in 2012 Placement Test items in terms of gender variable using Rasch Model in Turkey. The sample of this study was…

  12. System Model Bias Processing Approach for Regional Coordinated States Information Involved Filtering

    Directory of Open Access Journals (Sweden)

    Zebo Zhou

    2016-01-01

    Full Text Available In the Kalman filtering applications, the conventional dynamic model which connects the states information of two consecutive epochs by state transition matrix is usually predefined and assumed to be invariant. Aiming to improve the adaptability and accuracy of dynamic model, we propose multiple historical states involved filtering algorithm. An autoregressive model is used as the dynamic model which is subsequently combined with observation model for deriving the optimal window-recursive filter formulae in the sense of minimum mean square error principle. The corresponding test statistics characteristics of system residuals are discussed in details. The test statistics of regional predicted residuals are then constructed in a time-window for model bias testing with two hypotheses, that is, the null and alternative hypotheses. Based on the innovations test statistics, we develop a model bias processing procedure including bias detection, location identification, and state correction. Finally, the minimum detectable bias and bias-to-noise ratio are both computed for evaluating the internal and external reliability of overall system, respectively.

  13. The Properties of Model Selection when Retaining Theory Variables

    DEFF Research Database (Denmark)

    Hendry, David F.; Johansen, Søren

    Economic theories are often fitted directly to data to avoid possible model selection biases. We show that embedding a theory model that specifies the correct set of m relevant exogenous variables, x{t}, within the larger set of m+k candidate variables, (x{t},w{t}), then selection over the second...... set by their statistical significance can be undertaken without affecting the estimator distribution of the theory parameters. This strategy returns the theory-parameter estimates when the theory is correct, yet protects against the theory being under-specified because some w{t} are relevant....

  14. Mixed Model Association with Family-Biased Case-Control Ascertainment.

    Science.gov (United States)

    Hayeck, Tristan J; Loh, Po-Ru; Pollack, Samuela; Gusev, Alexander; Patterson, Nick; Zaitlen, Noah A; Price, Alkes L

    2017-01-05

    Mixed models have become the tool of choice for genetic association studies; however, standard mixed model methods may be poorly calibrated or underpowered under family sampling bias and/or case-control ascertainment. Previously, we introduced a liability threshold-based mixed model association statistic (LTMLM) to address case-control ascertainment in unrelated samples. Here, we consider family-biased case-control ascertainment, where case and control subjects are ascertained non-randomly with respect to family relatedness. Previous work has shown that this type of ascertainment can severely bias heritability estimates; we show here that it also impacts mixed model association statistics. We introduce a family-based association statistic (LT-Fam) that is robust to this problem. Similar to LTMLM, LT-Fam is computed from posterior mean liabilities (PML) under a liability threshold model; however, LT-Fam uses published narrow-sense heritability estimates to avoid the problem of biased heritability estimation, enabling correct calibration. In simulations with family-biased case-control ascertainment, LT-Fam was correctly calibrated (average χ(2) = 1.00-1.02 for null SNPs), whereas the Armitage trend test (ATT), standard mixed model association (MLM), and case-control retrospective association test (CARAT) were mis-calibrated (e.g., average χ(2) = 0.50-1.22 for MLM, 0.89-2.65 for CARAT). LT-Fam also attained higher power than other methods in some settings. In 1,259 type 2 diabetes-affected case subjects and 5,765 control subjects from the CARe cohort, downsampled to induce family-biased ascertainment, LT-Fam was correctly calibrated whereas ATT, MLM, and CARAT were again mis-calibrated. Our results highlight the importance of modeling family sampling bias in case-control datasets with related samples. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

  15. Selected soil thermal conductivity models

    Directory of Open Access Journals (Sweden)

    Rerak Monika

    2017-01-01

    Full Text Available The paper presents collected from the literature models of soil thermal conductivity. This is a very important parameter, which allows one to assess how much heat can be transferred from the underground power cables through the soil. The models are presented in table form, thus when the properties of the soil are given, it is possible to select the most accurate method of calculating its thermal conductivity. Precise determination of this parameter results in designing the cable line in such a way that it does not occur the process of cable overheating.

  16. Use of Paired Simple and Complex Models to Reduce Predictive Bias and Quantify Uncertainty

    DEFF Research Database (Denmark)

    Doherty, John; Christensen, Steen

    2011-01-01

    into the costs of model simplification, and into how some of these costs may be reduced. It then describes a methodology for paired model usage through which predictive bias of a simplified model can be detected and corrected, and postcalibration predictive uncertainty can be quantified. The methodology...

  17. Biased Type 1 Cannabinoid Receptor Signaling Influences Neuronal Viability in a Cell Culture Model of Huntington Disease.

    Science.gov (United States)

    Laprairie, Robert B; Bagher, Amina M; Kelly, Melanie E M; Denovan-Wright, Eileen M

    2016-03-01

    Huntington disease (HD) is an inherited, autosomal dominant, neurodegenerative disorder with limited treatment options. Prior to motor symptom onset or neuronal cell loss in HD, levels of the type 1 cannabinoid receptor (CB1) decrease in the basal ganglia. Decreasing CB1 levels are strongly correlated with chorea and cognitive deficit. CB1 agonists are functionally selective (biased) for divergent signaling pathways. In this study, six cannabinoids were tested for signaling bias in in vitro models of medium spiny projection neurons expressing wild-type (STHdh(Q7/Q7)) or mutant huntingtin protein (STHdh(Q111/Q111)). Signaling bias was assessed using the Black and Leff operational model. Relative activity [ΔlogR (τ/KA)] and system bias (ΔΔlogR) were calculated relative to the reference compound WIN55,212-2 for Gαi/o, Gαs, Gαq, Gβγ, and β-arrestin1 signaling following treatment with 2-arachidonoylglycerol (2-AG), anandamide (AEA), CP55,940, Δ(9)-tetrahydrocannabinol (THC), cannabidiol (CBD), and THC+CBD (1:1), and compared between wild-type and HD cells. The Emax of Gαi/o-dependent extracellular signal-regulated kinase (ERK) signaling was 50% lower in HD cells compared with wild-type cells. 2-AG and AEA displayed Gαi/o/Gβγ bias and normalized CB1 protein levels and improved cell viability, whereas CP55,940 and THC displayed β-arrestin1 bias and reduced CB1 protein levels and cell viability in HD cells. CBD was not a CB1 agonist but inhibited THC-dependent signaling (THC+CBD). Therefore, enhancing Gαi/o-biased endocannabinoid signaling may be therapeutically beneficial in HD. In contrast, cannabinoids that are β-arrestin-biased--such as THC found at high levels in modern varieties of marijuana--may be detrimental to CB1 signaling, particularly in HD where CB1 levels are already reduced.

  18. Biases in modeled surface snow BC mixing ratios in prescribed aerosol climate model runs

    OpenAIRE

    Doherty, S. J.; C. M. Bitz; M. G. Flanner

    2014-01-01

    A series of recent studies have used prescribed aerosol deposition flux fields in climate model runs to assess forcing by black carbon in snow. In these studies, the prescribed mass deposition flux of BC to surface snow is decoupled from the mass deposition flux of snow water to the surface. Here we use a series of offline calculations to show that this approach results, on average, in a~factor of about 1.5–2.5 high bias in annual-mean surface snow BC mixing ratios in three ...

  19. Selection bias in the link between child wantedness and child survival: theory and data from Matlab, Bangladesh.

    Science.gov (United States)

    Bishai, David; Razzaque, Abdur; Christiansen, Susan; Mustafa, A H M Golam; Hindin, Michelle

    2015-02-01

    We examine the potential effects of selection bias on the association between unwanted births and child mortality from 7,942 women from Matlab, Bangladesh who declared birth intentions in 1990 prior to conceiving pregnancies. We explore and test two opposing reasons for bias in the distribution of observed births. First, some women who report not wanting more children could face starvation or frailty; and if these women are infecund, the remaining unwanted births would appear more healthy. Second, some women who report not wanting more children could have social privileges in acquiring medical services, abortion, and contraceptives; and if these women avoid births, the remaining unwanted births would appear less healthy. We find (1) no overall effect of unwantedness on child survival in rural Bangladesh in the 1990s, (2) no evidence that biological processes are spuriously making the birth cohort look more healthy, and (3) some evidence that higher schooling for women who avoid unwanted births is biasing the observed sample to make unwanted births look less healthy. Efforts to understand the effect of unwantedness in data sets that do not control for complex patterns of selective birth may be misleading and require more cautious interpretation.

  20. Nonmaternal Care's Association With Mother's Parenting Sensitivity: A Case of Self-Selection Bias?

    Science.gov (United States)

    Nomaguchi, Kei M; Demaris, Alfred

    2013-06-01

    Although attachment theory posits that the use of nonmaternal care undermines quality of mothers' parenting, empirical evidence for this link is inconclusive. Using data from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (N = 1,233), the authors examined the associations between nonmaternal care characteristics and maternal sensitivity during the first 3 years of children's lives, with special attention to selection effects and moderation by resource levels. Findings from fixed-effects regression models suggested that, on average, there is little relationship between nonmaternal care characteristics and maternal sensitivity, once selection factors are held constant. Some evidence of moderation effects was found, however. Excellent-quality care is related to more sensitivity for mothers with lower family income. Poor-quality care is related to lower sensitivity for single mothers, but not partnered mothers. In sum, nonmaternal care characteristics do not seem to have as much influence on mothers' parenting as attachment theory claims.

  1. Bias correction methods for regional climate model simulations considering the distributional parametric uncertainty underlying the observations

    Science.gov (United States)

    Kim, Kue Bum; Kwon, Hyun-Han; Han, Dawei

    2015-11-01

    In this paper, we present a comparative study of bias correction methods for regional climate model simulations considering the distributional parametric uncertainty underlying the observations/models. In traditional bias correction schemes, the statistics of the simulated model outputs are adjusted to those of the observation data. However, the model output and the observation data are only one case (i.e., realization) out of many possibilities, rather than being sampled from the entire population of a certain distribution due to internal climate variability. This issue has not been considered in the bias correction schemes of the existing climate change studies. Here, three approaches are employed to explore this issue, with the intention of providing a practical tool for bias correction of daily rainfall for use in hydrologic models ((1) conventional method, (2) non-informative Bayesian method, and (3) informative Bayesian method using a Weather Generator (WG) data). The results show some plausible uncertainty ranges of precipitation after correcting for the bias of RCM precipitation. The informative Bayesian approach shows a narrower uncertainty range by approximately 25-45% than the non-informative Bayesian method after bias correction for the baseline period. This indicates that the prior distribution derived from WG may assist in reducing the uncertainty associated with parameters. The implications of our results are of great importance in hydrological impact assessments of climate change because they are related to actions for mitigation and adaptation to climate change. Since this is a proof of concept study that mainly illustrates the logic of the analysis for uncertainty-based bias correction, future research exploring the impacts of uncertainty on climate impact assessments and how to utilize uncertainty while planning mitigation and adaptation strategies is still needed.

  2. Bootstrap imputation with a disease probability model minimized bias from misclassification due to administrative database codes.

    Science.gov (United States)

    van Walraven, Carl

    2017-04-01

    Diagnostic codes used in administrative databases cause bias due to misclassification of patient disease status. It is unclear which methods minimize this bias. Serum creatinine measures were used to determine severe renal failure status in 50,074 hospitalized patients. The true prevalence of severe renal failure and its association with covariates were measured. These were compared to results for which renal failure status was determined using surrogate measures including the following: (1) diagnostic codes; (2) categorization of probability estimates of renal failure determined from a previously validated model; or (3) bootstrap methods imputation of disease status using model-derived probability estimates. Bias in estimates of severe renal failure prevalence and its association with covariates were minimal when bootstrap methods were used to impute renal failure status from model-based probability estimates. In contrast, biases were extensive when renal failure status was determined using codes or methods in which model-based condition probability was categorized. Bias due to misclassification from inaccurate diagnostic codes can be minimized using bootstrap methods to impute condition status using multivariable model-derived probability estimates. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. Introducing decorated HODs: modelling assembly bias in the galaxy-halo connection

    Science.gov (United States)

    Hearin, Andrew P.; Zentner, Andrew R.; van den Bosch, Frank C.; Campbell, Duncan; Tollerud, Erik

    2016-08-01

    The connection between galaxies and dark matter haloes is often inferred from data using probabilistic models, such as the halo occupation distribution (HOD). Conventional HOD formulations assume that only halo mass governs the galaxy-halo connection. Violations of this assumption, known as galaxy assembly bias, threaten the HOD programme. We introduce decorated HODs, a new, flexible class of models designed to account for assembly bias. Decorated HODs minimally expand the parameter space and maximize the independence between traditional and novel HOD parameters. We use decorated HODs to quantify the influence of assembly bias on clustering and lensing statistics. For SDSS-like samples, the impact of assembly bias on galaxy clustering can be as large as a factor of 2 on r ˜ 200 kpc scales and ˜15 per cent in the linear regime. Assembly bias can either enhance or diminish clustering on large scales, but generally increases clustering on scales r ≲ 1 Mpc. We performed our calculations with HALOTOOLS, an open-source, community-driven PYTHON package for studying the galaxy-halo connection (http://halotools.readthedocs.org). We conclude by describing the use of decorated HODs to treat assembly bias in otherwise conventional likelihood analyses.

  4. Statistical model selection with “Big Data”

    Directory of Open Access Journals (Sweden)

    Jurgen A. Doornik

    2015-12-01

    Full Text Available Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem, using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem, using a viable approach that resolves the computational problem of immense numbers of possible models.

  5. An Agent Memory Model Enabling Rational and Biased Reasoning

    NARCIS (Netherlands)

    Heuvelink, A.; Klein, M.C.A.; Treur, J.

    2008-01-01

    This paper presents an architecture for a memory model that facilitates versatile reasoning mechanisms over the beliefs stored in an agent's belief base. Based on an approach for belief aggregation, a model is introduced for controlling both the formation of abstract and complex beliefs and the

  6. Selection bias in dynamically-measured super-massive black hole samples: consequences for pulsar timing arrays

    CERN Document Server

    Sesana, A; Bernardi, M; Sheth, R K

    2016-01-01

    Supermassive black hole -- host galaxy relations are key to the computation of the expected gravitational wave background (GWB) in the pulsar timing array (PTA) frequency band. It has been recently pointed out that standard relations adopted in GWB computations are in fact biased-high. We show that when this selection bias is taken into account, the expected GWB in the PTA band is a factor of about three smaller than previously estimated. Compared to other scaling relations recently published in the literature, the median amplitude of the signal at $f=1$yr$^{-1}$ drops from $1.3\\times10^{-15}$ to $4\\times10^{-16}$. Although this solves any potential tension between theoretical predictions and recent PTA limits without invoking other dynamical effects (such as stalling, eccentricity or strong coupling with the galactic environment), it also makes the GWB detection more challenging.

  7. The large influence of climate model bias on terrestrial carbon cycle simulations

    Science.gov (United States)

    Ahlström, Anders; Schurgers, Guy; Smith, Benjamin

    2017-01-01

    Global vegetation models and terrestrial carbon cycle models are widely used for projecting the carbon balance of terrestrial ecosystems. Ensembles of such models show a large spread in carbon balance predictions, ranging from a large uptake to a release of carbon by the terrestrial biosphere, constituting a large uncertainty in the associated feedback to atmospheric CO2 concentrations under global climate change. Errors and biases that may contribute to such uncertainty include ecosystem model structure, parameters and forcing by climate output from general circulation models (GCMs) or the atmospheric components of Earth system models (ESMs), e.g. as prepared for use in IPCC climate change assessments. The relative importance of these contributing factors to the overall uncertainty in carbon cycle projections is not well characterised. Here we investigate the role of climate model-derived biases by forcing a single global ecosystem-carbon cycle model, with original climate outputs from 15 ESMs and GCMs from the CMIP5 ensemble. We show that variation among the resulting ensemble of present and future carbon cycle simulations propagates from biases in annual means of temperature, precipitation and incoming shortwave radiation. Future changes in carbon pools, and thus land carbon sink trends, are also affected by climate biases, although to a smaller extent than the absolute size of carbon pools. Our results suggest that climate biases could be responsible for a considerable fraction of the large uncertainties in ESM simulations of land carbon fluxes and pools, amounting to about 40% of the range reported for ESMs. We conclude that climate bias-induced uncertainties must be decreased to make accurate coupled atmosphere-carbon cycle projections.

  8. Beyond icebergs: modeling globalization as biased technical change

    OpenAIRE

    2004-01-01

    We propose a new approach to model costly international trade, which includes the standard approach, the “iceberg” transport cost, as a special case. The key idea is to make the technologies of supplying the good depend on the destination of the good. To demonstrate our approach, we extend the Ricardian model with a continuum of goods, due to Dornbusch, Fischer and Samuelson (1977), by introducing multiple factors of production and by making each industry consist of the domestic division, whi...

  9. Perceived Ideological Bias in the College Classroom and the Role of Student Reflective Thinking: A Proposed Model

    Science.gov (United States)

    Linvill, Darren L.; Mazer, Joseph P.

    2011-01-01

    This study tests a model of students' reflective thinking, perceived ideological bias among university faculty, and reactions to ideological bias in the college classroom. Participants were 187 undergraduates who completed the Reasoning About Current Issues Questionnaire and the Political Bias in the Classroom Survey. Structural equation modeling…

  10. Kinematic bias on centrality selection of jet events in pPb collisions at the LHC

    Energy Technology Data Exchange (ETDEWEB)

    Armesto, Néstor, E-mail: nestor.armesto@usc.es [Departamento de Física de Partículas and IGFAE, Universidade de Santiago de Compostela, E-15706 Santiago de Compostela, Galicia (Spain); Gülhan, Doğa Can, E-mail: dgulhan@mit.edu [Laboratory for Nuclear Science and Department of Physics, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139 (United States); Milhano, José Guilherme, E-mail: guilherme.milhano@tecnico.ulisboa.pt [CENTRA, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, P-1049-001 Lisboa (Portugal); Physics Department, Theory Unit, CERN, CH-1211 Genève 23 (Switzerland)

    2015-07-30

    Centrality selection has been observed to have a large effect on jet observables in pPb collisions at the Large Hadron Collider, stronger than that predicted by the nuclear modification of parton densities. We study to which extent simple considerations of energy–momentum conservation which link the hard process with the underlying event that provides the centrality estimator, affect jets observables in such collisions. We develop a simplistic approach that considers first the production of jets in a pp collision as described by PYTHIA. From each pp collision, the value of the energy of the parton from the proton participating in the hard scattering is extracted. Then, the underlying event is generated simulating a pPb collision through HIJING, but with the energy of the proton decreased according to the value extracted in the previous step, and both collisions are added. This model is able to capture the bulk of the centrality effect for central to semicentral collisions, for the two available sets of data: dijets from the CMS Collaboration and single jets from the ATLAS Collaboration. As expected, the model fails for peripheral collisions where very few nucleons from Pb participate.

  11. Wealth concentration in a biased asset-exchange model

    Science.gov (United States)

    Devitt-Lee, Adrian

    Economic inequality is a significant and dynamic problem throughout the world. Asset-exchange models have been used to model macroeconomic systems based on microeconomic assumptions about how agents exchange wealth in an economy. Previous studies of a certain asset-exchange model, called the Yard-Sale model, have found that trade alone promotes the condensation of wealth to a single individual in an economy [Chakraborti, 2002, Moukarzel et al., 2007, Boghosian, 2014b]. A later study found that a slight modification of the Yard-Sale model seems to allow for the coexistence of both "condensed wealth" and a normal population in an economy [Boghosian et al., 2016a]. This work formalizes the notion of wealth condensation in a macroeconomic system. This can be done by extending Schwartz's theory of distributions to allow for objects which increase at most linearly at infinity, or by considering condensed wealth to be a nonstandard phenomenon, and describing it as such. Numerical simulations indicate that this continuous description of wealth concentration is a valid approximation of wealth concentration in discrete systems with as few as 256 agents. We then study the properties of the steady-state distribution of wealth in such a system, and mention the fit of our system to the distribution of wealth in the United States in 2016.

  12. Bias and Uncertainty in Regression-Calibrated Models of Groundwater Flow in Heterogeneous Media

    DEFF Research Database (Denmark)

    Cooley, R.L.; Christensen, Steen

    2006-01-01

    of the approximate inputs is in error with respect to the same model function written in terms of β, f(β), which is assumed to be nearly exact. The difference f(β) – f(γθ*), termed model error, is spatially correlated, generates prediction biases, and causes standard confidence and prediction intervals to be too......, and for correction factors needed to adjust the sizes of confidence and prediction intervals for possible use of a diagonal weight matrix in place of the correct one. If terms expressing the degree of intrinsic nonlinearity for f(β) and f(γθ*) are small, then most of the biases are small and the correction factors...... are reduced in magnitude. Biases, correction factors, and confidence and prediction intervals were obtained for a test problem for which model error is large to test robustness of the methodology. Numerical results conform with the theoretical analysis....

  13. Model selection for pion photoproduction

    Science.gov (United States)

    Landay, J.; Döring, M.; Fernández-Ramírez, C.; Hu, B.; Molina, R.

    2017-01-01

    Partial-wave analysis of meson and photon-induced reactions is needed to enable the comparison of many theoretical approaches to data. In both energy-dependent and independent parametrizations of partial waves, the selection of the model amplitude is crucial. Principles of the S matrix are implemented to a different degree in different approaches; but a many times overlooked aspect concerns the selection of undetermined coefficients and functional forms for fitting, leading to a minimal yet sufficient parametrization. We present an analysis of low-energy neutral pion photoproduction using the least absolute shrinkage and selection operator (LASSO) in combination with criteria from information theory and K -fold cross validation. These methods are not yet widely known in the analysis of excited hadrons but will become relevant in the era of precision spectroscopy. The principle is first illustrated with synthetic data; then, its feasibility for real data is demonstrated by analyzing the latest available measurements of differential cross sections (d σ /d Ω ), photon-beam asymmetries (Σ ), and target asymmetry differential cross sections (d σT/d ≡T d σ /d Ω ) in the low-energy regime.

  14. A Diffusion Model Analysis of Decision Biases Affecting Delayed Recognition of Emotional Stimuli.

    Science.gov (United States)

    Bowen, Holly J; Spaniol, Julia; Patel, Ronak; Voss, Andreas

    2016-01-01

    Previous empirical work suggests that emotion can influence accuracy and cognitive biases underlying recognition memory, depending on the experimental conditions. The current study examines the effects of arousal and valence on delayed recognition memory using the diffusion model, which allows the separation of two decision biases thought to underlie memory: response bias and memory bias. Memory bias has not been given much attention in the literature but can provide insight into the retrieval dynamics of emotion modulated memory. Participants viewed emotional pictorial stimuli; half were given a recognition test 1-day later and the other half 7-days later. Analyses revealed that emotional valence generally evokes liberal responding, whereas high arousal evokes liberal responding only at a short retention interval. The memory bias analyses indicated that participants experienced greater familiarity with high-arousal compared to low-arousal items and this pattern became more pronounced as study-test lag increased; positive items evoke greater familiarity compared to negative and this pattern remained stable across retention interval. The findings provide insight into the separate contributions of valence and arousal to the cognitive mechanisms underlying delayed emotion modulated memory.

  15. A clinical trial alert tool to recruit large patient samples and assess selection bias in general practice research

    Directory of Open Access Journals (Sweden)

    Scheidt-Nave Christa

    2011-02-01

    Full Text Available Abstract Background Many research projects in general practice face problems when recruiting patients, often resulting in low recruitment rates and an unknown selection bias, thus limiting their value for health services research. The objective of the study is to evaluate the recruitment performance of the practice staff in 25 participating general practices when using a clinical trial alert (CTA tool. Methods The CTA tool was developed for an osteoporosis survey of patients at risk for osteoporosis and fractures. The tool used data from electronic patient records (EPRs to automatically identify the population at risk (net sample, to apply eligibility criteria, to contact eligible patients, to enrol and survey at least 200 patients per practice. The effects of the CTA intervention were evaluated on the basis of recruitment efficiency and selection bias. Results The CTA tool identified a net sample of 16,067 patients (range 162 to 1,316 per practice, of which the practice staff reviewed 5,161 (32% cases for eligibility. They excluded 3,248 patients and contacted 1,913 patients. Of these, 1,526 patients (range 4 to 202 per practice were successfully enrolled and surveyed. This made up 9% of the net sample and 80% of the patients contacted. Men and older patients were underrepresented in the study population. Conclusion Although the recruitment target was unreachable for most practices, the practice staff in the participating practices used the CTA tool successfully to identify, document and survey a large patient sample. The tool also helped the research team to precisely determine a slight selection bias.

  16. Physical Ability-Task Performance Models: Assessing the Risk of Omitted Variable Bias

    Science.gov (United States)

    2008-09-15

    Variable Bias References Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step...Boomsma, A. (2000). Reporting analysis of covariance structures. Structural Equation Modeling , 7(3), 461-483. Browne, M. W., & Cudeck, R. (1993...Company. Fan, X., Thompson, B., & Wang, L. (1999). Effects of sample size, estimation methods, and model specification on structural equation modeling fit

  17. Bias correction of temperature and precipitation data for regional climate model application to the Rhine basin

    Science.gov (United States)

    Terink, W.; Hurkmans, R. T. W. L.; Uijlenhoet, R.; Torfs, P. J. J. F.; Warmerdam, P. M. M.

    2009-04-01

    The Hydrology and Quantitative Water Management group of Wageningen University is involved in the EU research project NeWater. The objective of this project is to develop tools which provide medium range hydrological predictions by coupling catchment-scale water balance models and ensembles from mesoscale climate models. The catchment-scale distributed hydrological model used in this study is the Variable Infiltration Capacity (VIC) model. This hydrological model in combination with an ensemble from the climate model ECHAM5 (developed by Max Plank Institute für Meteorologie (MPI-M), Hamburg) is being used to evaluate the effects of climate change on the hydrological regime of the Rhine basin and to assess the uncertainties involved in the ensembles from the climate model used in this study. Three future scenarios (2001-2100) are used in this study, which are downscaled ECHAM5 runs which were forced by the IPCC carbon emission scenarios B1, A1B and A2. A downscaled ECHAM5 "Climate of the 20th Century" run (1951-2000) is used as the reference climate. Downscaled ERA15 data is used to calibrate the VIC model. Downscaling of both the ECHAM5 and ERA15 model was carried out with the regional climate model REMO at MPI-M to a resolution of 0.088 degrees. The assessment of uncertainties involved in the climate model ensembles is performed by comparing the model (ECHAM5-REMO and ERA15-REMO) ensemble precipitation and temperature data with observations. This resulted in the detection of a bias in both the downscaled reference climate data and downscaled ERA15 data. A bias-correction has been applied to both the downscaled ERA15 data and the reference climate data. This bias-correction corrects for the mean and coefficient of variation for precipitation and the mean and standard deviation for temperature. The results of the applied bias-correction are analyzed spatially and temporally. Despite the fact that the bias-correction only uses two parameters, the coefficient of

  18. Selection BIAS: Stereotypes and discrimination related to having a history of cancer.

    Science.gov (United States)

    Martinez, Larry R; White, Craig D; Shapiro, Jenessa R; Hebl, Michelle R

    2016-01-01

    Although great strides have been made in increasing equality and inclusion in organizations, a number of stigmatized groups are overlooked by diversity initiatives, including people with a history of cancer. To examine the workplace experiences of these individuals in selection contexts, we conducted 3 complementary studies that assess the extent to which cancer is disclosed, the stereotypes associated with cancer in the workplace, and discrimination resulting from these stereotypes. In a pilot study, we surveyed 196 individuals with a history of cancer (across 2 samples) about their workplace disclosure habits. In Study 1, we explored stereotypes related to employees with a history of cancer using the framework outlined by the stereotype content model. In Study 2, we used a field study to assess the experiences of job applicants who indicated they were "cancer survivors" (vs. not) with both formal and interpersonal forms of discrimination. This research shows that cancer is disclosed at relatively high rates (pilot study), those with a history of cancer are stereotyped as being higher in warmth than competence (Study 1), and the stereotypes associated with those who have had cancer result in actual discrimination toward them (Study 2). We discuss the theory behind these findings and aim to inform both science and practice with respect to this growing workplace population.

  19. On the Tropical Atlantic SST warm bias in the Kiel Climate model

    Energy Technology Data Exchange (ETDEWEB)

    Wahl, Sebastian; Latif, Mojib; Park, Wonsun; Keenlyside, Noel [Leibniz Institute of Marine Sciences, Kiel (Germany)

    2011-03-15

    Most of the current coupled general circulation models show a strong warm bias in the eastern Tropical Atlantic. In this paper, various sensitivity experiments with the Kiel Climate Model (KCM) are described. A largely reduced warm bias and an improved seasonal cycle in the eastern Tropical Atlantic are simulated in one particular version of KCM. By comparing the stable and well-tested standard version with the sensitivity experiments and the modified version, mechanisms contributing to the reduction of the eastern Atlantic warm bias are identified and compared to what has been proposed in literature. The error in the spring and early summer zonal winds associated with erroneous zonal precipitation seems to be the key mechanism, and large-scale coupled ocean-atmosphere feedbacks play an important role in reducing the warm bias. Improved winds in boreal spring cause the summer cooling in the eastern Tropical Atlantic (ETA) via shoaling of the thermocline and increased upwelling, and hence reduced sea surface temperature (SST). Reduced SSTs in the summer suppress convection and favor the development of low-level cloud cover in the ETA region. Subsurface ocean structure is shown to be improved, and potentially influences the development of the bias. The strong warm bias along the southeastern coastline is related to underestimation of low-level cloud cover and the associated overestimation of surface shortwave radiation in the same region. Therefore, in addition to the primarily wind forced response at the equator both changes in surface shortwave radiation and outgoing longwave radiation contribute significantly to reduction of the warm bias from summer to fall. (orig.)

  20. Climate model biases in seasonally of continental water storage revealed by satellite gravimetry

    Science.gov (United States)

    Swenson, S.C.; Milly, P.C.D.

    2006-01-01

    Satellite gravimetric observations of monthly changes in continental water storage are compared with outputs from five climate models. All models qualitatively reproduce the global pattern of annual storage amplitude, and the seasonal cycle of global average storage is reproduced well, consistent with earlier studies. However, global average agreements mask systematic model biases in low latitudes. Seasonal extrema of low-latitude, hemispheric storage generally occur too early in the models, and model-specific errors in amplitude of the low-latitude annual variations are substantial. These errors are potentially explicable in terms of neglected or suboptimally parameterized water stores in the land models and precipitation biases in the climate models. Copyright 2006 by the American Geophysical Union.

  1. Kinetic models of collective decision-making in the presence of equality bias

    CERN Document Server

    Pareschi, Lorenzo; Zanella, Mattia

    2016-01-01

    We introduce and discuss kinetic models describing the influence of the competence in the evolution of decisions in a multi-agent system. The original exchange mechanism, which is based on the human tendency to compromise and change opinion through self-thinking, is here modified to include the role of the agents' competence. In particular, we take into account the agents' tendency to behave in the same way as if they were as good, or as bad, as their partner: the so-called equality bias. This occurred in a situation where a wide gap separated the competence of group members. We discuss the main properties of the kinetic models and numerically investigate some examples of collective decision under the influence of the equality bias. The results confirm that the equality bias leads the group to suboptimal decisions.

  2. Kinetic models of collective decision-making in the presence of equality bias

    Science.gov (United States)

    Pareschi, Lorenzo; Vellucci, Pierluigi; Zanella, Mattia

    2017-02-01

    We introduce and discuss kinetic models describing the influence of the competence in the evolution of decisions in a multi-agent system. The original exchange mechanism, which is based on the human tendency to compromise and change opinion through self-thinking, is here modified to include the role of the agents' competence. In particular, we take into account the agents' tendency to behave in the same way as if they were as good, or as bad, as their partner: the so-called equality bias. This occurred in a situation where a wide gap separated the competence of group members. We discuss the main properties of the kinetic models and numerically investigate some examples of collective decision under the influence of the equality bias. The results confirm that the equality bias leads the group to suboptimal decisions.

  3. Warm Bias and Parameterization of Boundary Upwelling in Ocean Models

    Energy Technology Data Exchange (ETDEWEB)

    Cessi, Paola; Wolfe, Christopher

    2012-11-06

    It has been demonstrated that Eastern Boundary Currents (EBC) are a baroclinic intensification of the interior circulation of the ocean due to the emergence of mesoscale eddies in response to the sharp buoyancy gradients driven by the wind-stress and the thermal surface forcing. The eddies accomplish the heat and salt transport necessary to insure that the subsurface flow is adiabatic, compensating for the heat and salt transport effected by the mean currents. The EBC thus generated occurs on a cross-shore scale of order 20-100 km, and thus this scale needs to be resolved in climate models in order to capture the meridional transport by the EBC. Our result indicate that changes in the near shore currents on the oceanic eastern boundaries are linked not just to local forcing, such as coastal changes in the winds, but depend on the basin-wide circulation as well.

  4. Neurodynamics of biased competition and cooperation for attention: a model with spiking neurons.

    Science.gov (United States)

    Deco, Gustavo; Rolls, Edmund T

    2005-07-01

    Recent neurophysiological experiments have led to a promising "biased competition hypothesis" of the neural basis of attention. According to this hypothesis, attention appears as a sometimes nonlinear property that results from a top-down biasing effect that influences the competitive and cooperative interactions that work both within cortical areas and between cortical areas. In this paper we describe a detailed dynamical analysis of the synaptic and neuronal spiking mechanisms underlying biased competition. We perform a detailed analysis of the dynamical capabilities of the system by exploring the stationary attractors in the parameter space by a mean-field reduction consistent with the underlying synaptic and spiking dynamics. The nonstationary dynamical behavior, as measured in neuronal recording experiments, is studied by an integrate-and-fire model with realistic dynamics. This elucidates the role of cooperation and competition in the dynamics of biased competition and shows why feedback connections between cortical areas need optimally to be weaker by a factor of about 2.5 than the feedforward connections in an attentional network. We modeled the interaction between top-down attention and bottom-up stimulus contrast effects found neurophysiologically and showed that top-down attentional effects can be explained by external attention inputs biasing neurons to move to different parts of their nonlinear activation functions. Further, it is shown that, although NMDA nonlinear effects may be useful in attention, they are not necessary, with nonlinear effects (which may appear multiplicative) being produced in the way just described.

  5. The Large Scale Bias of Dark Matter Halos: Numerical Calibration and Model Tests

    CERN Document Server

    Tinker, Jeremy L; Kravtsov, Andrey V; Klypin, Anatoly; Warren, Michael S; Yepes, Gustavo; Gottlober, Stefan

    2010-01-01

    We measure the clustering of dark matter halos in a large set of collisionless cosmological simulations of the flat LCDM cosmology. Halos are identified using the spherical overdensity algorithm, which finds the mass around isolated peaks in the density field such that the mean density is Delta times the background. We calibrate fitting functions for the large scale bias that are adaptable to any value of Delta we examine. We find a ~6% scatter about our best fit bias relation. Our fitting functions couple to the halo mass functions of Tinker et. al. (2008) such that bias of all dark matter is normalized to unity. We demonstrate that the bias of massive, rare halos is higher than that predicted in the modified ellipsoidal collapse model of Sheth, Mo, & Tormen (2001), and approaches the predictions of the spherical collapse model for the rarest halos. Halo bias results based on friends-of-friends halos identified with linking length 0.2 are systematically lower than for halos with the canonical Delta=200 o...

  6. HESS Opinions "Should we apply bias correction to global and regional climate model data?"

    Directory of Open Access Journals (Sweden)

    J. Liebert

    2012-09-01

    Full Text Available Despite considerable progress in recent years, output of both global and regional circulation models is still afflicted with biases to a degree that precludes its direct use, especially in climate change impact studies. This is well known, and to overcome this problem, bias correction (BC; i.e. the correction of model output towards observations in a post-processing step has now become a standard procedure in climate change impact studies. In this paper we argue that BC is currently often used in an invalid way: it is added to the GCM/RCM model chain without sufficient proof that the consistency of the latter (i.e. the agreement between model dynamics/model output and our judgement as well as the generality of its applicability increases. BC methods often impair the advantages of circulation models by altering spatiotemporal field consistency, relations among variables and by violating conservation principles. Currently used BC methods largely neglect feedback mechanisms, and it is unclear whether they are time-invariant under climate change conditions. Applying BC increases agreement of climate model output with observations in hindcasts and hence narrows the uncertainty range of simulations and predictions without, however, providing a satisfactory physical justification. This is in most cases not transparent to the end user. We argue that this hides rather than reduces uncertainty, which may lead to avoidable forejudging of end users and decision makers. We present here a brief overview of state-of-the-art bias correction methods, discuss the related assumptions and implications, draw conclusions on the validity of bias correction and propose ways to cope with biased output of circulation models in the short term and how to reduce the bias in the long term. The most promising strategy for improved future global and regional circulation model simulations is the increase in model resolution to the convection-permitting scale in combination with

  7. A model-based analysis of GC-biased gene conversion in the human and chimpanzee genomes.

    Directory of Open Access Journals (Sweden)

    John A Capra

    Full Text Available GC-biased gene conversion (gBGC is a recombination-associated process that favors the fixation of G/C alleles over A/T alleles. In mammals, gBGC is hypothesized to contribute to variation in GC content, rapidly evolving sequences, and the fixation of deleterious mutations, but its prevalence and general functional consequences remain poorly understood. gBGC is difficult to incorporate into models of molecular evolution and so far has primarily been studied using summary statistics from genomic comparisons. Here, we introduce a new probabilistic model that captures the joint effects of natural selection and gBGC on nucleotide substitution patterns, while allowing for correlations along the genome in these effects. We implemented our model in a computer program, called phastBias, that can accurately detect gBGC tracts about 1 kilobase or longer in simulated sequence alignments. When applied to real primate genome sequences, phastBias predicts gBGC tracts that cover roughly 0.3% of the human and chimpanzee genomes and account for 1.2% of human-chimpanzee nucleotide differences. These tracts fall in clusters, particularly in subtelomeric regions; they are enriched for recombination hotspots and fast-evolving sequences; and they display an ongoing fixation preference for G and C alleles. They are also significantly enriched for disease-associated polymorphisms, suggesting that they contribute to the fixation of deleterious alleles. The gBGC tracts provide a unique window into historical recombination processes along the human and chimpanzee lineages. They supply additional evidence of long-term conservation of megabase-scale recombination rates accompanied by rapid turnover of hotspots. Together, these findings shed new light on the evolutionary, functional, and disease implications of gBGC. The phastBias program and our predicted tracts are freely available.

  8. Oceanic Climatology in the Coupled Model FGOALS-g2:Improvements and Biases

    Institute of Scientific and Technical Information of China (English)

    LIN Pengfei; YU Yongqiang; LIU Hailong

    2013-01-01

    The present study examines simulated oceanic climatology in the Flexible Global Ocean-Atmosphere-Land System model,Grid-point Version 2 (FGOALS-g2) forced by historical external forcing data.The oceanic temperatures and circulations in FGOALS-g2 were found to be comparable to those observed,and substantially improved compared to those simulated by the previous version,FGOALS-g1.0.Compared with simulations by FGOALS-g1.0,the shallow mixed layer depths were better captured in the eastern Atlantic and Pacific Ocean in FGOALS-g2.In the high latitudes of the Northern Hemisphere,the cold biases of SST were about 1℃ 5℃ smaller in FGOALS-g2.The associated sea ice distributions and their seasonal cycles were more realistic in FGOALS-g2.The pattern of Atlantic Meridional Overturning Circulation (AMOC) was better simulated in FGOALS-g2,although its magnitude was larger than that found in observed data.The simulated Antarctic Circumpolar Current (ACC) transport was about 140 Sv through the Drake Passage,which is close to that observed.Moreover,Antarctic Intermediate Water (AAIW) was better captured in FGOALS-g2.However,large SST cold biases (>3℃) were still found to exist around major western boundary currents and in the Barents Sea,which can be explained by excessively strong oceanic cold advection and unresolved processes owing to the coarse resolution.In the Indo-Pacific warm pool,the cold biases were partly related to the excessive loss of heat from the ocean.Along the eastern coast in the Atlantic and Pacific Oceans,the warm biases were due to overestimation of shortwave radiation.In the Indian Ocean and Southern Ocean,the surface fresh biases were mainly due to the biases of precipitation.In the tropical Pacific Ocean,the surface fresh biases (>2 psu) were mainly caused by excessive precipitation and oceanic advection.In theIndo-Pacific Ocean,fresh biases were also found to dominate in the upper 1000 m,except in the northeastern Indian Ocean.There were warm and

  9. Apparatus and method for selective area deposition of thin films on electrically biased substrates

    Science.gov (United States)

    Zuhr, Raymond A.; Haynes, Tony E.; Golanski, Andrzej

    1994-01-01

    An ion beam deposition process for selective area deposition on a polarized substrate uses a potential applied to the substrate which allows the ionized particles to reach into selected areas for film deposition. Areas of the substrate to be left uncoated are held at a potential that repells the ionized particles.

  10. Selective attention, memory bias, and symptom perception in idiopathic environmental intolerance and somatoform disorders.

    Science.gov (United States)

    Witthöft, Michael; Gerlach, Alexander L; Bailer, Josef

    2006-08-01

    Idiopathic environmental intolerance (IEI) refers to a polysymptomatic condition, similar to somatoform disorders. Various processes seem to contribute to its yet unknown etiology. Attention and memory for somatic symptom and IEI-trigger words was compared among participants with IEI (n = 54), somatoform disorders (SFD; n = 44) and control participants (n = 54). Groups did not differ in a dot-probe task. However, in an emotional Stroop task, attention was biased in IEI and SFD groups toward symptom words but not toward IEI-trigger words. Only the IEI group rated trigger words as more unpleasant and more arousing, and participants remembered them better in a recognition task. These implicit and explicit cognitive abnormalities in IEI and SFD may maintain processes of somatosensory amplification.

  11. Scientific Research or Advocacy? Emotive Labels and Selection Bias Confound Survey Results

    Directory of Open Access Journals (Sweden)

    Jerome K. Vanclay

    2000-07-01

    Full Text Available Robert Costanza presents four compelling visions of the future, but the language he uses to describe them is emotive and value-laden and may bias the survey results. The descriptions and analogies used may evoke responses from the survey participants that reveal more about their reactions to the description than their attitudes toward a given scenario. It is hypothesized that the use of more neutral language may lead to more support for the scenario involving "self-limited consumption with ample resources" that Costanza calls "Big Government." If this hypothesis is correct, then the skeptic's policy that Costanza appears to prefer has the additional advantage of always leading to the favored outcome, regardless of the state of the world.

  12. Do Methodological Choices in Environmental Modeling Bias Rebound Effects? : A Case Study on Electric Cars

    NARCIS (Netherlands)

    Font Vivanco, D.; Tukker, A.; Kemp, R.

    2016-01-01

    Improvements in resource efficiency often underperform because of rebound effects. Calculations of the size of rebound effects are subject to various types of bias, among which methodological choices have received particular attention. Modellers have primarily focused on choices related to changes i

  13. Rotational diffusion model of orientational enhancement in AC field biased photorefractive polymers

    DEFF Research Database (Denmark)

    Pedersen, T.G.; Jespersen, K.G.; Johansen, P.M.

    2001-01-01

    The response of photorefractive (PR) polymers subject to AC field biasing is analyzed within the space-charge field formalism. The frequency dependence of orientational enhancement is taken into account using a rotational diffusion model for the angular distribution of chromophores. The possibility...

  14. Assessing the "Rothstein Falsification Test": Does It Really Show Teacher Value-Added Models Are Biased?

    Science.gov (United States)

    Goldhaber, Dan; Chaplin, Duncan Dunbar

    2015-01-01

    In an influential paper, Jesse Rothstein (2010) shows that standard value-added models (VAMs) suggest implausible and large future teacher effects on past student achievement. This is the basis of a falsification test that "appears" to indicate bias in typical VAM estimates of teacher contributions to student learning on standardized…

  15. Application of Photocurrent Model on Polymer Solar Cells Under Forward Bias Stress

    DEFF Research Database (Denmark)

    Rizzo, Antonio; Torto, Lorenzo; Wrachien, Nicola

    2017-01-01

    We performed a constant current stress at forward bias on organic heterojunction solar cells. We measured current voltage curves in both dark and light at each stress step to calculate the photocurrent. An existing model applied to photocurrent experimental data allows the estimation of several...

  16. Tie-breaker: Using language models to quantify gender bias in sports journalism

    CERN Document Server

    Fu, Liye; Lee, Lillian

    2016-01-01

    Gender bias is an increasingly important issue in sports journalism. In this work, we propose a language-model-based approach to quantify differences in questions posed to female vs. male athletes, and apply it to tennis post-match interviews. We find that journalists ask male players questions that are generally more focused on the game when compared with the questions they ask their female counterparts. We also provide a fine-grained analysis of the extent to which the salience of this bias depends on various factors, such as question type, game outcome or player rank.

  17. Selective Maintenance Model Considering Time Uncertainty

    OpenAIRE

    Le Chen; Zhengping Shu; Yuan Li; Xuezhi Lv

    2012-01-01

    This study proposes a selective maintenance model for weapon system during mission interval. First, it gives relevant definitions and operational process of material support system. Then, it introduces current research on selective maintenance modeling. Finally, it establishes numerical model for selecting corrective and preventive maintenance tasks, considering time uncertainty brought by unpredictability of maintenance procedure, indetermination of downtime for spares and difference of skil...

  18. Bias and uncertainty in regression-calibrated models of groundwater flow in heterogeneous media

    Science.gov (United States)

    Cooley, R.L.; Christensen, S.

    2006-01-01

    Groundwater models need to account for detailed but generally unknown spatial variability (heterogeneity) of the hydrogeologic model inputs. To address this problem we replace the large, m-dimensional stochastic vector ?? that reflects both small and large scales of heterogeneity in the inputs by a lumped or smoothed m-dimensional approximation ????*, where ?? is an interpolation matrix and ??* is a stochastic vector of parameters. Vector ??* has small enough dimension to allow its estimation with the available data. The consequence of the replacement is that model function f(????*) written in terms of the approximate inputs is in error with respect to the same model function written in terms of ??, ??,f(??), which is assumed to be nearly exact. The difference f(??) - f(????*), termed model error, is spatially correlated, generates prediction biases, and causes standard confidence and prediction intervals to be too small. Model error is accounted for in the weighted nonlinear regression methodology developed to estimate ??* and assess model uncertainties by incorporating the second-moment matrix of the model errors into the weight matrix. Techniques developed by statisticians to analyze classical nonlinear regression methods are extended to analyze the revised method. The analysis develops analytical expressions for bias terms reflecting the interaction of model nonlinearity and model error, for correction factors needed to adjust the sizes of confidence and prediction intervals for this interaction, and for correction factors needed to adjust the sizes of confidence and prediction intervals for possible use of a diagonal weight matrix in place of the correct one. If terms expressing the degree of intrinsic nonlinearity for f(??) and f(????*) are small, then most of the biases are small and the correction factors are reduced in magnitude. Biases, correction factors, and confidence and prediction intervals were obtained for a test problem for which model error is

  19. GPS satellite and receiver instrumental biases estimation using least squares method for accurate ionosphere modelling

    Indian Academy of Sciences (India)

    G Sasibhushana Rao

    2007-10-01

    The positional accuracy of the Global Positioning System (GPS)is limited due to several error sources.The major error is ionosphere.By augmenting the GPS,the Category I (CAT I)Precision Approach (PA)requirements can be achieved.The Space-Based Augmentation System (SBAS)in India is known as GPS Aided Geo Augmented Navigation (GAGAN).One of the prominent errors in GAGAN that limits the positional accuracy is instrumental biases.Calibration of these biases is particularly important in achieving the CAT I PA landings.In this paper,a new algorithm is proposed to estimate the instrumental biases by modelling the TEC using 4th order polynomial.The algorithm uses values corresponding to a single station for one month period and the results confirm the validity of the algorithm.The experimental results indicate that the estimation precision of the satellite-plus-receiver instrumental bias is of the order of ± 0.17 nsec.The observed mean bias error is of the order − 3.638 nsec and − 4.71 nsec for satellite 1 and 31 respectively.It is found that results are consistent over the period.

  20. Pharmacological reversal of cognitive bias in the chick anxiety-depression model.

    Science.gov (United States)

    Hymel, Kristen A; Sufka, Kenneth J

    2012-01-01

    Cognitive bias presents in clinical populations where anxious individuals adopt a more pessimistic interpretation of ambiguous aversive stimuli and depressed individuals adopt both a more pessimistic interpretation of ambiguous aversive stimuli and a less optimistic interpretation of ambiguous appetitive stimuli. These biases have been reversed by anxiolytics and antidepressants. In the current study, chicks exposed to an isolation stressor of 5-min to induce an anxiety-like state or 60-min to induce a depressive-like state were tested in a straight alley maze to a series of morphed ambiguous appetitive (chick silhouette) to aversive (owl silhouette) cues. Chicks in the depression-like state displayed more pessimistic-like and less optimistic-like approach behavior to ambiguous aversive and appetitive cues, respectively. Both forms of cognitive bias were reversed by 15.0 mg/kg imipramine. Chicks in anxiety-like state displayed more pessimistic-like approach behavior under the ambiguous aversive stimulus cues. However, 0.10 mg/kg clonidine produced modest sedation and thus, was ineffective at reversing this bias. The observation that cognitive biases of more pessimism and less optimism can be reversed in the depression-like phase by imipramine adds to the validity of the chick anxiety-depression model as a neuropsychiatric simulation. This article is part of a Special Issue entitled 'Anxiety and Depression'.

  1. A Class of Biased Estimators Besed on SVD in Linear Model

    Institute of Scientific and Technical Information of China (English)

    GUIQing-ming; DUANQing-tang; GUOJian-feng; ZHOUQiao-yun

    2003-01-01

    In this paper,a class of new biased estimators for linear model is proposed by modifying the singular values of the design matrix so as to directly overcome the difficulties caused by ill-conditioning in the design matrix.Some important properties of these new estimators are obtained.By appropriate choices of the biased parameters,we construct many useful and important estimators.An application of these new estimators in three-dimensional position adjustment by distance in a spatial coordiate surveys is given.The results show that the proposed biased estimators can effectively overcome ill-conditioning and their numerical stabilities are preferable to ordinary least square estimation.

  2. Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model.

    Science.gov (United States)

    Hijmans, Robert J

    2012-03-01

    Species distribution models are usually evaluated with cross-validation. In this procedure evaluation statistics are computed from model predictions for sites of presence and absence that were not used to train (fit) the model. Using data for 226 species, from six regions, and two species distribution modeling algorithms (Bioclim and MaxEnt), I show that this procedure is highly sensitive to "spatial sorting bias": the difference between the geographic distance from testing-presence to training-presence sites and the geographic distance from testing-absence (or testing-background) to training-presence sites. I propose the use of pairwise distance sampling to remove this bias, and the use of a null model that only considers the geographic distance to training sites to calibrate cross-validation results for remaining bias. Model evaluation results (AUC) were strongly inflated: the null model performed better than MaxEnt for 45% and better than Bioclim for 67% of the species. Spatial sorting bias and area under the receiver-operator curve (AUC) values increased when using partitioned presence data and random-absence data instead of independently obtained presence-absence testing data from systematic surveys. Pairwise distance sampling removed spatial sorting bias, yielding null models with an AUC close to 0.5, such that AUC was the same as null model calibrated AUC (cAUC). This adjustment strongly decreased AUC values and changed the ranking among species. Cross-validation results for different species are only comparable after removal of spatial sorting bias and/or calibration with an appropriate null model.

  3. Model selection and inference a practical information-theoretic approach

    CERN Document Server

    Burnham, Kenneth P

    1998-01-01

    This book is unique in that it covers the philosophy of model-based data analysis and an omnibus strategy for the analysis of empirical data The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data Kullback-Leibler information represents a fundamental quantity in science and is Hirotugu Akaike's basis for model selection The maximized log-likelihood function can be bias-corrected to provide an estimate of expected, relative Kullback-Leibler information This leads to Akaike's Information Criterion (AIC) and various extensions and these are relatively simple and easy to use in practice, but little taught in statistics classes and far less understood in the applied sciences than should be the case The information theoretic approaches provide a unified and rigorous theory, an extension of likelihood theory, an important application of information theory, and are ...

  4. Minimum Bias Trigger in ATLAS

    CERN Document Server

    Kwee, R E; The ATLAS collaboration

    2010-01-01

    Since the restart of the LHC in November 2009, ATLAS has collected inelastic pp-collisions to perform first measurements on charged particle densities. These measurements will help to constrain various models describing phenomenologically soft parton interactions. Understanding the trigger efficiencies for different event types are therefore crucial to minimize any possible bias in the event selection. ATLAS uses two main minimum bias triggers, featuring complementary detector components and trigger levels. While a hardware based first trigger level situated in the forward regions with 2.09 < |eta| < 3.8 has been proven to select pp-collisions very efficiently, the Inner Detector based minimum bias trigger uses a random seed on filled bunches and central tracking detectors for the event selection. Both triggers were essential for the analysis of kinematic spectra of charged particles. Their performance and trigger efficiency measurements as well as studies on possible bias sources will be presen...

  5. Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection.

    Science.gov (United States)

    Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Fotouhi, Farshad

    2014-11-01

    Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings before applying more complex approaches. Single Variable Classifier (SVC) ranking is a feature ranking based on the predictive performance of a classifier built using only a single feature. While benefiting from capabilities of classifiers, this ranking method is not as computationally intensive as wrappers. In this paper, we report the results of an extensive study on the bias and stability of such feature ranking method. We study whether the classifiers influence the SVC rankings or the discriminative power of features themselves has a dominant impact on the final rankings. We show the common intuition of using the same classifier for feature ranking and final classification does not always result in the best prediction performance. We then study if heterogeneous classifiers ensemble approaches provide more unbiased rankings and if they improve final classification performance. Furthermore, we calculate an empirical prediction performance loss for using the same classifier in SVC feature ranking and final classification from the optimal choices.

  6. Multivariate Bias Correction Procedures for Improving Water Quality Predictions using Mechanistic Models

    Science.gov (United States)

    Libera, D.; Arumugam, S.

    2015-12-01

    Water quality observations are usually not available on a continuous basis because of the expensive cost and labor requirements so calibrating and validating a mechanistic model is often difficult. Further, any model predictions inherently have bias (i.e., under/over estimation) and require techniques that preserve the long-term mean monthly attributes. This study suggests and compares two multivariate bias-correction techniques to improve the performance of the SWAT model in predicting daily streamflow, TN Loads across the southeast based on split-sample validation. The first approach is a dimension reduction technique, canonical correlation analysis that regresses the observed multivariate attributes with the SWAT model simulated values. The second approach is from signal processing, importance weighting, that applies a weight based off the ratio of the observed and model densities to the model data to shift the mean, variance, and cross-correlation towards the observed values. These procedures were applied to 3 watersheds chosen from the Water Quality Network in the Southeast Region; specifically watersheds with sufficiently large drainage areas and number of observed data points. The performance of these two approaches are also compared with independent estimates from the USGS LOADEST model. Uncertainties in the bias-corrected estimates due to limited water quality observations are also discussed.

  7. Data assimilation in hydrodynamic modelling: on the treatment of non-linearity and bias

    DEFF Research Database (Denmark)

    Sørensen, Jacob Viborg Tornfeldt; Madsen, Henrik

    2004-01-01

    oceanic models. Three measures of non-linearity and one bias measure have been implemented to assess the validity of these assumptions for a given model set-up. Two of these measures further express the non-Gaussianity and thus guide the proper statistical interpretation of the results. The applicability...... of the measures is demonstrated in two twin case experiments in an idealised set-up....

  8. Bias due to selective genotyping in genomic prediction using H-BLUP

    DEFF Research Database (Denmark)

    Wang, Lei; Madsen, Per; Sapp, Robyn

    H-BLUP uses a variance-covariance structure based on a combined relationship matrix (H), which augments a pedigree-based relationship matrix (A) with a genomic relationship matrix (G) for genotyped individuals. In practice, often only preselected individuals are genotyped and this selective genot...

  9. The importance of estimating selection bias on prevalence estimates, shortly after a disaster.

    NARCIS (Netherlands)

    Grievink, L.; Velden, P.G. van der; Yzermans, C.J.; Roorda, J.; Stellato, R.K.

    2006-01-01

    PURPOSE: The aim was to study selective participation and its effect on prevalence estimates in a health survey of affected residents 3 weeks after a man-made disaster in The Netherlands (May 13, 2000). METHODS: All affected adult residents were invited to participate. Survey (questionnaire) data we

  10. The importance of estimating selection bias on prevalence estimates shortly after a disaster.

    NARCIS (Netherlands)

    Grievink, Linda; Velden, Peter G van der; Yzermans, C Joris; Roorda, Jan; Stellato, Rebecca K

    2006-01-01

    PURPOSE: The aim was to study selective participation and its effect on prevalence estimates in a health survey of affected residents 3 weeks after a man-made disaster in The Netherlands (May 13, 2000). METHODS: All affected adult residents were invited to participate. Survey (questionnaire) data we

  11. Measuring Teaching Quality in Higher Education : Assessing the Problem of Selection Bias in Course Evaluations

    NARCIS (Netherlands)

    Salomons, Anna; Goos, Maarten

    2014-01-01

    Student evaluations of teaching are widely used to measure teaching quality and compare it across different courses, teachers, departments and institutions: as such, they are of increasing importance for teacher promotion decisions as well as student course selection. However, the response on course

  12. The importance of estimating selection bias on prevalence estimates shortly after a disaster.

    NARCIS (Netherlands)

    Grievink, Linda; Velden, Peter G van der; Yzermans, C Joris; Roorda, Jan; Stellato, Rebecca K

    2006-01-01

    PURPOSE: The aim was to study selective participation and its effect on prevalence estimates in a health survey of affected residents 3 weeks after a man-made disaster in The Netherlands (May 13, 2000). METHODS: All affected adult residents were invited to participate. Survey (questionnaire) data

  13. The importance of estimating selection bias on prevalence estimates, shortly after a disaster.

    NARCIS (Netherlands)

    Grievink, L.; Velden, P.G. van der; Yzermans, C.J.; Roorda, J.; Stellato, R.K.

    2006-01-01

    PURPOSE: The aim was to study selective participation and its effect on prevalence estimates in a health survey of affected residents 3 weeks after a man-made disaster in The Netherlands (May 13, 2000). METHODS: All affected adult residents were invited to participate. Survey (questionnaire) data

  14. Preliminary Study of m(b) Bias at Selected Soviet Seismic Stations.

    Science.gov (United States)

    1986-03-21

    8217-R66 395 PRELIMINARY STUDY OF M(B) BIRS AT SELECTED SOVIET 1/1 SEISMIC STRTIONS(U) SCIENCE APPLICATIONS INTERNATIONAL CORP ARLINGTON VR A S RYRLL...earthquakes in each source region was not given in the ,.t 1980 paper, but in the earlier work it ranged from 35 events for Asia and the Mediter - ranean to

  15. DIAGNOSTIC INVESTIGATION OF SIMULATION BIAS WITH THE GRAPES- MESO MODEL FOR A TORRENTIAL RAIN CASE

    Institute of Scientific and Technical Information of China (English)

    KONG Rong; WANG Jian-jie

    2007-01-01

    In this paper, the numerical simulation bias of the non-hydrostatic version GRAPES-Meso (Mesoscale of the Global and Regional Assimilation and Prediction System) at the resolution of 0.18° for a torrential rain case, which happened in May 31 st to June 1st 2005 over Hunan province, are diagnosed and investigated by using the radiosondes, intensive surface observation, and the operational global analysis data, and the sensitivity experimental results as well. It is shown in the result that the GRAPES-Meso could reproduce quite well the main features of large-scale circulation and the distribution of the accumulated 24h precipitation and the key locations of the torrential rainfall are captured reasonably well by the model.However, bias exist in the simulation of the mesoscale features of the torrential rain and details of the relevant systems, for example, the simulated rainfall that is too earlier in model integration and remarkable underprediction of the peak value of rainfall rates over the heaviest rainfall region, the weakness of the upper jet simulation and the overprediction of the south-west wind in the lower troposphere etc. The investigation reveals that the sources of the simulation bias are different. The erroneous model rainfall in the earlier integration stage over the heaviest rainfall region is induced by the model initial condition bias of the wind field at about 925hPa over the torrential rainfall region, where the bias grow rapidly and spread upward to about 600hPa level within the few hours into the integration and result in abnormal convergence of the wind and moisture, and thus the unreal rainfall over that region. The large bias on the simulated rainfall intensity over the heaviest rainfall region might be imputed to the following combined factors of(1)the simulation bias on the strenh and detailed structures of the upper-level jet core which bring about significant underpredictions of the dynamic conditions (including upper-level divergence

  16. Comparing the impact of time displaced and biased precipitation estimates for online updated urban runoff models.

    Science.gov (United States)

    Borup, Morten; Grum, Morten; Mikkelsen, Peter Steen

    2013-01-01

    When an online runoff model is updated from system measurements, the requirements of the precipitation input change. Using rain gauge data as precipitation input there will be a displacement between the time when the rain hits the gauge and the time where the rain hits the actual catchment, due to the time it takes for the rain cell to travel from the rain gauge to the catchment. Since this time displacement is not present for system measurements the data assimilation scheme might already have updated the model to include the impact from the particular rain cell when the rain data is forced upon the model, which therefore will end up including the same rain twice in the model run. This paper compares forecast accuracy of updated models when using time displaced rain input to that of rain input with constant biases. This is done using a simple time-area model and historic rain series that are either displaced in time or affected with a bias. The results show that for a 10 minute forecast, time displacements of 5 and 10 minutes compare to biases of 60 and 100%, respectively, independent of the catchments time of concentration.

  17. W(h)ither the Oracle? Cognitive biases and other human challenges of integrated environmental modeling

    Science.gov (United States)

    Glynn, Pierre D.; Ames, D.P.; Quinn, N. W. T.; Rizzoli, A.E.

    2014-01-01

    Integrated environmental modeling (IEM) can organize and increase our knowledge of the complex, dynamic ecosystems that house our natural resources and control the quality of our environments. Human behavior, however, must be taken into account. Human biases/heuristics reflect adaptation over our evolutionary past to frequently experienced situations that affected our survival and that provided sharply distinguished feedbacks at the level of the individual. Unfortunately, human behavior is not adapted to the more diffusely experienced, less frequently encountered, problems and issues that IEM typically seeks to address in the simulation of natural resources and environments. While seeking inspiration from the prophetic traditions of the Oracle of Delphi, several human biases are identified that may affect how the science base of IEM is assembled, and how IEM results are interpreted and used. These biases are supported by personal observations, and by the findings of behavioral scientists. A process for critical analysis is proposed that solicits explicit accounting and cognizance of potential human biases. A number of suggestions are made to address the human challenges of IEM, in addition to maintaining attitudes of watchful humility, open-mindedness, honesty, and transparent accountability. These include creating a new area of study in the behavioral biogeosciences, using structured processes for engaging the modeling and stakeholder community in IEM, and using “red teams” to increase resilience of IEM constructs and use.

  18. Reporting error in weight and its implications for bias in economic models.

    Science.gov (United States)

    Cawley, John; Maclean, Johanna Catherine; Hammer, Mette; Wintfeld, Neil

    2015-12-01

    Most research on the economic consequences of obesity uses data on self-reported weight, which contains reporting error that has the potential to bias coefficient estimates in economic models. The purpose of this paper is to measure the extent and characteristics of reporting error in weight, and to examine its impact on regression coefficients in models of the healthcare consequences of obesity. We analyze data from the National Health and Nutrition Examination Survey (NHANES) for 2003-2010, which includes both self-reports and measurements of weight and height. We find that reporting error in weight is non-classical: underweight respondents tend to overreport, and overweight and obese respondents tend to underreport, their weight, with underreporting increasing in measured weight. This error results in roughly 1 out of 7 obese individuals being misclassified as non-obese. Reporting error is also correlated with other common regressors in economic models, such as education. Although it is a common misconception that reporting error always causes attenuation bias, comparisons of models that use self-reported and measured weight confirm that reporting error can cause upward bias in coefficient estimates. For example, use of self-reports leads to overestimates of the probability that an obese man uses a prescription drug, has a healthcare visit, or has a hospital admission. These findings underscore that models of the consequences of obesity should use measurements of weight, when available, and that social science datasets should measure weight rather than simply ask subjects to report their weight.

  19. Simultaneous Estimation of Model State Variables and Observation and Forecast Biases Using a Two-Stage Hybrid Kalman Filter

    Science.gov (United States)

    Pauwels, V. R. N.; DeLannoy, G. J. M.; Hendricks Franssen, H.-J.; Vereecken, H.

    2013-01-01

    In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

  20. Simultaneous estimation of model state variables and observation and forecast biases using a two-stage hybrid Kalman filter

    Directory of Open Access Journals (Sweden)

    V. R. N. Pauwels

    2013-04-01

    Full Text Available In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the Discrete Kalman Filter, and the state variables using the Ensemble Kalman Filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware Ensemble Kalman Filter. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

  1. Bayesian Constrained-Model Selection for Factor Analytic Modeling

    OpenAIRE

    Peeters, Carel F.W.

    2016-01-01

    My dissertation revolves around Bayesian approaches towards constrained statistical inference in the factor analysis (FA) model. Two interconnected types of restricted-model selection are considered. These types have a natural connection to selection problems in the exploratory FA (EFA) and confirmatory FA (CFA) model and are termed Type I and Type II model selection. Type I constrained-model selection is taken to mean the determination of the appropriate dimensionality of a model. This type ...

  2. Worry or craving? A selective review of evidence for food-related attention biases in obese individuals, eating-disorder patients, restrained eaters and healthy samples.

    Science.gov (United States)

    Werthmann, Jessica; Jansen, Anita; Roefs, Anne

    2015-05-01

    Living in an 'obesogenic' environment poses a serious challenge for weight maintenance. However, many people are able to maintain a healthy weight indicating that not everybody is equally susceptible to the temptations of this food environment. The way in which someone perceives and reacts to food cues, that is, cognitive processes, could underlie differences in susceptibility. An attention bias for food could be such a cognitive factor that contributes to overeating. However, an attention bias for food has also been implicated with restrained eating and eating-disorder symptomatology. The primary aim of the present review was to determine whether an attention bias for food is specifically related to obesity while also reviewing evidence for attention biases in eating-disorder patients, restrained eaters and healthy-weight individuals. Another aim was to systematically examine how selective attention for food relates (causally) to eating behaviour. Current empirical evidence on attention bias for food within obese samples, eating-disorder patients, and, even though to a lesser extent, in restrained eaters is contradictory. However, present experimental studies provide relatively consistent evidence that an attention bias for food contributes to subsequent food intake. This review highlights the need to distinguish not only between different (temporal) attention bias components, but also to take different motivations (craving v. worry) and their impact on attentional processing into account. Overall, the current state of research suggests that biased attention could be one important cognitive mechanism by which the food environment tempts us into overeating.

  3. Collaborative Project: The problem of bias in defining uncertainty in computationally enabled strategies for data-driven climate model development. Final Technical Report.

    Energy Technology Data Exchange (ETDEWEB)

    Huerta, Gabriel [Univ. of New Mexico, Albuquerque, NM (United States)

    2016-05-10

    The objective of the project is to develop strategies for better representing scientific sensibilities within statistical measures of model skill that then can be used within a Bayesian statistical framework for data-driven climate model development and improved measures of model scientific uncertainty. One of the thorny issues in model evaluation is quantifying the effect of biases on climate projections. While any bias is not desirable, only those biases that affect feedbacks affect scatter in climate projections. The effort at the University of Texas is to analyze previously calculated ensembles of CAM3.1 with perturbed parameters to discover how biases affect projections of global warming. The hypothesis is that compensating errors in the control model can be identified by their effect on a combination of processes and that developing metrics that are sensitive to dependencies among state variables would provide a way to select version of climate models that may reduce scatter in climate projections. Gabriel Huerta at the University of New Mexico is responsible for developing statistical methods for evaluating these field dependencies. The UT effort will incorporate these developments into MECS, which is a set of python scripts being developed at the University of Texas for managing the workflow associated with data-driven climate model development over HPC resources. This report reflects the main activities at the University of New Mexico where the PI (Huerta) and the Postdocs (Nosedal, Hattab and Karki) worked on the project.

  4. The First Year of OCO-2 XCO2 Observations: Bias Correction and Comparison to Models.

    Science.gov (United States)

    O'Dell, C.; Eldering, A.; Frankenberg, C.; Crisp, D.; Gunson, M. R.; Fisher, B.; Mandrake, L.; McDuffie, J. L.; Pollock, H. R.; Wennberg, P. O.; Wunch, D.; Doran, G. B., Jr.

    2015-12-01

    Observations of atmospheric carbon dioxide from the Orbiting Carbon Observatory-2 (OCO-2) have the potential to be revolutionary in their impact on our understanding of carbon sources and sinks. For this to be achieved, however, requires the observations to have sub-ppm systematic errors; the large data density of OCO-2 generally means that random errors will be of lesser importance in terms of regional scale fluxes. In this presentation we report on results from the Atmospheric Carbon Observations from Space (ACOS) algorithm as applied to the first year of OCO-2 observations, with a particular focus on filtering and bias-correction of the OCO-2 data "Nadir" and "Glint" mode data. In general, we find the random errors to be low (0.5-2.0 ppm), especially for ocean glint retrievals, consistent with the higher signal-to-noise ratio and reduced effects of aerosols over ocean. Systematic errors are explored via comparison to TCCON, low-variability southern hemisphere data, and data taken over small spatial regions. These are used to form a multi-linear bias correction, similar that that implemented for ACOS/GOSAT observations. Additionally we present comparisons of the first year of bias-corrected OCO-2 observations to inverse model output. This comparison will shed light on potential retrieval biases still lurking in the OCO-2 data, such as from surface albedo, aerosol effects, and other error sources.

  5. Diffusion Modelling Reveals the Decision Making Processes Underlying Negative Judgement Bias in Rats.

    Directory of Open Access Journals (Sweden)

    Claire A Hales

    Full Text Available Human decision making is modified by emotional state. Rodents exhibit similar biases during interpretation of ambiguous cues that can be altered by affective state manipulations. In this study, the impact of negative affective state on judgement bias in rats was measured using an ambiguous-cue interpretation task. Acute treatment with an anxiogenic drug (FG7142, and chronic restraint stress and social isolation both induced a bias towards more negative interpretation of the ambiguous cue. The diffusion model was fit to behavioural data to allow further analysis of the underlying decision making processes. To uncover the way in which parameters vary together in relation to affective state manipulations, independent component analysis was conducted on rate of information accumulation and distances to decision threshold parameters for control data. Results from this analysis were applied to parameters from negative affective state manipulations. These projected components were compared to control components to reveal the changes in decision making processes that are due to affective state manipulations. Negative affective bias in rodents induced by either FG7142 or chronic stress is due to a combination of more negative interpretation of the ambiguous cue, reduced anticipation of the high reward and increased anticipation of the low reward.

  6. Diffusion Modelling Reveals the Decision Making Processes Underlying Negative Judgement Bias in Rats.

    Science.gov (United States)

    Hales, Claire A; Robinson, Emma S J; Houghton, Conor J

    2016-01-01

    Human decision making is modified by emotional state. Rodents exhibit similar biases during interpretation of ambiguous cues that can be altered by affective state manipulations. In this study, the impact of negative affective state on judgement bias in rats was measured using an ambiguous-cue interpretation task. Acute treatment with an anxiogenic drug (FG7142), and chronic restraint stress and social isolation both induced a bias towards more negative interpretation of the ambiguous cue. The diffusion model was fit to behavioural data to allow further analysis of the underlying decision making processes. To uncover the way in which parameters vary together in relation to affective state manipulations, independent component analysis was conducted on rate of information accumulation and distances to decision threshold parameters for control data. Results from this analysis were applied to parameters from negative affective state manipulations. These projected components were compared to control components to reveal the changes in decision making processes that are due to affective state manipulations. Negative affective bias in rodents induced by either FG7142 or chronic stress is due to a combination of more negative interpretation of the ambiguous cue, reduced anticipation of the high reward and increased anticipation of the low reward.

  7. Different patterns of medication change after subthalamic or pallidal stimulation for Parkinson's disease: target related effect or selection bias?

    Science.gov (United States)

    Minguez-Castellan..., A; Escamilla-Sevilla, F; Katati, M; Martin-Linares, J; Meersmans, M; Ortega-Moreno, A; Arjona, V

    2005-01-01

    Background: Bilateral subthalamic nucleus (STN) deep brain stimulation (DBS) is favoured over bilateral globus pallidus internus (Gpi) DBS for symptomatic treatment of advanced Parkinson's disease (PD) due to the possibility of reducing medication, despite lack of definitive comparative evidence. Objective: To analyse outcomes after one year of bilateral Gpi or STN DBS, with consideration of influence of selection bias on the pattern of postsurgical medication change. Methods: The first patients to undergo bilateral Gpi (n = 10) or STN (n = 10) DBS at our centre were studied. They were assessed presurgically and one year after surgery (CAPIT protocol). Results: Before surgery the Gpi DBS group had more dyskinesias and received lower doses of medication. At one year, mean reduction in UPDRS off medication score was 35% and 39% in the Gpi and STN groups, respectively (non-significant difference). Dyskinesias reduced in proportion to presurgical severity. The levodopa equivalent dose was significantly reduced only in the STN group (24%). This study high-lights the absence of significant differences between the groups in clinical scales and medication dose at one year. In the multivariate analysis of predictive factors for off-state motor improvement, the presurgical levodopa equivalent dose showed a direct relation in the STN and an inverse relation in the Gpi group. Conclusion: Differences in the patterns of medication change after Gpi and STN DBS may be partly due to a patient selection bias. Both procedures may be equally useful for different subgroups of patients with advanced PD, Gpi DBS especially for patients with lower threshold for dyskinesia. PMID:15607992

  8. General trends in selectively driven codon usage biases in the domain archaea.

    Science.gov (United States)

    Iriarte, Andrés; Jara, Eugenio; Leytón, Lucía; Diana, Leticia; Musto, Héctor

    2014-10-01

    Since the advent of rapid techniques for sequencing DNA in the mid 70's, it became clear that all codons coding for the same amino acid are not used according to neutral expectations. In the last 30 years, several theories were proposed for explaining this fact. However, the most important concepts were the result of analyses carried out in Bacteria, and unicellular and multicellular eukaryotes like mammals (in other words, in two of the three Domains of life). In this communication, we study the main forces that shape codon usage in Archaeae under an evolutionary perspective. This is important because, as known, the orthologous genes related with the informational system in this Domain (replication, transcription and translation) are more similar to eukaryotes than to Bacteria. Our results show that the effect of selection acting at the level of translation is present in the Domain but mainly restricted to only a phylum (Euryarchaeota) and therefore is not as extended as in Bacteria. Besides, we describe the phylogenetic distribution of translational optimal codons and estimate the effect of selection acting at the level of accuracy. Finally, we discuss these results under some peculiarities that characterize this Domain.

  9. Codon Usage Patterns in Corynebacterium glutamicum: Mutational Bias, Natural Selection and Amino Acid Conservation

    Directory of Open Access Journals (Sweden)

    Guiming Liu

    2010-01-01

    Full Text Available The alternative synonymous codons in Corynebacterium glutamicum, a well-known bacterium used in industry for the production of amino acid, have been investigated by multivariate analysis. As C. glutamicum is a GC-rich organism, G and C are expected to predominate at the third position of codons. Indeed, overall codon usage analyses have indicated that C and/or G ending codons are predominant in this organism. Through multivariate statistical analysis, apart from mutational selection, we identified three other trends of codon usage variation among the genes. Firstly, the majority of highly expressed genes are scattered towards the positive end of the first axis, whereas the majority of lowly expressed genes are clustered towards the other end of the first axis. Furthermore, the distinct difference in the two sets of genes was that the C ending codons are predominate in putatively highly expressed genes, suggesting that the C ending codons are translationally optimal in this organism. Secondly, the majority of the putatively highly expressed genes have a tendency to locate on the leading strand, which indicates that replicational and transciptional selection might be invoked. Thirdly, highly expressed genes are more conserved than lowly expressed genes by synonymous and nonsynonymous substitutions among orthologous genes fromthe genomes of C. glutamicum and C. diphtheriae. We also analyzed other factors such as the length of genes and hydrophobicity that might influence codon usage and found their contributions to be weak.

  10. Selective attention to food-related stimuli in hunger: are attentional biases specific to emotional and psychopathological states, or are they also found in normal drive states?

    Science.gov (United States)

    Mogg, K; Bradley, B P; Hyare, H; Lee, S

    1998-02-01

    Previous work has indicated that anxiety disorders and eating disorders are associated with selective processing of stimuli relevant to patients' concerns (e.g. Mathews and MacLeod, 1994; Annual Review of Psychology, 45, 25-50; Channon et al., 1988; British Journal of Clinical Psychology, 27, 259-260). A dot probe task was used to investigate whether attentional biases are also a feature of a normal drive state. Specifically, we examined whether hunger is associated with biases in selective attention and in pre-attentive processes for food-relevant stimuli. Subjects with high levels of hunger showed a greater attentional bias for food-related words presented in a suprathreshold exposure condition (words shown for 500 msec), in comparison with those with low hunger. There was no evidence in the present study of a hunger-related bias in pre-attentive processes (i.e. when words were shown for 14 msec and masked). Results suggest that a non-emotional motivational state, such as hunger, is associated with a bias in certain aspects of information processing, such as selective attention, for stimuli that are relevant to the motivational state. Findings are discussed in relation to recent research into emotion-related cognitive biases.

  11. Heterogeneity, self-Selection Bias and Return to Education%异质性、自选择偏差和教育收益率

    Institute of Scientific and Technical Information of China (English)

    缪柏其; 舒海兵; 叶五一

    2011-01-01

    Employing latest 8urvey data, this paper adopts generalized selection correction method which allows heterogeneity and self-selection bias problem, to estimate the return to college education of 2006. Meanwhile, we compare our results with traditional model and analyze the applicability of other non-experimental evaluation method. We find the retum to college for 2006 has increased substantially with the deeply economic reform and development of labor market. In addition, there is no significantly observable heterogeneity, but evidently unobservable heterogeneity and self-selection bias, and people sort into schooling on the basis of the principle of comparative advantage.%采用广义选择修正法,在考虑异质性和自选择偏差问题基础之上,结合最新的调查数据,评估了我国2006年高等教育收益率,并与传统模型下的研究结果进行了比较.同时还分析了其他非实验数据效应评估模型在本文的适用性.研究结果表明,随着经济改革深化和劳动市场的完善,2006年的高等教育收益率较之前有了显著的提高.另外,还发现可观察的异质性不明显,但不可观察的异质性和自选择偏差选择显著存在,且人们会根据自己的比较优势选择是否接受高等教育.

  12. Selected Logistics Models and Techniques.

    Science.gov (United States)

    1984-09-01

    ACCESS PROCEDURE: On-Line System (OLS), UNINET . RCA maintains proprietary control of this model, and the model is available only through a lease...System (OLS), UNINET . RCA maintains proprietary control of this model, and the model is available only through a lease arrangement. • SPONSOR: ASD/ACCC

  13. A road map for improving dry-bias in simulating the South Asian monsoon precipitation by climate models

    Science.gov (United States)

    Goswami, Bidyut Bikash; Goswami, B. N.

    2017-09-01

    An outstanding problem of climate models is the persistent dry bias in simulating precipitation over the south Asian summer monsoon region. Guided by observations, it is hypothesized that the dry-bias in simulating precipitation by the models is related to underestimation of high pass variance by most models. An analysis of the simulated mean and variance in precipitation by 36 coupled models show that the dry bias in simulating the mean precipitation by the models is indeed proportional to the underestimation of the variance. Models also indicate that the underestimation of the high-pass variance arise due to the underestimation of the intense rainfall events by models. Further, it is found that the higher resolution models simulate increasingly reduced dry bias by simulating high-frequency variance better through better simulation probability of intense rainfall events. The robustness of our findings over different regions and during both boreal summer and winter seasons indicates the universality of the hypothesis.

  14. Producing physically consistent and bias free extreme precipitation events over the Switzerland: Bridging gaps between meteorology and impact models

    Science.gov (United States)

    José Gómez-Navarro, Juan; Raible, Christoph C.; Blumer, Sandro; Martius, Olivia; Felder, Guido

    2016-04-01

    Extreme precipitation episodes, although rare, are natural phenomena that can threat human activities, especially in areas densely populated such as Switzerland. Their relevance demands the design of public policies that protect public assets and private property. Therefore, increasing the current understanding of such exceptional situations is required, i.e. the climatic characterisation of their triggering circumstances, severity, frequency, and spatial distribution. Such increased knowledge shall eventually lead us to produce more reliable projections about the behaviour of these events under ongoing climate change. Unfortunately, the study of extreme situations is hampered by the short instrumental record, which precludes a proper characterization of events with return period exceeding few decades. This study proposes a new approach that allows studying storms based on a synthetic, but physically consistent database of weather situations obtained from a long climate simulation. Our starting point is a 500-yr control simulation carried out with the Community Earth System Model (CESM). In a second step, this dataset is dynamically downscaled with the Weather Research and Forecasting model (WRF) to a final resolution of 2 km over the Alpine area. However, downscaling the full CESM simulation at such high resolution is infeasible nowadays. Hence, a number of case studies are previously selected. This selection is carried out examining the precipitation averaged in an area encompassing Switzerland in the ESM. Using a hydrological criterion, precipitation is accumulated in several temporal windows: 1 day, 2 days, 3 days, 5 days and 10 days. The 4 most extreme events in each category and season are selected, leading to a total of 336 days to be simulated. The simulated events are affected by systematic biases that have to be accounted before this data set can be used as input in hydrological models. Thus, quantile mapping is used to remove such biases. For this task

  15. Do Methodological Choices in Environmental Modeling Bias Rebound Effects? A Case Study on Electric Cars.

    Science.gov (United States)

    Font Vivanco, David; Tukker, Arnold; Kemp, René

    2016-10-18

    Improvements in resource efficiency often underperform because of rebound effects. Calculations of the size of rebound effects are subject to various types of bias, among which methodological choices have received particular attention. Modellers have primarily focused on choices related to changes in demand, however, choices related to modeling the environmental burdens from such changes have received less attention. In this study, we analyze choices in the environmental assessment methods (life cycle assessment (LCA) and hybrid LCA) and environmental input-output databases (E3IOT, Exiobase and WIOD) used as a source of bias. The analysis is done for a case study on battery electric and hydrogen cars in Europe. The results describe moderate rebound effects for both technologies in the short term. Additionally, long-run scenarios are calculated by simulating the total cost of ownership, which describe notable rebound effect sizes-from 26 to 59% and from 18 to 28%, respectively, depending on the methodological choices-with favorable economic conditions. Relevant sources of bias are found to be related to incomplete background systems, technology assumptions and sectorial aggregation. These findings highlight the importance of the method setup and of sensitivity analyses of choices related to environmental modeling in rebound effect assessments.

  16. Bias-dependent model of the electrical impedance of ionic polymer-metal composites.

    Science.gov (United States)

    Cha, Youngsu; Porfiri, Maurizio

    2013-02-01

    In this paper, we analyze the charge dynamics of ionic polymer-metal composites (IPMCs) in response to voltage inputs composed of a large dc bias and a small superimposed time-varying voltage. The IPMC chemoelectrical behavior is described through the modified Poisson-Nernst-Planck framework, in which steric effects are taken into consideration. The physics of charge build-up and mass transfer in the proximity of the high surface electrodes is modeled by schematizing the IPMC as the stacked sequence of five layers, in which the ionomeric membrane is separated from the metal electrodes by two composite layers. The method of matched asymptotic expansions is used to derive a semianalytical solution for the concentration of mobile counterions and the electric potential in the IPMC, which is, in turn, used to establish an equivalent circuit model for the IPMC electrical response. The circuit model consists of the series connection of a resistor and two complex elements, each constituted by the parallel connection of a capacitor and a Warburg impedance. The resistor is associated with ion transport in the ionomeric membrane and is independent of the dc bias. The capacitors and the Warburg impedance idealize charge build-up and mass transfer in the vicinity of the electrodes and their value is controlled by the dc bias. The proposed approach is validated against experimental results on in-house fabricated IPMCs and the accuracy of the equivalent circuit is assessed through comparison with finite element results.

  17. Foreground Bias From Parametric Models of Far-IR Dust Emission

    CERN Document Server

    Kogut, A

    2016-01-01

    We use simple toy models of far-IR dust emission to estimate the accuracy to which the polarization of the cosmic microwave background can be recovered using multi-frequency fits, if the parametric form chosen for the fitted dust model differs from the actual dust emission. Commonly used approximations to the far-IR dust spectrum yield CMB residuals comparable to or larger than the sensitivities expected for the next generation of CMB missions, despite fitting the combined CMB + foreground emission to precision 0.1% or better. The Rayleigh-Jeans approximation to the dust spectrum biases the fitted dust spectral index by Delta beta_d = 0.2 and the inflationary B-mode amplitude by Delta r = 0.03. Fitting the dust to a modified blackbody at a single temperature biases the best-fit CMB by Delta r > 0.003 if the true dust spectrum contains multiple temperature components. A 13-parameter model fitting two temperature components reduces this bias by an order of magnitude if the true dust spectrum is in fact a simple...

  18. Continuum model for chiral induced spin selectivity in helical molecules

    Energy Technology Data Exchange (ETDEWEB)

    Medina, Ernesto [Centro de Física, Instituto Venezolano de Investigaciones Científicas, 21827, Caracas 1020 A (Venezuela, Bolivarian Republic of); Groupe de Physique Statistique, Institut Jean Lamour, Université de Lorraine, 54506 Vandoeuvre-les-Nancy Cedex (France); Department of Chemistry and Biochemistry, Arizona State University, Tempe, Arizona 85287 (United States); González-Arraga, Luis A. [IMDEA Nanoscience, Cantoblanco, 28049 Madrid (Spain); Finkelstein-Shapiro, Daniel; Mujica, Vladimiro [Department of Chemistry and Biochemistry, Arizona State University, Tempe, Arizona 85287 (United States); Berche, Bertrand [Centro de Física, Instituto Venezolano de Investigaciones Científicas, 21827, Caracas 1020 A (Venezuela, Bolivarian Republic of); Groupe de Physique Statistique, Institut Jean Lamour, Université de Lorraine, 54506 Vandoeuvre-les-Nancy Cedex (France)

    2015-05-21

    A minimal model is exactly solved for electron spin transport on a helix. Electron transport is assumed to be supported by well oriented p{sub z} type orbitals on base molecules forming a staircase of definite chirality. In a tight binding interpretation, the spin-orbit coupling (SOC) opens up an effective π{sub z} − π{sub z} coupling via interbase p{sub x,y} − p{sub z} hopping, introducing spin coupled transport. The resulting continuum model spectrum shows two Kramers doublet transport channels with a gap proportional to the SOC. Each doubly degenerate channel satisfies time reversal symmetry; nevertheless, a bias chooses a transport direction and thus selects for spin orientation. The model predicts (i) which spin orientation is selected depending on chirality and bias, (ii) changes in spin preference as a function of input Fermi level and (iii) back-scattering suppression protected by the SO gap. We compute the spin current with a definite helicity and find it to be proportional to the torsion of the chiral structure and the non-adiabatic Aharonov-Anandan phase. To describe room temperature transport, we assume that the total transmission is the result of a product of coherent steps.

  19. Bias Correction for climate impact modeling within the framework of the HAPPI Initiative

    Science.gov (United States)

    Saeed, Fahad; Lange, Stefan; Schleussner, Carl-Friedrich

    2017-04-01

    In its landmark Paris Agreement of 2015, the Conference of the Parties of the United Nations Framework Convention on Climate Change (UNFCCC) invited the IPCC to prepare a special report "on the impacts of global warming of 1.5°C above pre-industrial levels and related greenhouse gas emission pathways" by 2018. Unfortunately, most current experiments (including Coupled Model Inter-comparison Project (CMIP)), are not specifically designed for making a substantial contribution to this report. To fill this gap, the HAPPI (Half a degree Additional warming, Projection, Prognosis and Impacts) initiative has been designed to assess climate projections, and in particular extreme weather, at present day and in worlds that are 1.5°C and 2.0°C warmer than pre-industrial conditions? Global Climate Model (GCM) output for HAPPI will be utilized to assess climate impacts with a range of sectorial climate impact models. Before the use of climate data as input for sectorial impact models, statistical bias correction is commonly applied to correct climate model data for systematic deviations of the simulated historic data from observations and to increase the accuracy of the projections. Different approaches have been adopted for this purpose, however the most common are the one based on transfer functions generated to map the distribution of the simulated historical data to that of the observations. In the current study, we presented results for a novel bias correction method developed for Inter-Sectoral Impact Model Intercomparison Project Phase 2b (ISIMIP2b) applied to output of different GCMs generated within the HAPPI project. The results indicate that the application of bias correction has shown substantial improvement in the results when we compared to observational data. Besides the marked improvement in seasonal mean differences for different variables, also the output for extreme event indicators is considerably improved. We conclude that the applied application of bias

  20. Biases in atmospheric CO2 estimates from correlated meteorology modeling errors

    Science.gov (United States)

    Miller, S. M.; Hayek, M. N.; Andrews, A. E.; Fung, I.; Liu, J.

    2015-03-01

    Estimates of CO2 fluxes that are based on atmospheric measurements rely upon a meteorology model to simulate atmospheric transport. These models provide a quantitative link between the surface fluxes and CO2 measurements taken downwind. Errors in the meteorology can therefore cause errors in the estimated CO2 fluxes. Meteorology errors that correlate or covary across time and/or space are particularly worrisome; they can cause biases in modeled atmospheric CO2 that are easily confused with the CO2 signal from surface fluxes, and they are difficult to characterize. In this paper, we leverage an ensemble of global meteorology model outputs combined with a data assimilation system to estimate these biases in modeled atmospheric CO2. In one case study, we estimate the magnitude of month-long CO2 biases relative to CO2 boundary layer enhancements and quantify how that answer changes if we either include or remove error correlations or covariances. In a second case study, we investigate which meteorological conditions are associated with these CO2 biases. In the first case study, we estimate uncertainties of 0.5-7 ppm in monthly-averaged CO2 concentrations, depending upon location (95% confidence interval). These uncertainties correspond to 13-150% of the mean afternoon CO2 boundary layer enhancement at individual observation sites. When we remove error covariances, however, this range drops to 2-22%. Top-down studies that ignore these covariances could therefore underestimate the uncertainties and/or propagate transport errors into the flux estimate. In the second case study, we find that these month-long errors in atmospheric transport are anti-correlated with temperature and planetary boundary layer (PBL) height over terrestrial regions. In marine environments, by contrast, these errors are more strongly associated with weak zonal winds. Many errors, however, are not correlated with a single meteorological parameter, suggesting that a single meteorological proxy is

  1. Acute pulmonary embolism: impact of selection bias in prospective diagnostic studies. ANTELOPE Study Group. Advances in New Technologies Evaluating the Localization of Pulmonary Embolism.

    Science.gov (United States)

    Hartmann, I J; Prins, M H; Büller, H R; Banga, J D

    2001-04-01

    We evaluated selection bias in a prospective study of 1,162 consecutive patients with suspected pulmonary embolism. Of these, 983 were eligible, and 627 could actually be included. During two months extensive data were collected on all non-included patients. Finally, our patient characteristics were compared with those of the PIOPED study (1990) and the study of Hull et al. (1994). Compared with included patients, the non-included patients had more often non-diagnostic V/Q scans (50% vs. 36%, p PIOPED study. In the PIOPED study patients who had contra-indications for pulmonary angiography were excluded, while in the study of Hull et al. those with inadequate cardiorespiratory reserve were excluded. In studies on new diagnostic technologies, patient selection bias does occur. The potential for such a selection bias should be taken into account when diagnostic strategies are devised to improve their generalizability and acceptability.

  2. A Modified Brain MR Image Segmentation and Bias Field Estimation Model Based on Local and Global Information

    Directory of Open Access Journals (Sweden)

    Wang Cong

    2016-01-01

    Full Text Available Because of the poor radio frequency coil uniformity and gradient-driven eddy currents, there is much noise and intensity inhomogeneity (bias in brain magnetic resonance (MR image, and it severely affects the segmentation accuracy. Better segmentation results are difficult to achieve by traditional methods; therefore, in this paper, a modified brain MR image segmentation and bias field estimation model based on local and global information is proposed. We first construct local constraints including image neighborhood information in Gaussian kernel mapping space, and then the complete regularization is established by introducing nonlocal spatial information of MR image. The weighting between local and global information is automatically adjusted according to image local information. At the same time, bias field information is coupled with the model, and it makes the model reduce noise interference but also can effectively estimate the bias field information. Experimental results demonstrate that the proposed algorithm has strong robustness to noise and bias field is well corrected.

  3. A conceptual model for establishing tolerance limits for analytic bias and imprecision based on variations in population test distributions.

    Science.gov (United States)

    Klee, G

    1997-04-25

    A conceptual model is proposed for defining analytic bias limits utilizing the variations found in cumulative test value distributions. The model is based on the propositions that changes in analytic bias are more important than analytic imprecision in medical diagnoses and that analytic bias alters clinical specificity more than clinical sensitivity. The rationale for these propositions are presented along with a step-by-step procedure for estimating bias tolerance limits. These concepts are illustrated with an example using prostate-specific antigen. A second protocol is provided to define analytic imprecision tolerance limits, based on the quality control performance characteristics required to maintain the bias tolerance limits. This model can be applied to most chemistry, immunoassay, and hematologic quantitative assays. The relationship of this procedure to the published procedures using biologic variation for defining analytic tolerance limits is discussed.

  4. Controlling Guessing Bias in the Dichotomous Rasch Model Applied to a Large-Scale, Vertically Scaled Testing Program

    Science.gov (United States)

    Andrich, David; Marais, Ida; Humphry, Stephen Mark

    2016-01-01

    Recent research has shown how the statistical bias in Rasch model difficulty estimates induced by guessing in multiple-choice items can be eliminated. Using vertical scaling of a high-profile national reading test, it is shown that the dominant effect of removing such bias is a nonlinear change in the unit of scale across the continuum. The…

  5. Mutation-selection models of codon substitution and their use to estimate selective strengths on codon usage

    DEFF Research Database (Denmark)

    Yang, Ziheng; Nielsen, Rasmus

    2008-01-01

    Current models of codon substitution are formulated at the levels of nucleotide substitution and do not explicitly consider the separate effects of mutation and selection. They are thus incapable of inferring whether mutation or selection is responsible for evolution at silent sites. Here we...... to examine the null hypothesis that codon usage is due to mutation bias alone, not influenced by natural selection. Application of the test to the mammalian data led to rejection of the null hypothesis in most genes, suggesting that natural selection may be a driving force in the evolution of synonymous...... codon usage in mammals. Estimates of selection coefficients nevertheless suggest that selection on codon usage is weak and most mutations are nearly neutral. The sensitivity of the analysis on the assumed mutation model is discussed....

  6. Impact of surface wind biases on the Antarctic sea ice concentration budget in climate models

    Science.gov (United States)

    Lecomte, O.; Goosse, H.; Fichefet, T.; Holland, P. R.; Uotila, P.; Zunz, V.; Kimura, N.

    2016-09-01

    We derive the terms in the Antarctic sea ice concentration budget from the output of three models, and compare them to observations of the same terms. Those models include two climate models from the 5th Coupled Model Intercomparison Project (CMIP5) and one ocean-sea ice coupled model with prescribed atmospheric forcing. Sea ice drift and wind fields from those models, in average over April-October 1992-2005, all exhibit large differences with the available observational or reanalysis datasets. However, the discrepancies between the two distinct ice drift products or the two wind reanalyses used here are sometimes even greater than those differences. Two major findings stand out from the analysis. Firstly, large biases in sea ice drift speed and direction in exterior sectors of the sea ice covered region tend to be systematic and consistent with those in winds. This suggests that sea ice errors in these areas are most likely wind-driven, so as errors in the simulated ice motion vectors. The systematic nature of these biases is less prominent in interior sectors, nearer the coast, where sea ice is mechanically constrained and its motion in response to the wind forcing more depending on the model rheology. Second, the intimate relationship between winds, sea ice drift and the sea ice concentration budget gives insight on ways to categorize models with regard to errors in their ice dynamics. In exterior regions, models with seemingly too weak winds and slow ice drift consistently yield a lack of ice velocity divergence and hence a wrong wintertime sea ice growth rate. In interior sectors, too slow ice drift, presumably originating from issues in the physical representation of sea ice dynamics as much as from errors in surface winds, leads to wrong timing of the late winter ice retreat. Those results illustrate that the applied methodology provides a valuable tool for prioritizing model improvements based on the ice concentration budget-ice drift biases-wind biases

  7. MODEL SELECTION FOR SPECTROPOLARIMETRIC INVERSIONS

    Energy Technology Data Exchange (ETDEWEB)

    Asensio Ramos, A.; Manso Sainz, R.; Martinez Gonzalez, M. J.; Socas-Navarro, H. [Instituto de Astrofisica de Canarias, E-38205, La Laguna, Tenerife (Spain); Viticchie, B. [ESA/ESTEC RSSD, Keplerlaan 1, 2200 AG Noordwijk (Netherlands); Orozco Suarez, D., E-mail: aasensio@iac.es [National Astronomical Observatory of Japan, Mitaka, Tokyo 181-8588 (Japan)

    2012-04-01

    Inferring magnetic and thermodynamic information from spectropolarimetric observations relies on the assumption of a parameterized model atmosphere whose parameters are tuned by comparison with observations. Often, the choice of the underlying atmospheric model is based on subjective reasons. In other cases, complex models are chosen based on objective reasons (for instance, the necessity to explain asymmetries in the Stokes profiles) but it is not clear what degree of complexity is needed. The lack of an objective way of comparing models has, sometimes, led to opposing views of the solar magnetism because the inferred physical scenarios are essentially different. We present the first quantitative model comparison based on the computation of the Bayesian evidence ratios for spectropolarimetric observations. Our results show that there is not a single model appropriate for all profiles simultaneously. Data with moderate signal-to-noise ratios (S/Ns) favor models without gradients along the line of sight. If the observations show clear circular and linear polarization signals above the noise level, models with gradients along the line are preferred. As a general rule, observations with large S/Ns favor more complex models. We demonstrate that the evidence ratios correlate well with simple proxies. Therefore, we propose to calculate these proxies when carrying out standard least-squares inversions to allow for model comparison in the future.

  8. Effects of model chemistry and data biases on stratospheric ozone assimilation

    Directory of Open Access Journals (Sweden)

    L. Coy

    2007-06-01

    Full Text Available The innovations or observation minus forecast (O–F residuals produced by a data assimilation system provide a convenient metric of evaluating global analyses. In this study, O–F statistics from the Global Ozone Assimilation Testing System (GOATS are used to examine how ozone assimilation products and their associated O–F statistics depend on input data biases and ozone photochemistry parameterizations (OPP. All the GOATS results shown are based on a 6-h forecast and analysis cycle using observations from SBUV/2 (Solar Backscatter UltraViolet instrument-2 during September–October 2002. Results show that zonal mean ozone analyses are more independent of observation biases and drifts when using an OPP, while the mean ozone O–Fs are more sensitive to observation drifts when using an OPP. In addition, SD O–Fs (standard deviations are reduced in the upper stratosphere when using an OPP due to a reduction of forecast model noise and to increased covariance between the forecast model and the observations. Experiments that changed the OPP reference state to match the observations by using an "adaptive" OPP scheme reduced the mean ozone O–Fs at the expense of zonal mean ozone analyses being more susceptible to data biases and drifts. Additional experiments showed that the upper boundary of the ozone DAS can affect the quality of the ozone analysis and therefore should be placed well above (at least a scale height the region of interest.

  9. Bias correction of temperature and precipitation data for regional climate model application to the Rhine basin

    Directory of Open Access Journals (Sweden)

    W. Terink

    2009-08-01

    Full Text Available In many climate impact studies hydrological models are forced with meteorological forcing data without an attempt to assess the quality of these forcing data. The objective of this study is to compare downscaled ERA15 (ECMWF-reanalysis data precipitation and temperature with observed precipitation and temperature and apply a bias correction to these forcing variables. The bias-corrected precipitation and temperature data will be used in another study as input for the Variable Infiltration Capacity (VIC model. Observations were available for 134 sub-basins throughout the Rhine basin at a temporal resolution of one day from the International Commission for the Hydrology of the Rhine basin (CHR. Precipitation is corrected by fitting the mean and coefficient of variation (CV of the observations. Temperature is corrected by fitting the mean and standard deviation of the observations. It seems that the uncorrected ERA15 is too warm and too wet for most of the Rhine basin. The bias correction leads to satisfactory results, precipitation and temperature differences decreased significantly. Corrections were largest during summer for both precipitation and temperature, and for September and October for precipitation only. Besides the statistics the correction method was intended to correct for, it is also found to improve the correlations for the fraction of wet days and lag-1 autocorrelations between ERA15 and the observations.

  10. Spatial But Not Oculomotor Information Biases Perceptual Memory: Evidence From Face Perception and Cognitive Modeling.

    Science.gov (United States)

    Wantz, Andrea L; Lobmaier, Janek S; Mast, Fred W; Senn, Walter

    2017-08-01

    Recent research put forward the hypothesis that eye movements are integrated in memory representations and are reactivated when later recalled. However, "looking back to nothing" during recall might be a consequence of spatial memory retrieval. Here, we aimed at distinguishing between the effect of spatial and oculomotor information on perceptual memory. Participants' task was to judge whether a morph looked rather like the first or second previously presented face. Crucially, faces and morphs were presented in a way that the morph reactivated oculomotor and/or spatial information associated with one of the previously encoded faces. Perceptual face memory was largely influenced by these manipulations. We considered a simple computational model with an excellent match (4.3% error) that expresses these biases as a linear combination of recency, saccade, and location. Surprisingly, saccades did not play a role. The results suggest that spatial and temporal rather than oculomotor information biases perceptual face memory. Copyright © 2016 Cognitive Science Society, Inc.

  11. Unraveling the sub-processes of selective attention: insights from dynamic modeling and continuous behavior.

    Science.gov (United States)

    Frisch, Simon; Dshemuchadse, Maja; Görner, Max; Goschke, Thomas; Scherbaum, Stefan

    2015-11-01

    Selective attention biases information processing toward stimuli that are relevant for achieving our goals. However, the nature of this bias is under debate: Does it solely rely on the amplification of goal-relevant information or is there a need for additional inhibitory processes that selectively suppress currently distracting information? Here, we explored the processes underlying selective attention with a dynamic, modeling-based approach that focuses on the continuous evolution of behavior over time. We present two dynamic neural field models incorporating the diverging theoretical assumptions. Simulations with both models showed that they make similar predictions with regard to response times but differ markedly with regard to their continuous behavior. Human data observed via mouse tracking as a continuous measure of performance revealed evidence for the model solely based on amplification but no indication of persisting selective distracter inhibition.

  12. Modeling Systematic Change in Stopover Duration Does Not Improve Bias in Trends Estimated from Migration Counts.

    Directory of Open Access Journals (Sweden)

    Tara L Crewe

    Full Text Available The use of counts of unmarked migrating animals to monitor long term population trends assumes independence of daily counts and a constant rate of detection. However, migratory stopovers often last days or weeks, violating the assumption of count independence. Further, a systematic change in stopover duration will result in a change in the probability of detecting individuals once, but also in the probability of detecting individuals on more than one sampling occasion. We tested how variation in stopover duration influenced accuracy and precision of population trends by simulating migration count data with known constant rate of population change and by allowing daily probability of survival (an index of stopover duration to remain constant, or to vary randomly, cyclically, or increase linearly over time by various levels. Using simulated datasets with a systematic increase in stopover duration, we also tested whether any resulting bias in population trend could be reduced by modeling the underlying source of variation in detection, or by subsampling data to every three or five days to reduce the incidence of recounting. Mean bias in population trend did not differ significantly from zero when stopover duration remained constant or varied randomly over time, but bias and the detection of false trends increased significantly with a systematic increase in stopover duration. Importantly, an increase in stopover duration over time resulted in a compounding effect on counts due to the increased probability of detection and of recounting on subsequent sampling occasions. Under this scenario, bias in population trend could not be modeled using a covariate for stopover duration alone. Rather, to improve inference drawn about long term population change using counts of unmarked migrants, analyses must include a covariate for stopover duration, as well as incorporate sampling modifications (e.g., subsampling to reduce the probability that individuals will

  13. Anodal transcranial direct current stimulation relieves the unilateral bias of a rat model of Parkinson's disease.

    Science.gov (United States)

    Li, Yiyan; Tian, Xulong; Qian, Long; Yu, Xuehong; Jiang, Weiwei

    2011-01-01

    The unilaterally lesioned rat model of Parkinson's disease which fails to orient to the food stimuli presented on the contralateral side of its preferential side of body could be induced by the injection of 6-hydroxydopamine (6-OHDA) into the medial forebrain bundle (MFB). We employed transcranial direct current stimulation (tDCS, current intensity: 80 μA, and 40 μA; anodal electrode area: 3.14 mm(2); stimulation time: 30 minutes) over the M1 area to relieve the ipsilateral bias in the rat model. A corridor test was set to count the ipsilateral bias of the rats. In this experiment, 30 Sprague-Dawley rats (80 μA: n = 8, 40 μA: n = 8, sham: n = 7, healthy control: n = 7) were chosen for the corridor test and the tDCS session. The lesioned rats exhibited increased ipsilateral bias 4 weeks after the lesion surgery (P < 0.01), and the anodal tDCS with the active electrode on the lesioned side relieved the ipsilateral bias significantly (P < 0.01) immediately after the surgery and the improvement lasted for nearly 1 day. The rats in the group of 80 μA exhibited more significant changes than the 40 μA group after one day. After all the experiments, the histological process showed no neurotrauma led by the tDCS. In conclusion, the modulatory function of the cortical excitability of the tDCS may awaken the compensatory mechanisms and the response mechanisms which modulate the loss of the brain function. Further studies should be done to provide more evidence about the assumption.

  14. Genetic search feature selection for affective modeling

    DEFF Research Database (Denmark)

    Martínez, Héctor P.; Yannakakis, Georgios N.

    2010-01-01

    Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built...

  15. Stability and bifurcation analysis of a vector-bias model of malaria transmission.

    Science.gov (United States)

    Buonomo, Bruno; Vargas-De-León, Cruz

    2013-03-01

    The vector-bias model of malaria transmission, recently proposed by Chamchod and Britton, is considered. Nonlinear stability analysis is performed by means of the Lyapunov theory and the LaSalle Invariance Principle. The classical threshold for the basic reproductive number, R(0), is obtained: if R(0)>1, then the disease will spread and persist within its host population. If R(0)1, the endemic persistence of the disease has been proved to hold also for the extended model. This last result is obtained by means of the geometric approach to global stability. Copyright © 2012 Elsevier Inc. All rights reserved.

  16. Extra-tropical origin of equatorial Pacific cold bias in climate models with links to cloud albedo

    Science.gov (United States)

    Burls, Natalie J.; Muir, Leslie; Vincent, Emmanuel M.; Fedorov, Alexey

    2017-09-01

    General circulation models frequently suffer from a substantial cold bias in equatorial Pacific sea surface temperatures (SSTs). For instance, the majority of the climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) have this particular problem (17 out of the 26 models evaluated in the present study). Here, we investigate the extent to which these equatorial cold biases are related to mean climate biases generated in the extra-tropics and then communicated to the equator via the oceanic subtropical cells (STCs). With an evident relationship across the CMIP5 models between equatorial SSTs and upper ocean temperatures in the extra-tropical subduction regions, our analysis suggests that cold SST biases within the extra-tropical Pacific indeed translate into a cold equatorial bias via the STCs. An assessment of the relationship between these extra-tropical SST biases and local surface heat flux components indicates a link to biases in the simulated shortwave fluxes. Further sensitivity studies with a climate model (CESM) in which extra-tropical cloud albedo is systematically varied illustrate the influence of cloud albedo perturbations, not only directly above the oceanic subduction regions but across the extra-tropics, on the equatorial bias. The CESM experiments reveal a quadratic relationship between extra-tropical Pacific albedo and the root-mean-square-error in equatorial SSTs—a relationship with which the CMIP5 models generally agree. Thus, our study suggests that one way to improve the equatorial cold bias in the models is to improve the representation of subtropical and mid-latitude cloud albedo.

  17. Extra-tropical origin of equatorial Pacific cold bias in climate models with links to cloud albedo

    Science.gov (United States)

    Burls, Natalie J.; Muir, Leslie; Vincent, Emmanuel M.; Fedorov, Alexey

    2016-11-01

    General circulation models frequently suffer from a substantial cold bias in equatorial Pacific sea surface temperatures (SSTs). For instance, the majority of the climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) have this particular problem (17 out of the 26 models evaluated in the present study). Here, we investigate the extent to which these equatorial cold biases are related to mean climate biases generated in the extra-tropics and then communicated to the equator via the oceanic subtropical cells (STCs). With an evident relationship across the CMIP5 models between equatorial SSTs and upper ocean temperatures in the extra-tropical subduction regions, our analysis suggests that cold SST biases within the extra-tropical Pacific indeed translate into a cold equatorial bias via the STCs. An assessment of the relationship between these extra-tropical SST biases and local surface heat flux components indicates a link to biases in the simulated shortwave fluxes. Further sensitivity studies with a climate model (CESM) in which extra-tropical cloud albedo is systematically varied illustrate the influence of cloud albedo perturbations, not only directly above the oceanic subduction regions but across the extra-tropics, on the equatorial bias. The CESM experiments reveal a quadratic relationship between extra-tropical Pacific albedo and the root-mean-square-error in equatorial SSTs—a relationship with which the CMIP5 models generally agree. Thus, our study suggests that one way to improve the equatorial cold bias in the models is to improve the representation of subtropical and mid-latitude cloud albedo.

  18. Comparing the impact of time displaced and biased precipitation estimates for online updated urban runoff models

    DEFF Research Database (Denmark)

    2013-01-01

    When an online runoff model is updated from system measurements, the requirements of the precipitation input change. Using rain gauge data as precipitation input there will be a displacement between the time when the rain hits the gauge and the time where the rain hits the actual catchment, due...... to the time it takes for the rain cell to travel from the rain gauge to the catchment. Since this time displacement is not present for system measurements the data assimilation scheme might already have updated the model to include the impact from the particular rain cell when the rain data is forced upon...... the model, which therefore will end up including the same rain twice in the model run. This paper compares forecast accuracy of updated models when using time displaced rain input to that of rain input with constant biases. This is done using a simple time-area model and historic rain series that are either...

  19. Understanding our love affair with p-chlorophenyl: present day implications from historical biases of reagent selection.

    Science.gov (United States)

    Brown, Dean G; Gagnon, Moriah M; Boström, Jonas

    2015-03-12

    We report here an unexpectedly strong preference toward para substitution in phenyl rings within drug discovery programs. A population analysis of aromatic rings in various drug databases demonstrated that para substitution is favored over meta and ortho regioisomers, with p-chlorophenyl (p-ClPh) being one of the most predominant examples. We speculate that the frequency of p-ClPh is traced back to historical models of medicinal chemistry where para-substituted regioisomers were perhaps more easily accessed, and further reinforced by Topliss in 1972 that if Ph was active, the p-ClPh should be made because of ease of synthesis and hydrophobicity driven potency effects. On the basis of our analysis, the para bias has become useful conventional wisdom but perhaps so much so that a perception has been created that druglike space favors a linear aromatic structure. It is hoped this analysis will catalyze a new look at design of reagent databases and screening collections.

  20. Climate projections of future extreme events accounting for modelling uncertainties and historical simulation biases

    Science.gov (United States)

    Brown, Simon J.; Murphy, James M.; Sexton, David M. H.; Harris, Glen R.

    2014-11-01

    A methodology is presented for providing projections of absolute future values of extreme weather events that takes into account key uncertainties in predicting future climate. This is achieved by characterising both observed and modelled extremes with a single form of non-stationary extreme value (EV) distribution that depends on global mean temperature and which includes terms that account for model bias. Such a distribution allows the prediction of future "observed" extremes for any period in the twenty-first century. Uncertainty in modelling future climate, arising from a wide range of atmospheric, oceanic, sulphur cycle and carbon cycle processes, is accounted for by using probabilistic distributions of future global temperature and EV parameters. These distributions are generated by Bayesian sampling of emulators with samples weighted by their likelihood with respect to a set of observational constraints. The emulators are trained on a large perturbed parameter ensemble of global simulations of the recent past, and the equilibrium response to doubled CO2. Emulated global EV parameters are converted to the relevant regional scale through downscaling relationships derived from a smaller perturbed parameter regional climate model ensemble. The simultaneous fitting of the EV model to regional model data and observations allows the characterisation of how observed extremes may change in the future irrespective of biases that may be present in the regional models simulation of the recent past climate. The clearest impact of a parameter perturbation in this ensemble was found to be the depth to which plants can access water. Members with shallow soils tend to be biased hot and dry in summer for the observational period. These biases also appear to have an impact on the potential future response for summer temperatures with some members with shallow soils having increases for extremes that reduce with extreme severity. We apply this methodology for London, using the

  1. Using propensity scores to adjust for selection bias when assessing the effectiveness of Alcoholics Anonymous in observational studies.

    Science.gov (United States)

    Ye, Yu; Kaskutas, Lee Ann

    2009-09-01

    The effectiveness of Alcoholics Anonymous (AA) is difficult to establish. Observational studies consistently find strong dose-response relationships between AA meeting attendance and abstinence, and the only experimental studies favoring AA have been of 12-step facilitation treatment rather than of AA per se. Pending future randomized trials, this paper uses propensity score (PS) method to address the selection bias that potentially confounds the effect of AA in observational studies. The study followed a treatment sample for 1 year to assess post-treatment AA attendance and abstinence (n=569). Propensity scores were constructed based on known confounders including motivation, problem severity, and prior help-seeking. AA attendance during the 12-month follow-up period was studied as a predictor of alcohol abstinence for 30 days prior to the follow-up interview. PS stratification and PS matching techniques were used to adjust for the self-select bias associated with respondents' propensity to attend AA. The overall advantage in abstinence initially observed narrowed when adjusted. The odds ratio associated with AA attendance reduced from 3.6 to 3.0 after PS stratification and 2.6 after PS matching to AA-attenders. Support for AA effectiveness was strengthened in the quintile with lower propensity scores and when AA-nonattenders were matched as the target group, but was weakened among those in the higher PS quintiles and when matching to AA-attenders. These results confirm the robustness of AA effectiveness overall, because the results for higher abstinence associated with AA attendance following propensity score adjustment remained significant, and the reduction in the magnitude of AA's effect was moderate. However, the effect modification by propensity scores in both PS stratification and PS matching approaches seems to suggest that AA may be most helpful, or matter more, for those with a lower propensity to attend AA. Conversely, for those with a high propensity

  2. On the wintertime low bias of Northern Hemisphere carbon monoxide in global model studies

    Science.gov (United States)

    Stein, O.; Schultz, M. G.; Bouarar, I.; Clark, H.; Huijnen, V.; Gaudel, A.; George, M.; Clerbaux, C.

    2014-01-01

    The uncertainties in the global budget of carbon monoxide (CO) are assessed to explain causes for the long-standing issue of Northern Hemispheric wintertime underestimation of CO concentrations in global models. With a series of MOZART sensitivity simulations for the year 2008, the impacts from changing a variety of surface sources and sinks were analyzed. The model results were evaluated with monthly averages of surface station observations from the global CO monitoring network as well as with total columns observed from satellites and with vertical profiles from measurements on passenger aircraft. Our basic simulation using MACCity anthropogenic emissions underestimated Northern Hemispheric near-surface CO concentrations on average by more than 20 ppb from December to April with the largest bias over Europe of up to 75 ppb in January. An increase in global biomass burning or biogenic emissions of CO or volatile organic compounds (VOC) is not able to reduce the annual course of the model bias and yields too high concentrations over the Southern Hemisphere. Raising global annual anthropogenic emissions results in overestimations of surface concentrations in most regions all-year-round. Instead, our results indicate that anthropogenic emissions in the MACCity inventory are too low for the industrialized countries during winter and spring. Thus we found it necessary to adjust emissions seasonally with regionally varying scaling factors. Moreover, exchanging the original resistance-type dry deposition scheme with a parameterization for CO uptake by oxidation from soil bacteria and microbes reduced the boreal winter dry deposition fluxes and could partly correct for the model bias. When combining the modified dry deposition scheme with increased wintertime road traffic emissions over Europe and North America (factors up to 4.5 and 2, respectively) we were able to optimize the match to surface observations and to reduce the model bias significantly with respect to the

  3. On the wintertime low bias of Northern Hemisphere carbon monoxide in global model studies

    Directory of Open Access Journals (Sweden)

    O. Stein

    2014-01-01

    Full Text Available The uncertainties in the global budget of carbon monoxide (CO are assessed to explain causes for the long-standing issue of Northern Hemispheric wintertime underestimation of CO concentrations in global models. With a series of MOZART sensitivity simulations for the year 2008, the impacts from changing a variety of surface sources and sinks were analyzed. The model results were evaluated with monthly averages of surface station observations from the global CO monitoring network as well as with total columns observed from satellites and with vertical profiles from measurements on passenger aircraft. Our basic simulation using MACCity anthropogenic emissions underestimated Northern Hemispheric near-surface CO concentrations on average by more than 20 ppb from December to April with the largest bias over Europe of up to 75 ppb in January. An increase in global biomass burning or biogenic emissions of CO or volatile organic compounds (VOC is not able to reduce the annual course of the model bias and yields too high concentrations over the Southern Hemisphere. Raising global annual anthropogenic emissions results in overestimations of surface concentrations in most regions all-year-round. Instead, our results indicate that anthropogenic emissions in the MACCity inventory are too low for the industrialized countries during winter and spring. Thus we found it necessary to adjust emissions seasonally with regionally varying scaling factors. Moreover, exchanging the original resistance-type dry deposition scheme with a parameterization for CO uptake by oxidation from soil bacteria and microbes reduced the boreal winter dry deposition fluxes and could partly correct for the model bias. When combining the modified dry deposition scheme with increased wintertime road traffic emissions over Europe and North America (factors up to 4.5 and 2, respectively we were able to optimize the match to surface observations and to reduce the model bias significantly with

  4. Improving the kinetics from molecular simulations using biased Markov state models

    Science.gov (United States)

    Rudzinski, Joseph F.; Kremer, Kurt; Bereau, Tristan

    Molecular simulations can provide microscopic insight into the physical and chemical driving forces of complex molecular processes. Despite continued advancement of simulation methodology, model errors may lead to inconsistencies between simulated and experimentally-measured observables. This work presents a robust and systematic framework for reweighting the ensemble of dynamical paths sampled in a molecular simulation in order to ensure consistency with a set of given kinetic observables. The method employs the well-developed Markov state modeling framework in order to efficiently treat simulated dynamical paths. We demonstrate that, for two distinct coarse-grained peptide models, biasing the Markov state model to reproduce a small number of reference kinetic constraints significantly improves the dynamical properties of the model, while simultaneously refining the static equilibrium properties.

  5. Bias in diet determination: incorporating traditional methods in Bayesian mixing models.

    Science.gov (United States)

    Franco-Trecu, Valentina; Drago, Massimiliano; Riet-Sapriza, Federico G; Parnell, Andrew; Frau, Rosina; Inchausti, Pablo

    2013-01-01

    There are not "universal methods" to determine diet composition of predators. Most traditional methods are biased because of their reliance on differential digestibility and the recovery of hard items. By relying on assimilated food, stable isotope and Bayesian mixing models (SIMMs) resolve many biases of traditional methods. SIMMs can incorporate prior information (i.e. proportional diet composition) that may improve the precision in the estimated dietary composition. However few studies have assessed the performance of traditional methods and SIMMs with and without informative priors to study the predators' diets. Here we compare the diet compositions of the South American fur seal and sea lions obtained by scats analysis and by SIMMs-UP (uninformative priors) and assess whether informative priors (SIMMs-IP) from the scat analysis improved the estimated diet composition compared to SIMMs-UP. According to the SIMM-UP, while pelagic species dominated the fur seal's diet the sea lion's did not have a clear dominance of any prey. In contrast, SIMM-IP's diets compositions were dominated by the same preys as in scat analyses. When prior information influenced SIMMs' estimates, incorporating informative priors improved the precision in the estimated diet composition at the risk of inducing biases in the estimates. If preys isotopic data allow discriminating preys' contributions to diets, informative priors should lead to more precise but unbiased estimated diet composition. Just as estimates of diet composition obtained from traditional methods are critically interpreted because of their biases, care must be exercised when interpreting diet composition obtained by SIMMs-IP. The best approach to obtain a near-complete view of predators' diet composition should involve the simultaneous consideration of different sources of partial evidence (traditional methods, SIMM-UP and SIMM-IP) in the light of natural history of the predator species so as to reliably ascertain and

  6. Model selection for amplitude analysis

    CERN Document Server

    Guegan, Baptiste; Stevens, Justin; Williams, Mike

    2015-01-01

    Model complexity in amplitude analyses is often a priori under-constrained since the underlying theory permits a large number of amplitudes to contribute to most physical processes. The use of an overly complex model results in reduced predictive power and worse resolution on unknown parameters of interest. Therefore, it is common to reduce the complexity by removing from consideration some subset of the allowed amplitudes. This paper studies a data-driven method for limiting model complexity through regularization during regression in the context of a multivariate (Dalitz-plot) analysis. The regularization technique applied greatly improves the performance. A method is also proposed for obtaining the significance of a resonance in a multivariate amplitude analysis.

  7. Forest fire risk assessment in Sweden using climate model data: bias correction and future changes

    Directory of Open Access Journals (Sweden)

    W. Yang

    2015-01-01

    Full Text Available As the risk for a forest fire is largely influenced by weather, evaluating its tendency under a changing climate becomes important for management and decision making. Currently, biases in climate models make it difficult to realistically estimate the future climate and consequent impact on fire risk. A distribution-based scaling (DBS approach was developed as a post-processing tool that intends to correct systematic biases in climate modelling outputs. In this study, we used two projections, one driven by historical reanalysis (ERA40 and one from a global climate model (ECHAM5 for future projection, both having been dynamically downscaled by a regional climate model (RCA3. The effects of the post-processing tool on relative humidity and wind speed were studied in addition to the primary variables precipitation and temperature. Finally, the Canadian Fire Weather Index system was used to evaluate the influence of changing meteorological conditions on the moisture content in fuel layers and the fire-spread risk. The forest fire risk results using DBS are proven to better reflect risk using observations than that using raw climate outputs. For future periods, southern Sweden is likely to have a higher fire risk than today, whereas northern Sweden will have a lower risk of forest fire.

  8. Reducing biases in regional climate downscaling by applying Bayesian model averaging on large-scale forcing

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Hongwei [APEC Climate Center, Busan (Korea, Republic of); Wang, Bin [University of Hawaii at Manoa, Department of Meteorology, Honolulu, HI (United States); University of Hawaii at Manoa, International Pacific Research Center, Honolulu, HI (United States); Wang, Bin [Chinese Academy of Sciences, LASG, Institute of Atmospheric Physics, Beijing (China)

    2012-11-15

    Reduction of uncertainty in large-scale lateral-boundary forcing in regional climate modeling is a critical issue for improving the performance of regional climate downscaling. Numerical simulations of 1998 East Asian summer monsoon were conducted using the Weather Research and Forecast model forced by four different reanalysis datasets, their equal-weight ensemble, and Bayesian model averaging (BMA) ensemble means. Large discrepancies were found among experiments forced by the four individual reanalysis datasets mainly due to the uncertainties in the moisture field of large-scale forcing over ocean. We used satellite water-vapor-path data as observed truth-and-training data to determine the posterior probability (weight) for each forcing dataset using the BMA method. The experiment forced by the equal-weight ensemble reduced the circulation biases significantly but reduced the precipitation biases only moderately. However, the experiment forced by the BMA ensemble outperformed not only the experiments forced by individual reanalysis datasets but also the equal-weight ensemble experiment in simulating the seasonal mean circulation and precipitation. These results suggest that the BMA ensemble method is an effective method for reducing the uncertainties in lateral-boundary forcing and improving model performance in regional climate downscaling. (orig.)

  9. Biased random key genetic algorithm with insertion and gender selection for capacitated vehicle routing problem with time windows

    Science.gov (United States)

    Rochman, Auliya Noor; Prasetyo, Hari; Nugroho, Munajat Tri

    2017-06-01

    Vehicle Routing Problem (VRP) often occurs when the manufacturers need to distribute their product to some customers/outlets. The distribution process is typically restricted by the capacity of the vehicle and the working hours at the distributor. This type of VRP is also known as Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). A Biased Random Key Genetic Algorithm (BRKGA) was designed and coded in MATLAB to solve the CVRPTW case of soft drink distribution. The standard BRKGA was then modified by applying chromosome insertion into the initial population and defining chromosome gender for parent undergoing crossover operation. The performance of the established algorithms was then compared to a heuristic procedure for solving a soft drink distribution. Some findings are revealed (1) the total distribution cost of BRKGA with insertion (BRKGA-I) results in a cost saving of 39% compared to the total cost of heuristic method, (2) BRKGA with the gender selection (BRKGA-GS) could further improve the performance of the heuristic method. However, the BRKGA-GS tends to yield worse results compared to that obtained from the standard BRKGA.

  10. Looking to the Future: Incorporating Genomic Information into Disparities Research to Reduce Measurement Error and Selection Bias

    Science.gov (United States)

    Shields, Alexandra E; Crown, William H

    2012-01-01

    Objective To extend recent conceptual and methodological advances in disparities research to include the incorporation of genomic information in analyses of racial/ethnic disparities in health care and health outcomes. Data Sources Published literature on human genetic variation, the role of genetics in disease and response to treatment, and methodological developments in disparities research. Study Design We present a conceptual framework for incorporating genomic information into the Institute of Medicine definition of racial/ethnic disparities in health care, identify key concepts used in disparities research that can be informed by genomics research, and illustrate the incorporation of genomic information into current methods using the example of HER-2 mutations guiding care for breast cancer. Principal Findings Genomic information has not yet been incorporated into disparities research, though it has direct relevance to concepts of race/ethnicity, health status, appropriate care, and socioeconomic status. The HER-2 example demonstrates how available genetic information can be incorporated into current disparities methods to reduce selection bias and measurement error. Advances in health information infrastructure may soon make standardized genetic information more available to health services researchers. Conclusion Genomic information can refine measurement of racial/ethnic disparities in health care and health outcomes and should be included wherever possible in disparities research. PMID:22515190

  11. Citation bias and selective focus on positive findings in the literature on the serotonin transporter gene (5-HTTLPR), life stress and depression

    NARCIS (Netherlands)

    Vries, de Ymkje Anna; Roest, A. M.; Franzen, M.; Munaf, M. R.; Bastiaansen, J. A.

    2016-01-01

    Background Caspi et al.'s 2003 report that 5-HTTLPR genotype moderates the influence of life stress on depression has been highly influential but remains contentious. We examined whether the evidence base for the 5-HTTLPR-stress interaction has been distorted by citation bias and a selective focus o

  12. Exploring the Links Between Biases in Regional Climate Models and their Representation of Synoptic Circulation Types in the European Alps

    Science.gov (United States)

    Addor, N.; Rohrer, M.; Furrer, R.; Seibert, J.

    2014-12-01

    Climate model simulations can show large departures from observations. These biases are often subtracted and then forgotten, or post-processed for impact modeling using pragmatic methods that typically do not account for the origins of the biases. Yet, enhancing our understanding of the reasons behind these biases is essential for the improvement of climate models and for the design of more robust bias-correction techniques. We pursue these objectives by exploring the links between biases at different spatial scales. We quantify biases at the regional scale in an alpine area (Switzerland) and explore whether they are influenced by the misrepresentation of the frequency, temperature and precipitation of circulation types (CTs) at the synoptic scale. Our analysis relies on simulations from 14 regional climate models (RCMs) forced by general circulation models and produced for the ENSEMBLES project. For each model, we characterized the daily synoptic situation over 1960-2099 by using a CT classification based on a hierarchical cluster analysis of principal components. The classification relies on sea level pressure fields within an area representative of the European Alps. We find significant biases in the CT frequency for 1980-2001 and that some of them lead to biases in temperature and precipitation reported in RCM comparison studies. For instance, in winter, models overestimate both the frequency and precipitation intensity of westerly situations, which carry moist air from the Atlantic Ocean and are responsible for most of the rainfall. We propose this as a main driver for the generalized overestimation of precipitation in winter, which is one of the most concerning biases affecting simulations in our area, as it leads to unrealistic estimates of snow accumulation. A general consequence is that CT-based downscaling methods that do not account for biases in CT frequency will generate biased outputs. Overall, we show that decomposing RCM time series using CTs

  13. Regressions by leaps and bounds and biased estimation techniques in yield modeling

    Science.gov (United States)

    Marquina, N. E. (Principal Investigator)

    1979-01-01

    The author has identified the following significant results. It was observed that OLS was not adequate as an estimation procedure when the independent or regressor variables were involved in multicollinearities. This was shown to cause the presence of small eigenvalues of the extended correlation matrix A'A. It was demonstrated that the biased estimation techniques and the all-possible subset regression could help in finding a suitable model for predicting yield. Latent root regression was an excellent tool that found how many predictive and nonpredictive multicollinearities there were.

  14. Alternatives to accuracy and bias metrics based on percentage errors for radiation belt modeling applications

    Energy Technology Data Exchange (ETDEWEB)

    Morley, Steven Karl [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-07-01

    This report reviews existing literature describing forecast accuracy metrics, concentrating on those based on relative errors and percentage errors. We then review how the most common of these metrics, the mean absolute percentage error (MAPE), has been applied in recent radiation belt modeling literature. Finally, we describe metrics based on the ratios of predicted to observed values (the accuracy ratio) that address the drawbacks inherent in using MAPE. Specifically, we define and recommend the median log accuracy ratio as a measure of bias and the median symmetric accuracy as a measure of accuracy.

  15. Thermodynamic constitutive model for load-biased thermal cycling test of shape memory alloy

    Energy Technology Data Exchange (ETDEWEB)

    Young, Sung, E-mail: ysy@kut.ac.kr [Korea University of Technology and Education, Chonan (Korea, Republic of); Nam, Tae-Hyun, E-mail: tahynam@gnu.ac.kr [School of Materials Science and Engineering and ERI, Gyeongsang National University, 900 Gazwadong, Jinju, Gyeongnam 660-701 (Korea, Republic of)

    2013-12-15

    Graphical abstract: - Highlights: • Thermodynamic calculation model for martensitic transformation of shape memory alloy was proposed. • Evolution of the self-accommodation was considered independently by a rate-dependent kinetic equation. • Finite element calculation was conducted for B2–B19′ transformation of Ti–44.5Ni–5Cu–0.5 V (at.%). • Three-dimensional numerical results predict the macroscopic strain under bias loading accurately. - Abstract: This paper presents a three-dimensional calculation model for martensitic phase transformation of shape memory alloy. Constitutive model based on thermodynamic theory was provided. The average behavior was accounted for by considering the volume fraction of each martensitic variant in the material. Evolution of the volume fraction of each variant was determined by a rate-dependent kinetic equation. We assumed that nucleation rate is faster for the self-accommodation than for the stress-induced variants. Three-dimensional finite element analysis was conducted and the results were compared with the experimental data of Ti–44.5Ni–5Cu–0.5 V (at.%) alloy under bias loading.

  16. Models of cultural niche construction with selection and assortative mating.

    Science.gov (United States)

    Creanza, Nicole; Fogarty, Laurel; Feldman, Marcus W

    2012-01-01

    Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. Here we develop a model of this process and suggest some applications of this model. We examine the interactions between cultural transmission, selection, and assorting, paying particular attention to the complexities that arise when selection and assorting are both present, in which case stable polymorphisms of all cultural phenotypes are possible. We compare our model to a recent model for the joint evolution of religion and fertility and discuss other potential applications of cultural niche construction theory, including the evolution and maintenance of large-scale human conflict and the relationship between sex ratio bias and marriage customs. The evolutionary framework we introduce begins to address complexities that arise in the quantitative analysis of multiple interacting cultural traits.

  17. Models of cultural niche construction with selection and assortative mating.

    Directory of Open Access Journals (Sweden)

    Nicole Creanza

    Full Text Available Niche construction is a process through which organisms modify their environment and, as a result, alter the selection pressures on themselves and other species. In cultural niche construction, one or more cultural traits can influence the evolution of other cultural or biological traits by affecting the social environment in which the latter traits may evolve. Cultural niche construction may include either gene-culture or culture-culture interactions. Here we develop a model of this process and suggest some applications of this model. We examine the interactions between cultural transmission, selection, and assorting, paying particular attention to the complexities that arise when selection and assorting are both present, in which case stable polymorphisms of all cultural phenotypes are possible. We compare our model to a recent model for the joint evolution of religion and fertility and discuss other potential applications of cultural niche construction theory, including the evolution and maintenance of large-scale human conflict and the relationship between sex ratio bias and marriage customs. The evolutionary framework we introduce begins to address complexities that arise in the quantitative analysis of multiple interacting cultural traits.

  18. Bayesian nonparametric centered random effects models with variable selection.

    Science.gov (United States)

    Yang, Mingan

    2013-03-01

    In a linear mixed effects model, it is common practice to assume that the random effects follow a parametric distribution such as a normal distribution with mean zero. However, in the case of variable selection, substantial violation of the normality assumption can potentially impact the subset selection and result in poor interpretation and even incorrect results. In nonparametric random effects models, the random effects generally have a nonzero mean, which causes an identifiability problem for the fixed effects that are paired with the random effects. In this article, we focus on a Bayesian method for variable selection. We characterize the subject-specific random effects nonparametrically with a Dirichlet process and resolve the bias simultaneously. In particular, we propose flexible modeling of the conditional distribution of the random effects with changes across the predictor space. The approach is implemented using a stochastic search Gibbs sampler to identify subsets of fixed effects and random effects to be included in the model. Simulations are provided to evaluate and compare the performance of our approach to the existing ones. We then apply the new approach to a real data example, cross-country and interlaboratory rodent uterotrophic bioassay.

  19. Assessment of inter-model variability and biases of the global water cycle in CMIP3 climate models

    CERN Document Server

    Liepert, Beate G

    2011-01-01

    Observed changes such as increasing global temperatures and the intensification of the global water cycle in the 20th century are also robust results of coupled general circulation models. In spite of this success model-to-model variability and biases that are small in first order climate responses however, have implications for climate predictability especially when multi-model means are used. We show that most climate simulations of 20th and 21st century A2 scenario performed with IPCC-AR4 models have deficiencies in simulating the global atmospheric moisture balance. Large biases of only a few models affect the multi-model mean global moisture budget and an imbalanced flux of -0.14 Sv exists whereas the multi-model median imbalance is only -0.02 Sv. For most models, the detected imbalances furthermore change over time. As a consequence, in 13 of the 18 IPCC-AR4 models examined, global annual mean precipitation exceeds global evaporation, indicating that there should be a "leaking" of moisture from the atmo...

  20. Random Effect and Latent Variable Model Selection

    CERN Document Server

    Dunson, David B

    2008-01-01

    Presents various methods for accommodating model uncertainty in random effects and latent variable models. This book focuses on frequentist likelihood ratio and score tests for zero variance components. It also focuses on Bayesian methods for random effects selection in linear mixed effects and generalized linear mixed models

  1. Lack of blinding of outcome assessors in animal model experiments implies risk of observer bias

    DEFF Research Database (Denmark)

    Bello, Segun; Krogsbøll, Lasse T; Gruber, Jan

    2014-01-01

    and caused by three pesticides experiments with very large observer bias, pooled ROR was 0.20 (95% CI, 0.07, 0.59) in contrast to the pooled ROR in the other seven experiments, 0.82 (95% CI, 0.57, 1.17). CONCLUSION: Lack of blinding of outcome assessors in animal model experiments with subjective outcomes......OBJECTIVES: To examine the impact of not blinding outcome assessors on estimates of intervention effects in animal experiments modeling human clinical conditions. STUDY DESIGN AND SETTING: We searched PubMed, Biosis, Google Scholar, and HighWire Press and included animal model experiments with both...... blinded and nonblinded outcome assessors. For each experiment, we calculated the ratio of odds ratios (ROR), that is, the odds ratio (OR) from nonblinded assessments relative to the corresponding OR from blinded assessments. We standardized the ORs according to the experimental hypothesis...

  2. Review and selection of unsaturated flow models

    Energy Technology Data Exchange (ETDEWEB)

    Reeves, M.; Baker, N.A.; Duguid, J.O. [INTERA, Inc., Las Vegas, NV (United States)

    1994-04-04

    Since the 1960`s, ground-water flow models have been used for analysis of water resources problems. In the 1970`s, emphasis began to shift to analysis of waste management problems. This shift in emphasis was largely brought about by site selection activities for geologic repositories for disposal of high-level radioactive wastes. Model development during the 1970`s and well into the 1980`s focused primarily on saturated ground-water flow because geologic repositories in salt, basalt, granite, shale, and tuff were envisioned to be below the water table. Selection of the unsaturated zone at Yucca Mountain, Nevada, for potential disposal of waste began to shift model development toward unsaturated flow models. Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M&O) has the responsibility to review, evaluate, and document existing computer models; to conduct performance assessments; and to develop performance assessment models, where necessary. This document describes the CRWMS M&O approach to model review and evaluation (Chapter 2), and the requirements for unsaturated flow models which are the bases for selection from among the current models (Chapter 3). Chapter 4 identifies existing models, and their characteristics. Through a detailed examination of characteristics, Chapter 5 presents the selection of models for testing. Chapter 6 discusses the testing and verification of selected models. Chapters 7 and 8 give conclusions and make recommendations, respectively. Chapter 9 records the major references for each of the models reviewed. Appendix A, a collection of technical reviews for each model, contains a more complete list of references. Finally, Appendix B characterizes the problems used for model testing.

  3. Adjusting multistate capture-recapture models for misclassification bias: manatee breeding proportions

    Science.gov (United States)

    Kendall, W.L.; Hines, J.E.; Nichols, J.D.

    2003-01-01

    Matrix population models are important tools for research and management of populations. Estimating the parameters of these models is an important step in applying them to real populations. Multistate capture-recapture methods have provided a useful means for estimating survival and parameters of transition between locations or life history states but have mostly relied on the assumption that the state occupied by each detected animal is known with certainty. Nevertheless, in some cases animals can be misclassified. Using multiple capture sessions within each period of interest, we developed a method that adjusts estimates of transition probabilities for bias due to misclassification. We applied this method to 10 years of sighting data for a population of Florida manatees (Trichechus manatus latirostris) in order to estimate the annual probability of transition from nonbreeding to breeding status. Some sighted females were unequivocally classified as breeders because they were clearly accompanied by a first-year calf. The remainder were classified, sometimes erroneously, as nonbreeders because an attendant first-year calf was not observed or was classified as more than one year old. We estimated a conditional breeding probability of 0.31 + 0.04 (estimate + 1 SE) when we ignored misclassification bias, and 0.61 + 0.09 when we accounted for misclassification.

  4. A decision tree algorithm for investigation of model biases related to dynamical cores and physical parameterizations.

    Science.gov (United States)

    Soner Yorgun, M; Rood, Richard B

    2016-12-01

    An object-based evaluation method using a pattern recognition algorithm (i.e., classification trees) is applied to the simulated orographic precipitation for idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and the Eulerian spectral transform dynamical cores with varying resolutions. Daily simulations were analyzed and three different types of precipitation features were identified by the classification tree algorithm. The statistical characteristics of these features (i.e., maximum value, mean value, and variance) were calculated to quantify the difference between the dynamical cores and changing resolutions. Even with the simple and smooth topography in the idealized setups, complexity in the precipitation fields simulated by the models develops quickly. The classification tree algorithm using objective thresholding successfully detected different types of precipitation features even as the complexity of the precipitation field increased. The results show that the complexity and the bias introduced in small-scale phenomena due to the spectral transform method of CAM Eulerian spectral dynamical core is prominent, and is an important reason for its dissimilarity from the FV dynamical core. The resolvable scales, both in horizontal and vertical dimensions, have significant effect on the simulation of precipitation. The results of this study also suggest that an efficient and informative study about the biases produced by GCMs should involve daily (or even hourly) output (rather than monthly mean) analysis over local scales.

  5. An integrative affect regulation process model of internalized weight bias and intuitive eating in college women.

    Science.gov (United States)

    Webb, Jennifer B; Hardin, Abigail S

    2016-07-01

    The present study extended the weight stigma and well-being process model (Tylka et al., 2014) by examining three affect regulation pathways that may help simultaneously explain the predicted inverse association between internalized weight bias and intuitive eating. A weight-diverse sample of 333 college women completed an online survey assessing internalized weight stigma, intuitive eating, body shame, body image flexibility, and self-compassion. Self-reported height and weight were used to calculate body mass index (BMI). Non-parametric bootstrap resampling procedures were computed to ascertain the presence of the indirect effects of internalized weight bias on intuitive eating via the three hypothesized mediators controlling for BMI in a combined model. Results demonstrated that body image flexibility significantly and self-compassion marginally contributed unique variance in accounting for this relationship. Our preliminary cross-sectional findings contribute to a nascent body of scholarship seeking to provide a theoretically-driven understanding of how negative and positive forms of experiencing and relating to the body may co-occur within individuals. Results also point to potential target variables to consider incorporating in later-stage efforts to promote more adaptive ways of eating amidst internalized weight stigma.

  6. Genetic search feature selection for affective modeling

    DEFF Research Database (Denmark)

    Martínez, Héctor P.; Yannakakis, Georgios N.

    2010-01-01

    Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built....... The method is tested and compared against sequential forward feature selection and random search in a dataset derived from a game survey experiment which contains bimodal input features (physiological and gameplay) and expressed pairwise preferences of affect. Results suggest that the proposed method...

  7. Dopamine receptor blockade attenuates the general incentive motivational effects of noncontingently delivered rewards and reward-paired cues without affecting their ability to bias action selection.

    Science.gov (United States)

    Ostlund, Sean B; Maidment, Nigel T

    2012-01-01

    Environmental cues affect our behavior in a variety of ways. Despite playing an invaluable role in guiding our daily activities, such cues also appear to trigger the harmful, compulsive behaviors that characterize addiction and other disorders of behavioral control. In instrumental conditioning, rewards and reward-paired cues bias action selection and invigorate reward-seeking behaviors, and appear to do so through distinct neurobehavioral processes. Although reward-paired cues are known to invigorate performance through a dopamine-dependent incentive motivational process, it is not known if dopamine also mediates the influence of rewards and reward-paired cues over action selection. The current study contrasted the effects of systemic administration of the nonspecific dopamine receptor antagonist flupentixol on response invigoration and action bias in Pavlovian-instrumental transfer, a test of cue-elicited responding, and in instrumental reinstatement, a test of noncontingent reward-elicited responding. Hungry rats were trained on two different stimulus-outcome relationships (eg, tone-grain pellets and noise-sucrose solution) and two different action-outcome relationships (eg, left press-grain and right press-sucrose). At test, we found that flupentixol pretreatment blocked the response invigoration generated by the cues but spared their ability to bias action selection to favor the action whose outcome was signaled by the cue being presented. The response-biasing influence of noncontingent reward deliveries was also unaffected by flupentixol. Interestingly, although flupentixol had a modest effect on the immediate response invigoration produced by those rewards, it was particularly potent in countering the lingering enhancement of responding produced by multiple reward deliveries. These findings indicate that dopamine mediates the general incentive motivational effects of noncontingent rewards and reward-paired cues but does not support their ability to bias

  8. Model selection on solid ground: Rigorous comparison of nine ways to evaluate Bayesian model evidence.

    Science.gov (United States)

    Schöniger, Anneli; Wöhling, Thomas; Samaniego, Luis; Nowak, Wolfgang

    2014-12-01

    Bayesian model selection or averaging objectively ranks a number of plausible, competing conceptual models based on Bayes' theorem. It implicitly performs an optimal trade-off between performance in fitting available data and minimum model complexity. The procedure requires determining Bayesian model evidence (BME), which is the likelihood of the observed data integrated over each model's parameter space. The computation of this integral is highly challenging because it is as high-dimensional as the number of model parameters. Three classes of techniques to compute BME are available, each with its own challenges and limitations: (1) Exact and fast analytical solutions are limited by strong assumptions. (2) Numerical evaluation quickly becomes unfeasible for expensive models. (3) Approximations known as information criteria (ICs) such as the AIC, BIC, or KIC (Akaike, Bayesian, or Kashyap information criterion, respectively) yield contradicting results with regard to model ranking. Our study features a theory-based intercomparison of these techniques. We further assess their accuracy in a simplistic synthetic example where for some scenarios an exact analytical solution exists. In more challenging scenarios, we use a brute-force Monte Carlo integration method as reference. We continue this analysis with a real-world application of hydrological model selection. This is a first-time benchmarking of the various methods for BME evaluation against true solutions. Results show that BME values from ICs are often heavily biased and that the choice of approximation method substantially influences the accuracy of model ranking. For reliable model selection, bias-free numerical methods should be preferred over ICs whenever computationally feasible.

  9. Model selection on solid ground: Rigorous comparison of nine ways to evaluate Bayesian model evidence

    Science.gov (United States)

    Schöniger, Anneli; Wöhling, Thomas; Samaniego, Luis; Nowak, Wolfgang

    2014-12-01

    Bayesian model selection or averaging objectively ranks a number of plausible, competing conceptual models based on Bayes' theorem. It implicitly performs an optimal trade-off between performance in fitting available data and minimum model complexity. The procedure requires determining Bayesian model evidence (BME), which is the likelihood of the observed data integrated over each model's parameter space. The computation of this integral is highly challenging because it is as high-dimensional as the number of model parameters. Three classes of techniques to compute BME are available, each with its own challenges and limitations: (1) Exact and fast analytical solutions are limited by strong assumptions. (2) Numerical evaluation quickly becomes unfeasible for expensive models. (3) Approximations known as information criteria (ICs) such as the AIC, BIC, or KIC (Akaike, Bayesian, or Kashyap information criterion, respectively) yield contradicting results with regard to model ranking. Our study features a theory-based intercomparison of these techniques. We further assess their accuracy in a simplistic synthetic example where for some scenarios an exact analytical solution exists. In more challenging scenarios, we use a brute-force Monte Carlo integration method as reference. We continue this analysis with a real-world application of hydrological model selection. This is a first-time benchmarking of the various methods for BME evaluation against true solutions. Results show that BME values from ICs are often heavily biased and that the choice of approximation method substantially influences the accuracy of model ranking. For reliable model selection, bias-free numerical methods should be preferred over ICs whenever computationally feasible.

  10. On the wintertime low bias of Northern Hemisphere carbon monoxide found in global model simulations

    Science.gov (United States)

    Stein, O.; Schultz, M. G.; Bouarar, I.; Clark, H.; Huijnen, V.; Gaudel, A.; George, M.; Clerbaux, C.

    2014-09-01

    Despite the developments in the global modelling of chemistry and of the parameterization of the physical processes, carbon monoxide (CO) concentrations remain underestimated during Northern Hemisphere (NH) winter by most state-of-the-art chemistry transport models. The consequential model bias can in principle originate from either an underestimation of CO sources or an overestimation of its sinks. We address both the role of surface sources and sinks with a series of MOZART (Model for Ozone And Related Tracers) model sensitivity studies for the year 2008 and compare our results to observational data from ground-based stations, satellite observations, and vertical profiles from measurements on passenger aircraft. In our base case simulation using MACCity (Monitoring Atmospheric Composition and Climate project) anthropogenic emissions, the near-surface CO mixing ratios are underestimated in the Northern Hemisphere by more than 20 ppb from December to April, with the largest bias of up to 75 ppb over Europe in January. An increase in global biomass burning or biogenic emissions of CO or volatile organic compounds (VOCs) is not able to reduce the annual course of the model bias and yields concentrations over the Southern Hemisphere which are too high. Raising global annual anthropogenic emissions with a simple scaling factor results in overestimations of surface mixing ratios in most regions all year round. Instead, our results indicate that anthropogenic CO and, possibly, VOC emissions in the MACCity inventory are too low for the industrialized countries only during winter and spring. Reasonable agreement with observations can only be achieved if the CO emissions are adjusted seasonally with regionally varying scaling factors. A part of the model bias could also be eliminated by exchanging the original resistance-type dry deposition scheme with a parameterization for CO uptake by oxidation from soil bacteria and microbes, which reduces the boreal winter dry

  11. Reducing the bias of estimates of genotype by environment interactions in random regression sire models.

    Science.gov (United States)

    Lillehammer, Marie; Odegård, Jørgen; Meuwissen, Theo H E

    2009-03-19

    The combination of a sire model and a random regression term describing genotype by environment interactions may lead to biased estimates of genetic variance components because of heterogeneous residual variance. In order to test different models, simulated data with genotype by environment interactions, and dairy cattle data assumed to contain such interactions, were analyzed. Two animal models were compared to four sire models. Models differed in their ability to handle heterogeneous variance from different sources. Including an individual effect with a (co)variance matrix restricted to three times the sire (co)variance matrix permitted the modeling of the additive genetic variance not covered by the sire effect. This made the ability of sire models to handle heterogeneous genetic variance approximately equivalent to that of animal models. When residual variance was heterogeneous, a different approach to account for the heterogeneity of variance was needed, for example when using dairy cattle data in order to prevent overestimation of genetic heterogeneity of variance. Including environmental classes can be used to account for heterogeneous residual variance.

  12. Using different Facebook advertisements to recruit men for an online mental health study: Engagement and selection bias

    Directory of Open Access Journals (Sweden)

    Isabella Choi

    2017-06-01

    Full Text Available A growing number of researchers are using Facebook to recruit for a range of online health, medical, and psychosocial studies. There is limited research on the representativeness of participants recruited from Facebook, and the content is rarely mentioned in the methods, despite some suggestion that the advertisement content affects recruitment success. This study explores the impact of different Facebook advertisement content for the same study on recruitment rate, engagement, and participant characteristics. Five Facebook advertisement sets (“resilience”, “happiness”, “strength”, “mental fitness”, and “mental health” were used to recruit male participants to an online mental health study which allowed them to find out about their mental health and wellbeing through completing six measures. The Facebook advertisements recruited 372 men to the study over a one month period. The cost per participant from the advertisement sets ranged from $0.55 to $3.85 Australian dollars. The “strength” advertisements resulted in the highest recruitment rate, but participants from this group were least engaged in the study website. The “strength” and “happiness” advertisements recruited more younger men. Participants recruited from the “mental health” advertisements had worse outcomes on the clinical measures of distress, wellbeing, strength, and stress. This study confirmed that different Facebook advertisement content leads to different recruitment rates and engagement with a study. Different advertisement also leads to selection bias in terms of demographic and mental health characteristics. Researchers should carefully consider the content of social media advertisements to be in accordance with their target population and consider reporting this to enable better assessment of generalisability.

  13. The SDT Model of Belief Bias: Complexity, Time, and Cognitive Ability Mediate the Effects of Believability

    Science.gov (United States)

    Trippas, Dries; Handley, Simon J.; Verde, Michael F.

    2013-01-01

    When people evaluate conclusions, they are often influenced by prior beliefs. Prevalent theories claim that "belief bias" affects the quality of syllogistic reasoning. However, recent work by Dube, Rotello, and Heit (2010) has suggested that belief bias may be a simple response bias. In Experiment 1, receiver operating characteristic…

  14. Novel temperature modeling and compensation method for bias of ring laser gyroscope based on least-squares support vector machine

    Institute of Scientific and Technical Information of China (English)

    Xudong Yu; Yu Wang; Guo Wei; Pengfei Zhang; Xingwu Long

    2011-01-01

    Bias of ring-laser-gyroscope (RLG) changes with temperature in a nonlinear way. This is an important restraining factor for improving the accuracy of RLG. Considering the limitations of least-squares regression and neural network, we propose a new method of temperature compensation of RLG bias-building function regression model using least-squares support vector machine (LS-SVM). Static and dynamic temperature experiments of RLG bias are carried out to validate the effectiveness of the proposed method. Moreover,the traditional least-squares regression method is compared with the LS-SVM-based method. The results show the maximum error of RLG bias drops by almost two orders of magnitude after static temperature compensation, while bias stability of RLG improves by one order of magnitude after dynamic temperature compensation. Thus, the proposed method reduces the influence of temperature variation on the bias of the RLG effectively and improves the accuracy of the gyro scope considerably.%@@ Bias of ring-laser-gyroscope (RLG) changes with temperature in a nonlinear way.This is an important restraining factor for improving the accuracy of RLG.Considering the limitations of least-squares regression and neural network, we propose a new method of temperature compensation of RLG bias-building function regression model using least-squares support vector machine (LS-SVM).Static and dynamic temperature experiments of RLG bias are carried out to validate the effectiveness of the proposed method.Moreover,the traditional least-squares regression method is compared with the LS-SVM-based method.

  15. Minimizing the wintertime low bias of Northern Hemisphere carbon monoxide in global model simulations

    Science.gov (United States)

    Stein, Olaf; Schultz, Martin G.; Bouarar, Idir; Clark, Hannah; Huijnen, Vincent; Gaudel, Audrey; George, Maya; Clerbaux, Cathy

    2015-04-01

    Carbon monoxide (CO) is a product of incomplete combustion and is also produced from oxidation of volatile organic compounds (VOC) in the atmosphere. It is of interest as an indirect greenhouse gas and an air pollutant causing health effects and is thus subject to emission restrictions. CO acts as a major sink for the OH radical and as a precursor for tropospheric ozone and affects the oxidizing capacity of the atmosphere as well as regional air quality. Despite the developments in the global modelling of chemistry and of the parameterization of the physical processes, CO concentrations remain underestimated during NH winter by most state-of-the-art chemical transport models. The resulting model bias can in principle originate from either an underestimation of CO sources or an overestimation of its sinks. We address both the role of sources and sinks with a series of MOZART chemistry transport model sensitivity simulations for the year 2008 and compare our results to observational data from ground-based stations, satellite observations, and from MOZAIC tropospheric profile measurements on passenger aircraft. Our base case simulation using the MACCity emission inventory (Granier et al. 2011) underestimates the near-surface Northern Hemispheric CO mixing ratios by more than 20 ppb from December to April with a maximal bias of 40 ppb in January. The bias is strongest for the European region (up to 75 ppb in January). From our sensitivity studies the mismatch between observed and modelled atmospheric CO concentrations can be explained by a combination of the following emission inventory shortcuts: (i) missing anthropogenic wintertime CO emissions from traffic or other combustion processes, (ii) missing anthropogenic VOC emissions, (iii) an exaggerated downward trend in the RCP8.5 scenario underlying the MACCity inventory, (iv) a lack of knowledge about the seasonality of emissions. Deficiencies in the parameterization of the dry deposition velocities can also lead to

  16. Model selection for Gaussian kernel PCA denoising

    DEFF Research Database (Denmark)

    Jørgensen, Kasper Winther; Hansen, Lars Kai

    2012-01-01

    We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...

  17. Melody Track Selection Using Discriminative Language Model

    Science.gov (United States)

    Wu, Xiao; Li, Ming; Suo, Hongbin; Yan, Yonghong

    In this letter we focus on the task of selecting the melody track from a polyphonic MIDI file. Based on the intuition that music and language are similar in many aspects, we solve the selection problem by introducing an n-gram language model to learn the melody co-occurrence patterns in a statistical manner and determine the melodic degree of a given MIDI track. Furthermore, we propose the idea of using background model and posterior probability criteria to make modeling more discriminative. In the evaluation, the achieved 81.6% correct rate indicates the feasibility of our approach.

  18. Item Bias Detection Using the Loglinear Rasch Model: Observed and Unobserved Subgroups. Research Report 86-2.

    Science.gov (United States)

    Kelderman, Henk

    A method is proposed for the detection of item bias with respect to observed or unobserved subgroups. The method uses quasi-loglinear models for the incomplete subgroup x test score x item 1 x ... x item k contingency table. If the subgroup membership is unknown, the models are the incomplete-latent-class models of S. J. Haberman (1979). The…

  19. Negative learning bias is associated with risk aversion in a genetic animal model of depression

    Directory of Open Access Journals (Sweden)

    Steven John Shabel

    2014-01-01

    Full Text Available The lateral habenula (LHb is activated by aversive stimuli and the omission of reward, inhibited by rewarding stimuli and is hyperactive in helpless rats – an animal model of depression. Here we test the hypothesis that congenital learned helpless (cLH rats are more sensitive to decreases in reward size and/or less sensitive to increases in reward than wild-type (WT control rats. Consistent with the hypothesis, we found that cLH rats were slower to switch preference between two responses after a small upshift in reward size on one of the responses but faster to switch their preference after a small downshift in reward size. cLH rats were also more risk-averse than WT rats – they chose a response delivering a constant amount of reward (safe response more often than a response delivering a variable amount of reward (risky response compared to WT rats. Interestingly, the level of bias towards negative events was associated with the rat’s level of risk aversion when compared across individual rats. cLH rats also showed impaired appetitive Pavlovian conditioning but more accurate responding in a two-choice sensory discrimination task. These results are consistent with a negative learning bias and risk aversion in cLH rats, suggesting abnormal processing of rewarding and aversive events in the LHb of cLH rats.

  20. Genetic biasing through cultural transmission: do simple Bayesian models of language evolution generalize?

    Science.gov (United States)

    Dediu, Dan

    2009-08-07

    The recent Bayesian approaches to language evolution and change seem to suggest that genetic biases can impact on the characteristics of language, but, at the same time, that its cultural transmission can partially free it from these same genetic constraints. One of the current debates centres on the striking differences between sampling and a posteriori maximising Bayesian learners, with the first converging on the prior bias while the latter allows a certain freedom to language evolution. The present paper shows that this difference disappears if populations more complex than a single teacher and a single learner are considered, with the resulting behaviours more similar to the sampler. This suggests that generalisations based on the language produced by Bayesian agents in such homogeneous single agent chains are not warranted. It is not clear which of the assumptions in such models are responsible, but these findings seem to support the rising concerns on the validity of the "acquisitionist" assumption, whereby the locus of language change and evolution is taken to be the first language acquirers (children) as opposed to the competent language users (the adults).

  1. Comparison of two bias correction methods for precipitation simulated with a regional climate model

    Science.gov (United States)

    Tschöke, Gabriele Vanessa; Kruk, Nadiane Smaha; de Queiroz, Paulo Ivo Braga; Chou, Sin Chan; de Sousa Junior, Wilson Cabral

    2017-02-01

    This study evaluates the performance of two bias correction techniques—power transformation and gamma distribution adjustment—for Eta regional climate model (RCM) precipitation simulations. For the gamma distribution adjustment, the number of dry days is not taken as a fixed parameter; rather, we propose a new methodology for handling dry days. We consider two cases: the first case is defined as having a greater number of simulated dry days than the observed number, and the second case is defined as the opposite. The present climate period was divided into calibration and validation sets. We evaluate the results of the two bias correction techniques using the Kolmogorov-Smirnov nonparametric test and the sum of the differences between the cumulative distribution curves. These tests show that both correction techniques were effective in reducing errors and consequently improving the reliability of the simulations. However, the gamma distribution correction method proved to be more efficient, particularly in reducing the error in the number of dry days.

  2. Analytical recovery of protozoan enumeration methods: have drinking water QMRA models corrected or created bias?

    Science.gov (United States)

    Schmidt, P J; Emelko, M B; Thompson, M E

    2013-05-01

    Quantitative microbial risk assessment (QMRA) is a tool to evaluate the potential implications of pathogens in a water supply or other media and is of increasing interest to regulators. In the case of potentially pathogenic protozoa (e.g. Cryptosporidium oocysts and Giardia cysts), it is well known that the methods used to enumerate (oo)cysts in samples of water and other media can have low and highly variable analytical recovery. In these applications, QMRA has evolved from ignoring analytical recovery to addressing it in point-estimates of risk, and then to addressing variation of analytical recovery in Monte Carlo risk assessments. Often, variation of analytical recovery is addressed in exposure assessment by dividing concentration values that were obtained without consideration of analytical recovery by random beta-distributed recovery values. A simple mathematical proof is provided to demonstrate that this conventional approach to address non-constant analytical recovery in drinking water QMRA will lead to overestimation of mean pathogen concentrations. The bias, which can exceed an order of magnitude, is greatest when low analytical recovery values are common. A simulated dataset is analyzed using a diverse set of approaches to obtain distributions representing temporal variation in the oocyst concentration, and mean annual risk is then computed from each concentration distribution using a simple risk model. This illustrative example demonstrates that the bias associated with mishandling non-constant analytical recovery and non-detect samples can cause drinking water systems to be erroneously classified as surpassing risk thresholds.

  3. Selection of Representative Models for Decision Analysis Under Uncertainty

    Science.gov (United States)

    Meira, Luis A. A.; Coelho, Guilherme P.; Santos, Antonio Alberto S.; Schiozer, Denis J.

    2016-03-01

    The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request.

  4. Expert System Model for Educational Personnel Selection

    Directory of Open Access Journals (Sweden)

    Héctor A. Tabares-Ospina

    2013-06-01

    Full Text Available The staff selection is a difficult task due to the subjectivity that the evaluation means. This process can be complemented using a system to support decision. This paper presents the implementation of an expert system to systematize the selection process of professors. The management of software development is divided into 4 parts: requirements, design, implementation and commissioning. The proposed system models a specific knowledge through relationships between variables evidence and objective.

  5. A hybrid Bayesian hierarchical model combining cohort and case-control studies for meta-analysis of diagnostic tests: Accounting for partial verification bias.

    Science.gov (United States)

    Ma, Xiaoye; Chen, Yong; Cole, Stephen R; Chu, Haitao

    2014-05-26

    To account for between-study heterogeneity in meta-analysis of diagnostic accuracy studies, bivariate random effects models have been recommended to jointly model the sensitivities and specificities. As study design and population vary, the definition of disease status or severity could differ across studies. Consequently, sensitivity and specificity may be correlated with disease prevalence. To account for this dependence, a trivariate random effects model had been proposed. However, the proposed approach can only include cohort studies with information estimating study-specific disease prevalence. In addition, some diagnostic accuracy studies only select a subset of samples to be verified by the reference test. It is known that ignoring unverified subjects may lead to partial verification bias in the estimation of prevalence, sensitivities, and specificities in a single study. However, the impact of this bias on a meta-analysis has not been investigated. In this paper, we propose a novel hybrid Bayesian hierarchical model combining cohort and case-control studies and correcting partial verification bias at the same time. We investigate the performance of the proposed methods through a set of simulation studies. Two case studies on assessing the diagnostic accuracy of gadolinium-enhanced magnetic resonance imaging in detecting lymph node metastases and of adrenal fluorine-18 fluorodeoxyglucose positron emission tomography in characterizing adrenal masses are presented.

  6. Sensorimotor learning biases choice behavior: a learning neural field model for decision making.

    Directory of Open Access Journals (Sweden)

    Christian Klaes

    Full Text Available According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action

  7. Bayesian variable selection for latent class models.

    Science.gov (United States)

    Ghosh, Joyee; Herring, Amy H; Siega-Riz, Anna Maria

    2011-09-01

    In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.

  8. Large-scale atmospheric circulation biases and changes in global climate model simulations and their importance for climate change in Central Europe

    Directory of Open Access Journals (Sweden)

    A. P. van Ulden

    2006-01-01

    Full Text Available The quality of global sea level pressure patterns has been assessed for simulations by 23 coupled climate models. Most models showed high pattern correlations. With respect to the explained spatial variance, many models showed serious large-scale deficiencies, especially at mid-latitudes. Five models performed well at all latitudes and for each month of the year. Three models had a reasonable skill. We selected the five models with the best pressure patterns for a more detailed assessment of their simulations of the climate in Central Europe. We analysed observations and simulations of monthly mean geostrophic flow indices and of monthly mean temperature and precipitation. We used three geostrophic flow indices: the west component and south component of the geostrophic wind at the surface and the geostrophic vorticity. We found that circulation biases were important, and affected precipitation in particular. Apart from these circulation biases, the models showed other biases in temperature and precipitation, which were for some models larger than the circulation induced biases. For the 21st century the five models simulated quite different changes in circulation, precipitation and temperature. Precipitation changes appear to be primarily caused by circulation changes. Since the models show widely different circulation changes, especially in late summer, precipitation changes vary widely between the models as well. Some models simulate severe drying in late summer, while one model simulates significant precipitation increases in late summer. With respect to the mean temperature the circulation changes were important, but not dominant. However, changes in the distribution of monthly mean temperatures, do show large indirect influences of circulation changes. Especially in late summer, two models simulate very strong warming of warm months, which can be attributed to severe summer drying in the simulations by these models. The models differ also

  9. Bias Analysis of Single Equation Based on Econometric Model%基于单方程计量经济模型的贝叶斯分析

    Institute of Scientific and Technical Information of China (English)

    周俊梅; 蒋文江

    2013-01-01

    The Bias problem of statistical inference about the single equation econometric model is studied,According to the prior distribution structure of sample likelihood function of the unknown parameters,normal and Gamma distribution is selected as the prior distribution.The posterior distribution of statistical inference.Finally deduced Bias unknown parameters in two under quadratic loss function estimation.%研究了关于单方程计量经济模型的贝叶斯统计推断问题,即根据样本似然函数的形式构造出未知参数的先验分布,选取正态-Gamma分布为先验分布,对后验分布进行统计推断.最后推导了未知参数在二次损失函数下的贝叶斯估计.

  10. A genetic algorithm based global search strategy for population pharmacokinetic/pharmacodynamic model selection.

    Science.gov (United States)

    Sale, Mark; Sherer, Eric A

    2015-01-01

    The current algorithm for selecting a population pharmacokinetic/pharmacodynamic model is based on the well-established forward addition/backward elimination method. A central strength of this approach is the opportunity for a modeller to continuously examine the data and postulate new hypotheses to explain observed biases. This algorithm has served the modelling community well, but the model selection process has essentially remained unchanged for the last 30 years. During this time, more robust approaches to model selection have been made feasible by new technology and dramatic increases in computation speed. We review these methods, with emphasis on genetic algorithm approaches and discuss the role these methods may play in population pharmacokinetic/pharmacodynamic model selection.

  11. A Second-order bias model for the Logarithmic Halo Mass Density

    CERN Document Server

    Jee, Inh; Kim, Juhan; Choi, Yun-Young; Kim, Sungsoo S

    2012-01-01

    We present an analytic model for the local bias of dark matter halos in a LCDM universe. The model uses the halo mass density instead of the halo number density and is searched for various halo mass cuts, smoothing lengths, and redshift epoches. We find that, when the logarithmic density is used, the second-order polynomial can fit the numerical relation between the halo mass distribution and the underlying matter distribution extremely well. In this model the logarithm of the dark matter density is expanded in terms of log halo mass density to the second order. The model remains excellent for all halo mass cuts (from M_{cut}=3\\times10^{11}$ to $3\\times10^{12}h^{-1}M_{\\odot}$), smoothing scales (from $R=5h^{-1}$Mpc to $50h^{-1}$Mpc), and redshift ranges (from z=0 to 1.0) considered in this study. The stochastic term in the relation is found not entirely random, but a part of the term can be determined by the magnitude of the shear tensor.

  12. Contribution of aboveground biomass uncertainty to bias in modeled global net ecosystem exchange

    Science.gov (United States)

    Poulter, B.; Delbart, N.; Maignan, F.; Saatchi, S. S.; Sitch, S.; Ciais, P.

    2011-12-01

    Biomass is a key ecosystem property that links biogeochemical fluxes with the accumulation of carbon. The spatial and temporal dynamics of biomass have implications for climate stability and other ecosystem services. Globally, terrestrial forest ecosystems store approximately 383 Pg C in aboveground biomass, about 45% compared to the amount of carbon in the atmosphere. Model-data comparisons of aboveground biomass have so far been limited by a lack of wall-to-wall coverage, which has recently been resolved from satellite remote sensing. These recent satellite products use lidar to measure forest structure directly or have developed novel data-fusion techniques. Here, we compare biomass estimates among terrestrial carbon cycle models, and benchmark these estimates with inventory and satellite-based estimates. Using an ensemble of dynamic global vegetation model simulations from the TRENDY archive, we then use the distribution of biomass estimates to evaluate bias in net ecosystem exchange caused by uncertainty from carbon turnover. By identifying detailed model structure and parameters that are linked to carbon turnover, targeted improvements can be made to more realistically simulate aboveground biomass.

  13. Impact of changes in the formulation of cloud-related processes on model biases and climate feedbacks

    NARCIS (Netherlands)

    Lacagnina, C.; Selten, F.; Siebesma, A.P.

    2014-01-01

    To test the impact of modeling uncertainties and biases on the simulation of cloud feedbacks, several configurations of the EC-Earth climate model are built altering physical parameterizations. An overview of the various radiative feedbacks diagnosed from the reference EC-Earth configuration is

  14. Media Bias

    OpenAIRE

    Sendhil Mullainathan; Andrei Shleifer

    2002-01-01

    There are two different types of media bias. One bias, which we refer to as ideology, reflects a news outlet's desire to affect reader opinions in a particular direction. The second bias, which we refer to as spin, reflects the outlet's attempt to simply create a memorable story. We examine competition among media outlets in the presence of these biases. Whereas competition can eliminate the effect of ideological bias, it actually exaggerates the incentive to spin stories.

  15. MODEL SELECTION FOR LOG-LINEAR MODELS OF CONTINGENCY TABLES

    Institute of Scientific and Technical Information of China (English)

    ZHAO Lincheng; ZHANG Hong

    2003-01-01

    In this paper, we propose an information-theoretic-criterion-based model selection procedure for log-linear model of contingency tables under multinomial sampling, and establish the strong consistency of the method under some mild conditions. An exponential bound of miss detection probability is also obtained. The selection procedure is modified so that it can be used in practice. Simulation shows that the modified method is valid. To avoid selecting the penalty coefficient in the information criteria, an alternative selection procedure is given.

  16. Adverse selection model regarding tobacco consumption

    Directory of Open Access Journals (Sweden)

    Dumitru MARIN

    2006-01-01

    Full Text Available The impact of introducing a tax on tobacco consumption can be studied trough an adverse selection model. The objective of the model presented in the following is to characterize the optimal contractual relationship between the governmental authorities and the two type employees: smokers and non-smokers, taking into account that the consumers’ decision to smoke or not represents an element of risk and uncertainty. Two scenarios are run using the General Algebraic Modeling Systems software: one without taxes set on tobacco consumption and another one with taxes set on tobacco consumption, based on an adverse selection model described previously. The results of the two scenarios are compared in the end of the paper: the wage earnings levels and the social welfare in case of a smoking agent and in case of a non-smoking agent.

  17. How many dinosaur species were there? Fossil bias and true richness estimated using a Poisson sampling model.

    Science.gov (United States)

    Starrfelt, Jostein; Liow, Lee Hsiang

    2016-04-01

    The fossil record is a rich source of information about biological diversity in the past. However, the fossil record is not only incomplete but has also inherent biases due to geological, physical, chemical and biological factors. Our knowledge of past life is also biased because of differences in academic and amateur interests and sampling efforts. As a result, not all individuals or species that lived in the past are equally likely to be discovered at any point in time or space. To reconstruct temporal dynamics of diversity using the fossil record, biased sampling must be explicitly taken into account. Here, we introduce an approach that uses the variation in the number of times each species is observed in the fossil record to estimate both sampling bias and true richness. We term our technique TRiPS (True Richness estimated using a Poisson Sampling model) and explore its robustness to violation of its assumptions via simulations. We then venture to estimate sampling bias and absolute species richness of dinosaurs in the geological stages of the Mesozoic. Using TRiPS, we estimate that 1936 (1543-2468) species of dinosaurs roamed the Earth during the Mesozoic. We also present improved estimates of species richness trajectories of the three major dinosaur clades: the sauropodomorphs, ornithischians and theropods, casting doubt on the Jurassic-Cretaceous extinction event and demonstrating that all dinosaur groups are subject to considerable sampling bias throughout the Mesozoic.

  18. Understanding selection bias, time-lags and measurement bias in secondary data sources: Putting the Encyclopedia of Associations database in broader context.

    Science.gov (United States)

    Bevan, Shaun; Baumgartner, Frank R; Johnson, Erik W; McCarthy, John D

    2013-11-01

    Secondary data gathered for purposes other than research play an important role in the social sciences. A recent data release has made an important source of publicly available data on associational interests, the Encyclopedia of Associations (EA), readily accessible to scholars (www.policyagendas.org). In this paper we introduce these new data and systematically investigate issues of lag between events and subsequent reporting in the EA, as these have important but under-appreciated effects on time-series statistical models. We further analyze the accuracy and coverage of the database in numerous ways. Our study serves as a guide to potential users of this database, but we also reflect upon a number of issues that should concern all researchers who use secondary data such as newspaper records, IRS reports and FBI Uniform Crime Reports. Copyright © 2013 Elsevier Inc. All rights reserved.

  19. Adaptive Covariance Estimation with model selection

    CERN Document Server

    Biscay, Rolando; Loubes, Jean-Michel

    2012-01-01

    We provide in this paper a fully adaptive penalized procedure to select a covariance among a collection of models observing i.i.d replications of the process at fixed observation points. For this we generalize previous results of Bigot and al. and propose to use a data driven penalty to obtain an oracle inequality for the estimator. We prove that this method is an extension to the matricial regression model of the work by Baraud.

  20. Wind-Farm Forecasting Using the HARMONIE Weather Forecast Model and Bayes Model Averaging for Bias Removal.

    Science.gov (United States)

    O'Brien, Enda; McKinstry, Alastair; Ralph, Adam

    2015-04-01

    Building on previous work presented at EGU 2013 (http://www.sciencedirect.com/science/article/pii/S1876610213016068 ), more results are available now from a different wind-farm in complex terrain in southwest Ireland. The basic approach is to interpolate wind-speed forecasts from an operational weather forecast model (i.e., HARMONIE in the case of Ireland) to the precise location of each wind-turbine, and then use Bayes Model Averaging (BMA; with statistical information collected from a prior training-period of e.g., 25 days) to remove systematic biases. Bias-corrected wind-speed forecasts (and associated power-generation forecasts) are then provided twice daily (at 5am and 5pm) out to 30 hours, with each forecast validation fed back to BMA for future learning. 30-hr forecasts from the operational Met Éireann HARMONIE model at 2.5km resolution have been validated against turbine SCADA observations since Jan. 2014. An extra high-resolution (0.5km grid-spacing) HARMONIE configuration has been run since Nov. 2014 as an extra member of the forecast "ensemble". A new version of HARMONIE with extra filters designed to stabilize high-resolution configurations has been run since Jan. 2015. Measures of forecast skill and forecast errors will be provided, and the contributions made by the various physical and computational enhancements to HARMONIE will be quantified.

  1. A Theoretical Model for Selective Exposure Research.

    Science.gov (United States)

    Roloff, Michael E.; Noland, Mark

    This study tests the basic assumptions underlying Fishbein's Model of Attitudes by correlating an individual's selective exposure to types of television programs (situation comedies, family drama, and action/adventure) with the attitudinal similarity between individual attitudes and attitudes characterized on the programs. Twenty-three college…

  2. Bias in peak clad temperature predictions due to uncertainties in modeling of ECC bypass and dissolved non-condensable gas phenomena

    Energy Technology Data Exchange (ETDEWEB)

    Rohatgi, U.S.; Neymotin, L.Y.; Jo, J.; Wulff, W. (Brookhaven National Lab., Upton, NY (USA))

    1990-09-01

    This report describes a general method for estimating the effect on the Reflood Phase PCT from systematic errors (biases) associated with the modelling of the ECCS and dissolved nitrogen, and the application of this method in estimating biases in the Reflood Phase PCT (second PCT) predicted by the TRAC/PF1/MOD1, Version 14.3. The bias in the second PCT due to the uncertainty in the existing code models for ECCS related phenomena is {minus}19{degree}K ({minus}34{degree}F). The negative bias implies that the code models for this phenomena are conservative. The bias in the second PCT due to the lack of modelling of dissolved N{sub 2} in the code is estimated to be 9.9{degree}K (17.8{degree}F). The positive bias implies that the absence of dissolved N{sub 2} model makes the code prediction of PCT non-conservative. The bias estimation in this report is a major exception among all other uncertainty and bias assessments performed in conjunction with the CSAU methodology demonstration, because this bias estimation benefitted from using full-scale test data from the full-scale Upper Plenum Test Facility (UPTF). Thus, the bias estimates presented here are unaffected by scale distortions in test facilities. Data from small size facilities were also available and an estimate of bias based on these data will be conservative. 35 refs., 18 figs., 5 tabs.

  3. Experimental Verification of the Physical Model for Droplet-Particles Cleaning in Pulsed Bias Arc Ion Plating

    Institute of Scientific and Technical Information of China (English)

    Yanhui ZHAO; Guoqiang LIN; Chuang DONG; Lishi WEN

    2005-01-01

    It has been reported that application of pulsed biases in arc ion plating could effectively eliminate droplet particles.The present paper aims at experimental verification of a physical model proposed previously by us which is based on particle charging and repulsion in the pulsed plasma sheath. An orthogonal experiment was designed for this purpose,using the electrical parameters of the pulsed bias for the deposition of TiN films on stainless steel substrates. The effect of these parameters on the amount and the size distribution of the particles were analyzed, and the results provided sufficient evidence for the physical model.

  4. Trends and biases in global scientific literature about ecological niche models.

    Science.gov (United States)

    Vaz, U L; Cunha, H F; Nabout, J C

    2015-11-01

    Recently, ecological niche models have been employed to investigate the potential geographical distribution of species. However, it is necessary to analyze the vast number of publications on this topic to understand the trends and biases of research using ecological niche models (ENMs). Therefore, this study aims to investigate trends in the scientific literature regarding studies on ENMs. For the quantitative analysis of the literature on ENMs, we performed a search in the Thomson ISI (Web of Science) database between 1991 and 2013. The search identified 3042 papers containing preselected keywords in either the title or abstract. The results showed that the number of papers has increased over the years (r=0.77, Pplants (402 papers, or 28.36% of the total). There was no relationship between the modeling method used and the taxonomic group studied (χ2=4.8, P=0.15). Finally, the wide availability of biological, environmental and computational resources has elicited the broad use of tools for ENMs. Despite the conceptual discussions of the ENMs, this method is currently the most effective way to evaluate the potential geographical distribution of species, and to predict the distribution under different environmental conditions (i.e., future or past scenarios).

  5. Value of bias-corrected satellite rainfall products in SWAT simulations and comparison with other models in the Mara basin

    Science.gov (United States)

    Serrat-Capdevila, A.; Abitew, T. A.; Roy, T.; van Griensven, A.; Valdes, J. B.; Bauwens, W.

    2015-12-01

    Hydrometeorological monitoring networks are often limited for basins located in the developing world such as the transboundary Mara Basin. The advent of earth observing systems have brought satellite rainfall and evapotranspiration products, which can be used to force hydrological models in data scarce basins. The objective of this study is to develop improved hydrologic simulations using distributed satellite rainfall products (CMORPH and TMPA) with a bias-correction, and compare the performance with different input data and models. The bias correction approach for the satellite-products (CMORPH and TMPA) involves the use of a distributed reference dataset (CHIRPS) and historical ground gauge records. We have applied the bias-corrected satellite products to force the Soil and Water Assessment Tool (SWAT) model for the Mara Basin. Firstly, we calibrate the SWAT parameters related to ET simulation using ET from remote sensing. Then, the SWAT parameters that control surface processes are calibrated using the available limited flow. From the analysis, we noted that not only the bias-corrected satellite rainfall but also augmenting limited flow data with monthly remote sensing ET improves the model simulation skill and reduces the parameter uncertainty to some extent. We have planned to compare these results from a lumped model forced by the same input satellite rainfall. This will shed light on the potential of satellite rainfall and remote sensing ET along with in situ data for hydrological processes modeling and the inherent uncertainty in a data scarce basin.

  6. Model selection for radiochromic film dosimetry

    CERN Document Server

    Méndez, Ignasi

    2015-01-01

    The purpose of this study was to find the most accurate model for radiochromic film dosimetry by comparing different channel independent perturbation models. A model selection approach based on (algorithmic) information theory was followed, and the results were validated using gamma-index analysis on a set of benchmark test cases. Several questions were addressed: (a) whether incorporating the information of the non-irradiated film, by scanning prior to irradiation, improves the results; (b) whether lateral corrections are necessary when using multichannel models; (c) whether multichannel dosimetry produces better results than single-channel dosimetry; (d) which multichannel perturbation model provides more accurate film doses. It was found that scanning prior to irradiation and applying lateral corrections improved the accuracy of the results. For some perturbation models, increasing the number of color channels did not result in more accurate film doses. Employing Truncated Normal perturbations was found to...

  7. Portfolio Selection Model with Derivative Securities

    Institute of Scientific and Technical Information of China (English)

    王春峰; 杨建林; 蒋祥林

    2003-01-01

    Traditional portfolio theory assumes that the return rate of portfolio follows normality. However, this assumption is not true when derivative assets are incorporated. In this paper a portfolio selection model is developed based on utility function which can capture asymmetries in random variable distributions. Other realistic conditions are also considered, such as liabilities and integer decision variables. Since the resulting model is a complex mixed-integer nonlinear programming problem, simulated annealing algorithm is applied for its solution. A numerical example is given and sensitivity analysis is conducted for the model.

  8. Automation of Endmember Pixel Selection in SEBAL/METRIC Model

    Science.gov (United States)

    Bhattarai, N.; Quackenbush, L. J.; Im, J.; Shaw, S. B.

    2015-12-01

    The commonly applied surface energy balance for land (SEBAL) and its variant, mapping evapotranspiration (ET) at high resolution with internalized calibration (METRIC) models require manual selection of endmember (i.e. hot and cold) pixels to calibrate sensible heat flux. Current approaches for automating this process are based on statistical methods and do not appear to be robust under varying climate conditions and seasons. In this paper, we introduce a new approach based on simple machine learning tools and search algorithms that provides an automatic and time efficient way of identifying endmember pixels for use in these models. The fully automated models were applied on over 100 cloud-free Landsat images with each image covering several eddy covariance flux sites in Florida and Oklahoma. Observed land surface temperatures at automatically identified hot and cold pixels were within 0.5% of those from pixels manually identified by an experienced operator (coefficient of determination, R2, ≥ 0.92, Nash-Sutcliffe efficiency, NSE, ≥ 0.92, and root mean squared error, RMSE, ≤ 1.67 K). Daily ET estimates derived from the automated SEBAL and METRIC models were in good agreement with their manual counterparts (e.g., NSE ≥ 0.91 and RMSE ≤ 0.35 mm day-1). Automated and manual pixel selection resulted in similar estimates of observed ET across all sites. The proposed approach should reduce time demands for applying SEBAL/METRIC models and allow for their more widespread and frequent use. This automation can also reduce potential bias that could be introduced by an inexperienced operator and extend the domain of the models to new users.

  9. Correcting North Atlantic sea surface salinity biases in the Kiel Climate Model: influences on ocean circulation and Atlantic Multidecadal Variability

    Science.gov (United States)

    Park, T.; Park, W.; Latif, M.

    2016-10-01

    A long-standing problem in climate models is the large sea surface salinity (SSS) biases in the North Atlantic. In this study, we describe the influences of correcting these SSS biases on the circulation of the North Atlantic as well as on North Atlantic sector mean climate and decadal to multidecadal variability. We performed integrations of the Kiel Climate Model (KCM) with and without applying a freshwater flux correction over the North Atlantic. The quality of simulating the mean circulation of the North Atlantic Ocean, North Atlantic sector mean climate and decadal variability is greatly enhanced in the freshwater flux-corrected integration which, by definition, depicts relatively small North Atlantic SSS biases. In particular, a large reduction in the North Atlantic cold sea surface temperature bias is observed and a more realistic Atlantic Multidecadal Variability simulated. Improvements relative to the non-flux corrected integration also comprise a more realistic representation of deep convection sites, sea ice, gyre circulation and Atlantic Meridional Overturning Circulation. The results suggest that simulations of North Atlantic sector mean climate and decadal variability could strongly benefit from alleviating sea surface salinity biases in the North Atlantic, which may enhance the skill of decadal predictions in that region.

  10. Impacts of Sea Surface Salinity Bias Correction on North Atlantic Ocean Circulation and Climate Variability in the Kiel Climate Model

    Science.gov (United States)

    Park, Taewook; Park, Wonsun; Latif, Mojib

    2016-04-01

    We investigated impacts of correcting North Atlantic sea surface salinity (SSS) biases on the ocean circulation of the North Atlantic and on North Atlantic sector mean climate and climate variability in the Kiel Climate Model (KCM). Bias reduction was achieved by applying a freshwater flux correction over the North Atlantic to the model. The quality of simulating the mean circulation of the North Atlantic Ocean, North Atlantic sector mean climate and decadal variability is greatly enhanced in the freshwater flux-corrected integration which, by definition, depicts relatively small North Atlantic SSS biases. In particular, a large reduction in the North Atlantic cold sea surface temperature (SST) bias is observed and a more realistic Atlantic Multidecadal Variability (AMV) simulated. Improvements relative to the non-flux corrected integration also comprise a more realistic representation of deep convection sites, sea ice, gyre circulation and Atlantic Meridional Overturning Circulation (AMOC). The results suggest that simulations of North Atlantic sector mean climate and decadal variability could strongly benefit from alleviating sea surface salinity biases in the North Atlantic, which may enhance the skill of decadal predictions in that region.

  11. Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model.

    Science.gov (United States)

    Chen, Yunjie; Zhao, Bo; Zhang, Jianwei; Zheng, Yuhui

    2014-09-01

    Accurate segmentation of magnetic resonance (MR) images remains challenging mainly due to the intensity inhomogeneity, which is also commonly known as bias field. Recently active contour models with geometric information constraint have been applied, however, most of them deal with the bias field by using a necessary pre-processing step before segmentation of MR data. This paper presents a novel automatic variational method, which can segment brain MR images meanwhile correcting the bias field when segmenting images with high intensity inhomogeneities. We first define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. In order to reduce the effect of the noise, the local intensity variations are described by the Gaussian distributions with different means and variances. Then, the objective functions are integrated over the entire domain. In order to obtain the global optimal and make the results independent of the initialization of the algorithm, we reconstructed the energy function to be convex and calculated it by using the Split Bregman theory. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. Our method is able to estimate the bias of quite general profiles, even in 7T MR images. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results.

  12. Tropical Indian Ocean surface salinity bias in Climate Forecasting System coupled models and the role of upper ocean processes

    Science.gov (United States)

    Parekh, Anant; Chowdary, Jasti S.; Sayantani, Ojha; Fousiya, T. S.; Gnanaseelan, C.

    2016-04-01

    In the present study sea surface salinity (SSS) biases and seasonal tendency over the Tropical Indian Ocean (TIO) in the coupled models [Climate Forecasting System version 1 (CFSv1) and version 2 (CFSv2)] are examined with respect to observations. Both CFSv1 and CFSv2 overestimate SSS over the TIO throughout the year. CFSv1 displays improper SSS seasonal cycle over the Bay of Bengal (BoB), which is due to weaker model precipitation and improper river runoff especially during summer and fall. Over the southeastern Arabian Sea (AS) weak horizontal advection associated with East Indian coastal current during winter limits the formation of spring fresh water pool. On the other hand, weaker Somali jet during summer results for reduced positive salt tendency in the central and eastern AS. Strong positive precipitation bias in CFSv1 over the region off Somalia during winter, weaker vertical mixing and absence of horizontal salt advection lead to unrealistic barrier layer during winter and spring. The weaker stratification and improper spatial distribution of barrier layer thickness (BLT) in CFSv1 indicate that not only horizontal flux distribution but also vertical salt distribution displays large discrepancies. Absence of fall Wyrtki jet and winter equatorial currents in this model limit the advection of horizontal salt flux to the eastern equatorial Indian Ocean. The associated weaker stratification in eastern equatorial Indian Ocean can lead to deeper mixed layer and negative Sea Surface Temperature (SST) bias, which in turn favor positive Indian Ocean Dipole bias in CFSv1. It is important to note that improper spatial distribution of barrier layer and stratification can alter the air-sea interaction and precipitation in the models. On the other hand CFSv2 could produce the seasonal evolution and spatial distribution of SSS, BLT and stratification better than CFSv1. However CFSv2 displays positive bias in evaporation over the whole domain and negative bias in

  13. Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology.

    Science.gov (United States)

    Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H

    2017-07-01

    Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in

  14. Aerosol model selection and uncertainty modelling by adaptive MCMC technique

    Directory of Open Access Journals (Sweden)

    M. Laine

    2008-12-01

    Full Text Available We present a new technique for model selection problem in atmospheric remote sensing. The technique is based on Monte Carlo sampling and it allows model selection, calculation of model posterior probabilities and model averaging in Bayesian way.

    The algorithm developed here is called Adaptive Automatic Reversible Jump Markov chain Monte Carlo method (AARJ. It uses Markov chain Monte Carlo (MCMC technique and its extension called Reversible Jump MCMC. Both of these techniques have been used extensively in statistical parameter estimation problems in wide area of applications since late 1990's. The novel feature in our algorithm is the fact that it is fully automatic and easy to use.

    We show how the AARJ algorithm can be implemented and used for model selection and averaging, and to directly incorporate the model uncertainty. We demonstrate the technique by applying it to the statistical inversion problem of gas profile retrieval of GOMOS instrument on board the ENVISAT satellite. Four simple models are used simultaneously to describe the dependence of the aerosol cross-sections on wavelength. During the AARJ estimation all the models are used and we obtain a probability distribution characterizing how probable each model is. By using model averaging, the uncertainty related to selecting the aerosol model can be taken into account in assessing the uncertainty of the estimates.

  15. Investigation of selection bias in the association of race with prevalent atrial fibrillation in a national cohort study: REasons for Geographic And Racial Differences in Stroke (REGARDS).

    Science.gov (United States)

    Thacker, Evan L; Soliman, Elsayed Z; Pulley, LeaVonne; Safford, Monika M; Howard, George; Howard, Virginia J

    2016-08-01

    Atrial fibrillation (AF) is diagnosed more commonly in whites than blacks in the United States. In epidemiologic studies, selection bias could induce a noncausal positive association of white race with prevalent AF if voluntary enrollment was influenced by both race and AF status. We investigated whether nonrandom enrollment biased the association of race with prevalent self-reported AF in the US-based REasons for Geographic And Racial Differences in Stroke Study (REGARDS). REGARDS had a two-stage enrollment process, allowing us to compare 30,183 fully enrolled REGARDS participants with 12,828 people who completed the first-stage telephone survey but did not complete the second-stage in-home visit to finalize their REGARDS enrollment (telephone-only participants). REGARDS enrollment was higher among whites (77.1%) than among blacks (62.3%) but did not differ by self-reported AF status. The prevalence of AF was 8.45% in whites and 5.86% in blacks adjusted for age, sex, income, education, and perceived general health. The adjusted white/black prevalence ratio of self-reported AF was 1.43 (95% CI, 1.32-1.56) among REGARDS participants and 1.38 (1.22-1.55) among telephone-only participants. These findings suggest that selection bias is not a viable explanation for the higher prevalence of self-reported AF among whites in population studies such as REGARDS. Copyright © 2016 Elsevier Inc. All rights reserved.

  16. The Biases of Optical Line-Ratio Selection for Active Galactic Nuclei, and the Intrinsic Relationship between Black Hole Accretion and Galaxy Star Formation

    CERN Document Server

    Trump, Jonathan R; Zeimann, Gregory R; Luck, Cuyler; Bridge, Joanna S; Grier, Catherine J; Hagen, Alex; Juneau, Stephanie; Montero-Dorta, Antonio; Rosario, David J; Brandt, W Niel; Ciardullo, Robin; Schneider, Donald P

    2015-01-01

    We use 317,000 emission-line galaxies from the Sloan Digital Sky Survey to investigate line-ratio selection of active galactic nuclei (AGNs). In particular, we demonstrate that "star formation dilution" by HII regions causes a significant bias against AGN selection in low-mass, blue, star-forming, disk-dominated galaxies. This bias is responsible for the observed preference of AGNs among high-mass, green, moderately star-forming, bulge-dominated hosts. We account for the bias and simulate the intrinsic population of emission-line AGNs using a physically-motivated Eddington ratio distribution, intrinsic AGN narrow line region line ratios, a luminosity-dependent Lbol/L[OIII] bolometric correction, and the observed Mbh-sigma relation. These simulations indicate that, in massive (log(M*/Msun) > 10) galaxies, AGN accretion is correlated with specific star formation rate but is otherwise uniform with stellar mass. There is some hint of lower black hole occupation in low-mass (log(M*/Msun) < 10) hosts, although o...

  17. On Model Selection Criteria in Multimodel Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Ye, Ming; Meyer, Philip D.; Neuman, Shlomo P.

    2008-03-21

    Hydrologic systems are open and complex, rendering them prone to multiple conceptualizations and mathematical descriptions. There has been a growing tendency to postulate several alternative hydrologic models for a site and use model selection criteria to (a) rank these models, (b) eliminate some of them and/or (c) weigh and average predictions and statistics generated by multiple models. This has led to some debate among hydrogeologists about the merits and demerits of common model selection (also known as model discrimination or information) criteria such as AIC [Akaike, 1974], AICc [Hurvich and Tsai, 1989], BIC [Schwartz, 1978] and KIC [Kashyap, 1982] and some lack of clarity about the proper interpretation and mathematical representation of each criterion. In particular, whereas we [Neuman, 2003; Ye et al., 2004, 2005; Meyer et al., 2007] have based our approach to multimodel hydrologic ranking and inference on the Bayesian criterion KIC (which reduces asymptotically to BIC), Poeter and Anderson [2005] and Poeter and Hill [2007] have voiced a preference for the information-theoretic criterion AICc (which reduces asymptotically to AIC). Their preference stems in part from a perception that KIC and BIC require a "true" or "quasi-true" model to be in the set of alternatives while AIC and AICc are free of such an unreasonable requirement. We examine the model selection literature to find that (a) all published rigorous derivations of AIC and AICc require that the (true) model having generated the observational data be in the set of candidate models; (b) though BIC and KIC were originally derived by assuming that such a model is in the set, BIC has been rederived by Cavanaugh and Neath [1999] without the need for such an assumption; (c) KIC reduces to BIC as the number of observations becomes large relative to the number of adjustable model parameters, implying that it likewise does not require the existence of a true model in the set of alternatives; (d) if a true

  18. Analysis of an influence of the bias correction method on the projected changes of flood indices in the selected catchments in Poland

    Science.gov (United States)

    Osuch, Marzena; Lawrence, Deborah; Meresa, Hadush K.; Napiórkowski, Jaroslaw J.; Romanowicz, Renata J.

    2016-04-01

    The aim of the study is an estimation of the uncertainty in flood indices introduced by bias correction of climate model variables in ten catchments in Poland. A simulation approach is used to obtain daily flows in catchments under changing climatic conditions, following the RCP4.5 and RCP8.5 emission scenarios. Climate projections were obtained from the EURO-CORDEX initiative, and time series of precipitation and air temperature from different RCM/GCMs for the periods: 1971-2000, 2021-2050 and 2071-2100 were used. The climate model outputs in the Poland area are highly biased; therefore, additional post processing in the form of bias correction of precipitation and temperature is needed. In this work we used four versions of the quantile mapping method (empirical quantile mapping, and three distribution based mappings: double gamma, single gamma and Birnbaum-Sanders) for correction of the precipitation time series and one method for air temperature correction (empirical quantile method). The HBV rainfall-runoff catchment-based model is used to estimate future flow time series. The models are calibrated using the available precipitation, air temperature, and flow observations for the period 1971-2000. Model performance is evaluated using observed data for the period 2001-2010. We also verify performance using the EURO-CORDEX simulations for the reference period (1971-2000), both with and without bias correction of the RCM/GCM outputs. Finally, the models are run for the future climate simulated by the RCM/GCM models for the years: 2021-2050 and 2071-2100. Changes in the mean annual flood and in flood quantiles are analysed and the effect of bias correction on the estimated changes is also considered. The results indicate substantial differences between climate models and catchments. The regional variability has a close relationship with the flood regime type. Catchments where high flows are expected to increase have a rainfall-dominated flood regime in the current

  19. Limb darkening and exoplanets: testing stellar model atmospheres and indentifying biases in transit parameters

    CERN Document Server

    Espinoza, Néstor

    2015-01-01

    Limb-darkening is fundamental in determining transit lightcurve shapes, and is typically modeled by a variety of laws that parametrize the intensity profile of the star that is being transited. Confronted with a transit lightcurve, some authors fix the parameters of these laws, the so-called limb-darkening coefficients (LDCs), while others prefer to let them float in the lightcurve fitting procedure. Which of these is the best strategy, however, is still unclear, as well as how and by how much each of these can bias the retrieved transit parameters. In this work we attempt to clarify those points by first re-calculating these LDCs, comparing them to measured values from Kepler transit lightcurves using an algorithm that takes into account uncertainties in both the geometry of the transit and the parameters of the stellar host. We show there are significant departures from predicted model values, suggesting that our understanding of limb-darkening still needs to improve. Then, we show through simulations that ...

  20. Dopamine, cognitive biases and assessment of certainty: A neurocognitive model of delusions.

    Science.gov (United States)

    Broyd, Annabel; Balzan, Ryan P; Woodward, Todd S; Allen, Paul

    2017-06-01

    This paper examines the evidence that delusions can be explained within the framework of a neurocognitive model of how the brain assesses certainty. Here, 'certainty' refers to both low-level interpretations of one's environment and high-level (conscious) appraisals of one's beliefs and experiences. A model is proposed explaining how the brain systems responsible for assigning certainty might dysfunction, contributing to the cause and maintenance of delusional beliefs. It is suggested that delusions arise through a combination of perturbed striatal dopamine and aberrant salience as well as cognitive biases such as the tendency to jump to conclusions (JTC) and hypersalience of evidence-hypothesis matches. The role of emotion, stress, trauma and sociocultural factors in forming and modifying delusions is also considered. Understanding the mechanisms involved in forming and maintaining delusions has important clinical implications, as interventions that improve cognitive flexibility (e.g. cognitive remediation therapy and mindfulness training) could potentially attenuate neurocognitive processes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. A protocol to identify and minimise selection and information bias in abattoir surveys estimating prevalence, using Fasciola hepatica as an example.

    Science.gov (United States)

    Carroll, Rebecca I; Forbes, Andrew; Graham, David A; Messam, Locksley L McV

    2017-09-01

    Abattoir surveys and findings from post-mortem meat inspection are commonly used to estimate infection or disease prevalence in farm animal populations. However, the function of an abattoir is to slaughter animals for human consumption, and the collection of information on animal health for research purposes is a secondary objective. This can result in methodological shortcomings leading to biased prevalence estimates. Selection bias can occur when the study population as obtained from the abattoir is not an accurate representation of the target population. Virtually all of the tests used in abattoir surveys to detect infections or diseases that impact animal health are imperfect, leading to errors in identifying the outcome of interest and consequently, information bias. Examination of abattoir surveys estimating prevalence in the literature reveals shortcomings in the methods used in these studies. While the STROBE-Vet statement provides clear guidance on the reporting of observational research, we have not found any guidelines in the literature advising researchers on how to conduct abattoir surveys. This paper presents a protocol in two flowcharts to help researchers (regardless of their background in epidemiology) to first identify, and, where possible, minimise biases in abattoir surveys estimating prevalence. Flowchart 1 examines the identification of the target population and the appropriate study population while Flowchart 2 guides the researcher in identifying, and, where possible, correcting potential sources of outcome misclassification. Examples of simple sensitivity analyses are also presented which approximate the likely uncertainty in prevalence estimates due to systematic errors. Finally, the researcher is directed to outline any limitations of the study in the discussion section of the paper. This protocol makes it easier to conduct an abattoir survey using sound methods, identifying and, where possible, minimizing biases. Copyright © 2017

  2. Post-model selection inference and model averaging

    Directory of Open Access Journals (Sweden)

    Georges Nguefack-Tsague

    2011-07-01

    Full Text Available Although model selection is routinely used in practice nowadays, little is known about its precise effects on any subsequent inference that is carried out. The same goes for the effects induced by the closely related technique of model averaging. This paper is concerned with the use of the same data first to select a model and then to carry out inference, in particular point estimation and point prediction. The properties of the resulting estimator, called a post-model-selection estimator (PMSE, are hard to derive. Using selection criteria such as hypothesis testing, AIC, BIC, HQ and Cp, we illustrate that, in terms of risk function, no single PMSE dominates the others. The same conclusion holds more generally for any penalised likelihood information criterion. We also compare various model averaging schemes and show that no single one dominates the others in terms of risk function. Since PMSEs can be regarded as a special case of model averaging, with 0-1 random-weights, we propose a connection between the two theories, in the frequentist approach, by taking account of the selection procedure when performing model averaging. We illustrate the point by simulating a simple linear regression model.

  3. A Bayesian Approach for Uncertainty Quantification of Extreme Precipitation Projections Including Climate Model Interdependency and Nonstationary Bias

    DEFF Research Database (Denmark)

    Sunyer Pinya, Maria Antonia; Madsen, Henrik; Rosbjerg, Dan

    2014-01-01

    in climate model biases are negligible. This study develops a Bayesian framework that accounts for model dependencies and changes in model biases and compares it to estimates calculated based on a frequentist approach. The Bayesian framework is used to investigate the effects of the two assumptions......Climate change impact studies are subject to numerous uncertainties and assumptions. One of the main sources of uncertainty arises from the interpretation of climate model projections. Probabilistic procedures based on multimodel ensembles have been suggested in the literature to quantify...... this source of uncertainty. However, the interpretation of multimodel ensembles remains challenging. Several assumptions are often required in the uncertainty quantification of climate model projections. For example, most methods often assume that the climate models are independent and/or that changes...

  4. Biases in Long-term NO2 Averages Inferred from Satellite Observations Due to Cloud Selection Criteria

    Science.gov (United States)

    Geddes, Jeffrey A.; Murphy, Jennifer G.; O'Brien, Jason M.; Celarier, Edward A.

    2012-01-01

    Retrievals of atmospheric trace gas column densities from space are compromised by the presence of clouds, requiring most studies to exclude observations with significant cloud fractions in the instrument's field of view. Using NO2 observations at three ground stations representing urban, suburban, and rural environments, and tropospheric vertical column densities measured by the Ozone Monitoring Instrument (OMI) over each site, we show that the observations from space represent monthly averaged ground-level pollutant conditions well (R=0.86) under relatively cloud-free conditions. However, by analyzing the ground-level data and applying the OMI cloud fraction as a filter, we show there is a significant bias in long-term averaged NO2 as a result of removing the data during cloudy conditions. For the ground-based sites considered in this study, excluding observations on days when OMI-derived cloud fractions were greater than 0.2 causes 12:00-14:00 mean summer mixing ratios to be underestimated by 12%+/-6%, 20%+/-7%, and 40%+/-10% on average (+/-1 standard deviation) at the urban, suburban, and rural sites respectively. This bias was investigated in particular at the rural site, a region where pollutant transport is the main source of NO2, and where longterm observations of NOy were also available. Evidence of changing photochemical conditions and a correlation between clear skies and the transport of cleaner air masses play key roles in explaining the bias. The magnitude of a bias is expected to vary from site to site depending on meteorology and proximity to NOx sources, and decreases when longer averaging times of ground station data (e.g. 24-h) are used for the comparison.

  5. Model structure selection in convolutive mixtures

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai

    2006-01-01

    The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimoneous represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimoneous...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: 'Are we actually dealing with a convolutive mixture?'. We try to answer this question for EEG data....

  6. Model structure selection in convolutive mixtures

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai

    2006-01-01

    The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data....

  7. Skewed factor models using selection mechanisms

    KAUST Repository

    Kim, Hyoung-Moon

    2015-12-21

    Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-tt, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset.

  8. Avoiding progenitor bias: The structural and mass evolution of Brightest Group and Cluster Galaxies in Hierarchical models since z~1

    CERN Document Server

    Shankar, Francesco; Rettura, Alessandro; Bouillot, Vincent; Moreno, Jorge; Licitra, Rossella; Bernardi, Mariangela; Huertas-Company, Marc; Mei, Simona; Ascaso, Begoña; Sheth, Ravi; Delaye, Lauriane; Raichoor, Anand

    2015-01-01

    The mass and structural evolution of massive galaxies is one of the hottest topics in galaxy formation. This is because it may reveal invaluable insights into the still debated evolutionary processes governing the growth and assembly of spheroids. However, direct comparison between models and observations is usually prevented by the so-called "progenitor bias", i.e., new galaxies entering the observational selection at later epochs, thus eluding a precise study of how pre-existing galaxies actually evolve in size. To limit this effect, we here gather data on high-redshift brightest group and cluster galaxies, evolve their (mean) host halo masses down to z=0 along their main progenitors, and assign as their "descendants" local SDSS central galaxies matched in host halo mass. At face value, the comparison between high redshift and local data suggests a noticeable increase in stellar mass of a factor of >2 since z~1, and of >2.5 in mean effective radius. We then compare the inferred stellar mass and size growth ...

  9. Enhanced bias stability of solution-processed zinc-tin-oxide thin film transistors using self-assembled monolayer as a selective channel passivation.

    Science.gov (United States)

    Heo, Jae-Sang; Park, Sung-Kyu

    2013-10-01

    The enhanced positive bias stability of amorphous zinc-tin-oxide thin-film transistors (a-ZTO TFTs) were obtained by applying self-assembled monolayer (SAM) as a selective passivation layer on the metal-oxide back channel area. The a-ZTO TFTs with passivation layers such as poly(methyl methacylate) (PMMA), SAM, and SAM/PMMA were fabricated by simple solution methods. After deposition of the passivation layers, the electrical characteristics of a-ZTO TFTs have not been changed and the threshold voltage shift (deltaV(th)) under gate-bias stress for around 10(4) seconds was improved. The deltaV(th) of the devices with PMMA, SAM, and SAM/PMMA dual layer were 3.79 V, 3.2 V, and 2.17 V, respectively.

  10. The effects of juvenile stress on anxiety, cognitive bias and decision making in adulthood: a rat model.

    Directory of Open Access Journals (Sweden)

    Nichola M Brydges

    Full Text Available Stress experienced in childhood is associated with an increased risk of developing psychiatric disorders in adulthood. These disorders are particularly characterized by disturbances to emotional and cognitive processes, which are not currently fully modeled in animals. Assays of cognitive bias have recently been used with animals to give an indication of their emotional/cognitive state. We used a cognitive bias test, alongside a traditional measure of anxiety (elevated plus maze, to investigate the effects of juvenile stress (JS on adulthood behaviour using a rodent model. During the cognitive bias test, animals were trained to discriminate between two reward bowls based on a stimulus (rough/smooth sandpaper encountered before they reached the bowls. One stimulus (e.g. rough was associated with a lower value reward than the other (e.g. smooth. Once rats were trained, their cognitive bias was explored through the presentation of an ambiguous stimulus (intermediate grade sandpaper: a rat was classed as optimistic if it chose the bowl ordinarily associated with the high value reward. JS animals were lighter than controls, exhibited increased anxiety-like behaviour in the elevated plus maze and were more optimistic in the cognitive bias test. This increased optimism may represent an optimal foraging strategy for these underweight animals. JS animals were also faster than controls to make a decision when presented with an ambiguous stimulus, suggesting altered decision making. These results demonstrate that stress in the juvenile phase can increase anxiety-like behaviour and alter cognitive bias and decision making in adulthood in a rat model.

  11. The surprising negative correlation of gene length and optimal codon use--disentangling translational selection from GC-biased gene conversion in yeast.

    Science.gov (United States)

    Stoletzki, Nina

    2011-04-11

    Surprisingly, in several multi-cellular eukaryotes optimal codon use correlates negatively with gene length. This contrasts with the expectation under selection for translational accuracy. While suggested explanations focus on variation in strength and efficiency of translational selection, it has rarely been noticed that the negative correlation is reported only in organisms whose optimal codons are biased towards codons that end with G or C (-GC). This raises the question whether forces that affect base composition--such as GC-biased gene conversion--contribute to the negative correlation between optimal codon use and gene length. Yeast is a good organism to study this as equal numbers of optimal codons end in -GC and -AT and one may hence compare frequencies of optimal GC- with optimal AT-ending codons to disentangle the forces. Results of this study demonstrate in yeast frequencies of GC-ending (optimal AND non-optimal) codons decrease with gene length and increase with recombination. A decrease of GC-ending codons along genes contributes to the negative correlation with gene length. Correlations with recombination and gene expression differentiate between GC-ending and optimal codons, and also substitution patterns support effects of GC-biased gene conversion. While the general effect of GC-biased gene conversion is well known, the negative correlation of optimal codon use with gene length has not been considered in this context before. Initiation of gene conversion events in promoter regions and the presence of a gene conversion gradient most likely explain the observed decrease of GC-ending codons with gene length and gene position.

  12. Behavioral optimization models for multicriteria portfolio selection

    Directory of Open Access Journals (Sweden)

    Mehlawat Mukesh Kumar

    2013-01-01

    Full Text Available In this paper, behavioral construct of suitability is used to develop a multicriteria decision making framework for portfolio selection. To achieve this purpose, we rely on multiple methodologies. Analytical hierarchy process technique is used to model the suitability considerations with a view to obtaining the suitability performance score in respect of each asset. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor's rating on the financial criteria. Two optimization models are developed for optimal asset allocation considering simultaneously financial and suitability criteria. An empirical study is conducted on randomly selected assets from National Stock Exchange, Mumbai, India to demonstrate the effectiveness of the proposed methodology.

  13. Multi-dimensional model order selection

    Directory of Open Access Journals (Sweden)

    Roemer Florian

    2011-01-01

    Full Text Available Abstract Multi-dimensional model order selection (MOS techniques achieve an improved accuracy, reliability, and robustness, since they consider all dimensions jointly during the estimation of parameters. Additionally, from fundamental identifiability results of multi-dimensional decompositions, it is known that the number of main components can be larger when compared to matrix-based decompositions. In this article, we show how to use tensor calculus to extend matrix-based MOS schemes and we also present our proposed multi-dimensional model order selection scheme based on the closed-form PARAFAC algorithm, which is only applicable to multi-dimensional data. In general, as shown by means of simulations, the Probability of correct Detection (PoD of our proposed multi-dimensional MOS schemes is much better than the PoD of matrix-based schemes.

  14. Model selection and comparison for independents sinusoids

    DEFF Research Database (Denmark)

    Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt

    2014-01-01

    this method by considering the problem in a full Bayesian framework instead of the approximate formulation, on which the asymptotic MAP criterion is based. This leads to a new model selection and comparison method, the lp-BIC, whose computational complexity is of the same order as the asymptotic MAP criterion......In the signal processing literature, many methods have been proposed for estimating the number of sinusoidal basis functions from a noisy data set. The most popular method is the asymptotic MAP criterion, which is sometimes also referred to as the BIC. In this paper, we extend and improve....... Through simulations, we demonstrate that the lp-BIC outperforms the asymptotic MAP criterion and other state of the art methods in terms of model selection, de-noising and prediction performance. The simulation code is available online....

  15. Assessing performance and seasonal bias of pollen-based climate reconstructions in a perfect model world

    Science.gov (United States)

    Rehfeld, Kira; Trachsel, Mathias; Telford, Richard J.; Laepple, Thomas

    2016-12-01

    temporal changes of a dominant climate variable, such as summer temperatures in the model's Arctic, are imprinted on a less important variable, leading to reconstructions biased towards the dominant variable's trends. Our results, although based on a model vegetation that is inevitably simpler than reality, indicate that reconstructions of multiple climate variables based on modern spatial bio-indicator datasets should be treated with caution. Expert knowledge on the ecophysiological drivers of the proxies, as well as statistical methods that go beyond the cross validation on modern calibration datasets, are crucial to avoid misinterpretation.

  16. A Bayesian model of biases in artificial language learning: the case of a word-order universal.

    Science.gov (United States)

    Culbertson, Jennifer; Smolensky, Paul

    2012-01-01

    In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners' input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized learning biases. The test case is an experiment (Culbertson, Smolensky, & Legendre, 2012) targeting the learning of word-order patterns in the nominal domain. The model identifies internal biases of the experimental participants, providing evidence that learners impose (possibly arbitrary) properties on the grammars they learn, potentially resulting in the cross-linguistic regularities known as typological universals. Learners exposed to mixtures of artificial grammars tended to shift those mixtures in certain ways rather than others; the model reveals how learners' inferences are systematically affected by specific prior biases. These biases are in line with a typological generalization-Greenberg's Universal 18-which bans a particular word-order pattern relating nouns, adjectives, and numerals. Copyright © 2012 Cognitive Science Society, Inc.

  17. Assessing the Rothstein Test: Does It Really Show Teacher Value-Added Models Are Biased? Working Paper 5

    Science.gov (United States)

    Goldhaber, Dan; Chaplin, Duncan

    2012-01-01

    In a provocative and influential paper, Jesse Rothstein (2010) finds that standard value-added models (VAMs) suggest implausible future teacher effects on past student achievement, a finding that obviously cannot be viewed as causal. This is the basis of a falsification test (the Rothstein falsification test) that appears to indicate bias in VAM…

  18. Assessing the "Rothstein Test": Does It Really Show Teacher Value-Added Models Are Biased? Working Paper 71

    Science.gov (United States)

    Goldhaber, Dan; Chaplin, Duncan

    2012-01-01

    In a provocative and influential paper, Jesse Rothstein (2010) finds that standard value-added models (VAMs) suggest implausible future teacher effects on past student achievement, a finding that obviously cannot be viewed as causal. This is the basis of a falsification test (the Rothstein falsification test) that appears to indicate bias in VAM…

  19. Quantification of aggregation bias in regional agricultural land use models: application to Guácimo County, Costa Rica

    NARCIS (Netherlands)

    Jansen, H.G.P.; Stoorvogel, J.J.

    1998-01-01

    Different specifications of a land use model for Guacimo county in Costa Rica were used to quantify various sources of aggregation bias, including variation in farm resource endowments within and between representative farm classes; spatially variable prices; and labor market inter-dependencies betw

  20. Return predictability and intertemporal asset allocation: Evidence from a bias-adjusted VAR model

    DEFF Research Database (Denmark)

    Engsted, Tom; Pedersen, Thomas Quistgaard

    We extend the VAR based intertemporal asset allocation approach from Campbell et al. (2003) to the case where the VAR parameter estimates are adjusted for small- sample bias. We apply the analytical bias formula from Pope (1990) using both Campbell et al.'s dataset, and an extended dataset...... with quarterly data from 1952 to 2006. The results show that correcting the VAR parameters for small-sample bias has both quantitatively and qualitatively important e¤ects on the strategic intertemporal part of optimal portfolio choice, especially for bonds: for intermediate values of risk...

  1. Tracking Models for Optioned Portfolio Selection

    Science.gov (United States)

    Liang, Jianfeng

    In this paper we study a target tracking problem for the portfolio selection involving options. In particular, the portfolio in question contains a stock index and some European style options on the index. A refined tracking-error-variance methodology is adopted to formulate this problem as a multi-stage optimization model. We derive the optimal solutions based on stochastic programming and optimality conditions. Attention is paid to the structure of the optimal payoff function, which is shown to possess rich properties.

  2. New insights in portfolio selection modeling

    OpenAIRE

    Zareei, Abalfazl

    2016-01-01

    Recent advancements in the field of network theory commence a new line of developments in portfolio selection techniques that stands on the ground of perceiving financial market as a network with assets as nodes and links accounting for various types of relationships among financial assets. In the first chapter, we model the shock propagation mechanism among assets via network theory and provide an approach to construct well-diversified portfolios that are resilient to shock propagation and c...

  3. On Identifiability of Bias-Type Actuator-Sensor Faults in Multiple-Model-Based Fault Detection and Identification

    Science.gov (United States)

    Joshi, Suresh M.

    2012-01-01

    This paper explores a class of multiple-model-based fault detection and identification (FDI) methods for bias-type faults in actuators and sensors. These methods employ banks of Kalman-Bucy filters to detect the faults, determine the fault pattern, and estimate the fault values, wherein each Kalman-Bucy filter is tuned to a different failure pattern. Necessary and sufficient conditions are presented for identifiability of actuator faults, sensor faults, and simultaneous actuator and sensor faults. It is shown that FDI of simultaneous actuator and sensor faults is not possible using these methods when all sensors have biases.

  4. Mitigating BeiDou Satellite-Induced Code Bias: Taking into Account the Stochastic Model of Corrections.

    Science.gov (United States)

    Guo, Fei; Li, Xin; Liu, Wanke

    2016-06-18

    The BeiDou satellite-induced code biases have been confirmed to be orbit type-, frequency-, and elevation-dependent. Such code-phase divergences (code bias variations) severely affect absolute precise applications which use code measurements. To reduce their adverse effects, an improved correction model is proposed in this paper. Different from the model proposed by Wanninger and Beer (2015), more datasets (a time span of almost two years) were used to produce the correction values. More importantly, the stochastic information, i.e., the precision indexes, were given together with correction values in the improved model. However, only correction values were given while the precision indexes were completely missing in the traditional model. With the improved correction model, users may have a better understanding of their corrections, especially the uncertainty of corrections. Thus, it is helpful for refining the stochastic model of code observations. Validation tests in precise point positioning (PPP) reveal that a proper stochastic model is critical. The actual precision of the corrected code observations can be reflected in a more objective manner if the stochastic model of the corrections is taken into account. As a consequence, PPP solutions with the improved model outperforms the traditional one in terms of positioning accuracy, as well as convergence speed. In addition, the Melbourne-Wübbena (MW) combination which serves for ambiguity fixing were verified as well. The uncorrected MW values show strong systematic variations with an amplitude of half a wide-lane cycle, which prevents precise ambiguity determination and successful ambiguity resolution. After application of the code bias correction models, the systematic variations can be greatly removed, and the resulting wide lane ambiguities are more likely to be fixed. Moreover, the code residuals show more reasonable distributions after code bias corrections with either the traditional or the improved model.

  5. Mitigating BeiDou Satellite-Induced Code Bias: Taking into Account the Stochastic Model of Corrections

    Science.gov (United States)

    Guo, Fei; Li, Xin; Liu, Wanke

    2016-01-01

    The BeiDou satellite-induced code biases have been confirmed to be orbit type-, frequency-, and elevation-dependent. Such code-phase divergences (code bias variations) severely affect absolute precise applications which use code measurements. To reduce their adverse effects, an improved correction model is proposed in this paper. Different from the model proposed by Wanninger and Beer (2015), more datasets (a time span of almost two years) were used to produce the correction values. More importantly, the stochastic information, i.e., the precision indexes, were given together with correction values in the improved model. However, only correction values were given while the precision indexes were completely missing in the traditional model. With the improved correction model, users may have a better understanding of their corrections, especially the uncertainty of corrections. Thus, it is helpful for refining the stochastic model of code observations. Validation tests in precise point positioning (PPP) reveal that a proper stochastic model is critical. The actual precision of the corrected code observations can be reflected in a more objective manner if the stochastic model of the corrections is taken into account. As a consequence, PPP solutions with the improved model outperforms the traditional one in terms of positioning accuracy, as well as convergence speed. In addition, the Melbourne-Wübbena (MW) combination which serves for ambiguity fixing were verified as well. The uncorrected MW values show strong systematic variations with an amplitude of half a wide-lane cycle, which prevents precise ambiguity determination and successful ambiguity resolution. After application of the code bias correction models, the systematic variations can be greatly removed, and the resulting wide lane ambiguities are more likely to be fixed. Moreover, the code residuals show more reasonable distributions after code bias corrections with either the traditional or the improved model

  6. Intercomparsion of regional biases and doubled CO{sub 2}-sensitivity of coupled atmosphere-ocean general circulation model experiments

    Energy Technology Data Exchange (ETDEWEB)

    Kittel, T.G.F. [Nat. Center for Atmos. Res., Boulder, CO (United States). Climate and Global Dynamics Div.]|[Climate System Modeling Program, University Corporation for Atmospheric Research, Boulder, CO 80307-3000 (United States); Giorgi, F.; Meehl, G.A. [Nat. Center for Atmos. Res., Boulder, CO (United States). Climate and Global Dynamics Div.

    1998-01-01

    We compared regional biases and transient doubled CO{sub 2} sensitivities of nine coupled atmosphere-ocean general circulation models (GCMs) from six international climate modeling groups. We evaluated biases and responses in winter and summer surface air temperatures and precipitation for seven subcontinental regions, including those in the 1990 intergovernmental panel on climate change (IPCC) scientific assessment. Regional biases were large and exceeded the variance among four climatological datasets, indicating that model biases were not primarily due to uncertainty in observations. Model responses to altered greenhouse forcing were substantial (average temperature change=2.7{+-}0.9 C, range of precipitation change =-35 to +120% of control). While coupled models include more climate system feedbacks than earlier GCMs implemented with mixed-layer ocean models, inclusion of a dynamic ocean alone did not improve simulation of long-term mean climatology nor increase convergence among model responses to altered greenhouse gas forcing. On the other hand, features of some of the coupled models including flux adjustment (which may have simply masked simulation errors), high horizontal resolution, and estimation of screen height temperature contributed to improved simulation of long-term surface climate. The large range of model responses was partly accounted for by inconsistencies in forcing scenarios and transient-simulation averaging periods. Nonetheless, the models generally had greater agreement in their sensitivities than their controls did with observations. This suggests that consistent, large-scale response features from an ensemble of model sensitivity experiments may not depend on details of their representation of present-day climate. (orig.) With 4 figs., 2 tabs., 65 refs.

  7. Bayesian model selection in Gaussian regression

    CERN Document Server

    Abramovich, Felix

    2009-01-01

    We consider a Bayesian approach to model selection in Gaussian linear regression, where the number of predictors might be much larger than the number of observations. From a frequentist view, the proposed procedure results in the penalized least squares estimation with a complexity penalty associated with a prior on the model size. We investigate the optimality properties of the resulting estimator. We establish the oracle inequality and specify conditions on the prior that imply its asymptotic minimaxity within a wide range of sparse and dense settings for "nearly-orthogonal" and "multicollinear" designs.

  8. Model Selection in Data Analysis Competitions

    DEFF Research Database (Denmark)

    Wind, David Kofoed; Winther, Ole

    2014-01-01

    The use of data analysis competitions for selecting the most appropriate model for a problem is a recent innovation in the field of predictive machine learning. Two of the most well-known examples of this trend was the Netflix Competition and recently the competitions hosted on the online platform...... Kaggle. In this paper, we will state and try to verify a set of qualitative hypotheses about predictive modelling, both in general and in the scope of data analysis competitions. To verify our hypotheses we will look at previous competitions and their outcomes, use qualitative interviews with top...

  9. Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias

    Science.gov (United States)

    Nüske, Feliks; Wu, Hao; Prinz, Jan-Hendrik; Wehmeyer, Christoph; Clementi, Cecilia; Noé, Frank

    2017-03-01

    Many state-of-the-art methods for the thermodynamic and kinetic characterization of large and complex biomolecular systems by simulation rely on ensemble approaches, where data from large numbers of relatively short trajectories are integrated. In this context, Markov state models (MSMs) are extremely popular because they can be used to compute stationary quantities and long-time kinetics from ensembles of short simulations, provided that these short simulations are in "local equilibrium" within the MSM states. However, over the last 15 years since the inception of MSMs, it has been controversially discussed and not yet been answered how deviations from local equilibrium can be detected, whether these deviations induce a practical bias in MSM estimation, and how to correct for them. In this paper, we address these issues: We systematically analyze the estimation of MSMs from short non-equilibrium simulations, and we provide an expression for the error between unbiased transition probabilities and the expected estimate from many short simulations. We show that the unbiased MSM estimate can be obtained even from relatively short non-equilibrium simulations in the limit of long lag times and good discretization. Further, we exploit observable operator model (OOM) theory to derive an unbiased estimator for the MSM transition matrix that corrects for the effect of starting out of equilibrium, even when short lag times are used. Finally, we show how the OOM framework can be used to estimate the exact eigenvalues or relaxation time scales of the system without estimating an MSM transition matrix, which allows us to practically assess the discretization quality of the MSM. Applications to model systems and molecular dynamics simulation data of alanine dipeptide are included for illustration. The improved MSM estimator is implemented in PyEMMA of version 2.3.

  10. Potential Bias in Projecting Future Regional Megadrought Risk: Insights From A Global Data-Model Framework

    Science.gov (United States)

    Overpeck, J. T.; Ault, T.; Cole, J. E.; Fasullo, J.; Loope, G. R.; Parsons, L. A.; Stevenson, S.

    2015-12-01

    Megadrought is one of the most significant and costly climate extremes, and one that stakeholders (e.g., water and other resource managers) the world over wish to understand better; in particular, they need estimates of the risk of severe droughts as a function of drought frequency, severity, duration, and atmospheric greenhouse gas concentration. In many dry-climate regions of the globe, megadrought is synonymous with multi-decadal drought. However, in other regions, megadrought can be defined as extended drought, mostly not seen in the period of instrumental observations, and that would have large impacts if it were to occur in the future. New and published paleoclimatic observations allow us to understand the spectrum of drought in many regions of the globe; droughts exceeding 50 years have occurred in recent Earth history in southwestern North America, sub-Saharan Africa, the Mediterranean and Australia, whereas shorter megadroughts have occurred in Monsoon Asia, Amazonia and elsewhere. Data-model comparisons for regions with sufficiently long (e.g., 1000-2000 years) records of observed hydroclimatic variability suggest that state-of-the-art models can provide realistic estimates of interannual to decadal drought risk, but underestimate the risk of megadrought. Likely reasons for this shortcoming are the lack of sufficient multi-decadal variability in simulations of the past and future, plus an underappreciated understanding about how temperature variability and land-surface feedbacks interact with hydrological and ecological drought, as well as the roles played by unusually wet hydroclimatic extremes (e.g., ENSO related) in ending droughts of long duration. Paleoclimatic records also provide the opportunity to estimate how much models underestimate megadrought risk as a function of locale, frequency, severity, duration, and atmospheric greenhouse gas concentration; they also aid in providing stakeholders with bias-corrected estimates of megadrought risk.

  11. Biased thermohaline exchanges with the arctic across the Iceland-Faroe Ridge in ocean climate models

    Directory of Open Access Journals (Sweden)

    S. M. Olsen

    2015-07-01

    Full Text Available The northern limb of the Atlantic thermohaline circulation and its transport of heat and salt towards the Arctic strongly modulates the climate of the Northern Hemisphere. Presence of warm surface waters prevents ice formation in parts of the Arctic Mediterranean and ocean heat is in critical regions directly available for sea-ice melt, while salt transport may be critical for the stability of the exchanges. Hereby, ocean heat and salt transports play a disproportionally strong role in the climate system and realistic simulation is a requisite for reliable climate projections. Across the Greenland-Scotland Ridge (GSR this occurs in three well defined branches where anomalies in the warm and saline Atlantic inflow across the shallow Iceland-Faroe Ridge (IFR have shown particularly difficult to simulate in global ocean models. This branch (IF-inflow carries about 40 % of the total ocean heat transport into the Arctic Mediterranean and is well constrained by observation during the last two decades but is associated with significant inter-annual fluctuations. The inconsistency between model results and observational data is here explained by the inability of coarse resolution models to simulate the overflow across the IFR (IF-overflow, which feeds back on the simulated IF-inflow. In effect, this is reduced in the model to reflect only the net exchange across the IFR. Observational evidence is presented for a substantial and persistent IF-overflow and mechanisms that qualitatively control its intensity. Through this, we explain the main discrepancies between observed and simulated exchange. Our findings rebuild confidence in modeled net exchange across the IFR, but reveal that compensation of model deficiencies here through other exchange branches is not effective. This implies that simulated ocean heat transport to the Arctic is biased low by more than 10 % and associated with a reduced level of variability while the quality of the simulated salt

  12. Biased thermohaline exchanges with the Arctic across the Iceland-Faroe Ridge in ocean climate models

    Science.gov (United States)

    Olsen, S. M.; Hansen, B.; Østerhus, S.; Quadfasel, D.; Valdimarsson, H.

    2016-04-01

    The northern limb of the Atlantic thermohaline circulation and its transport of heat and salt towards the Arctic strongly modulate the climate of the Northern Hemisphere. The presence of warm surface waters prevents ice formation in parts of the Arctic Mediterranean, and ocean heat is directly available for sea-ice melt, while salt transport may be critical for the stability of the exchanges. Through these mechanisms, ocean heat and salt transports play a disproportionally strong role in the climate system, and realistic simulation is a requisite for reliable climate projections. Across the Greenland-Scotland Ridge (GSR) this occurs in three well-defined branches where anomalies in the warm and saline Atlantic inflow across the shallow Iceland-Faroe Ridge (IFR) have been shown to be particularly difficult to simulate in global ocean models. This branch (IF-inflow) carries about 40 % of the total ocean heat transport into the Arctic Mediterranean and is well constrained by observation during the last 2 decades but associated with significant inter-annual fluctuations. The inconsistency between model results and observational data is here explained by the inability of coarse-resolution models to simulate the overflow across the IFR (IF-overflow), which feeds back onto the simulated IF-inflow. In effect, this is reduced in the model to reflect only the net exchange across the IFR. Observational evidence is presented for a substantial and persistent IF-overflow and mechanisms that qualitatively control its intensity. Through this, we explain the main discrepancies between observed and simulated exchange. Our findings rebuild confidence in modelled net exchange across the IFR, but reveal that compensation of model deficiencies here through other exchange branches is not effective. This implies that simulated ocean heat transport to the Arctic is biased low by more than 10 % and associated with a reduced level of variability, while the quality of the simulated salt

  13. Regulation of an Autoimmune Model for Multiple Sclerosis in Th2-Biased GATA3 Transgenic Mice

    Directory of Open Access Journals (Sweden)

    Viromi Fernando

    2014-01-01

    Full Text Available T helper (Th2 cells have been proposed to play a neuroprotective role in multiple sclerosis (MS. This is mainly based on “loss-of-function” studies in an animal model for MS, experimental autoimmune encephalomyelitis (EAE, using blocking antibodies against Th2 related cytokines, and knockout mice lacking Th2-related molecules. We tested whether an increase of Th2 responses (“gain-of-function” approach could alter EAE, the approach of novel GATA binding protein 3 (GATA3-transgenic (tg mice that overexpress GATA3, a transcription factor required for Th2 differentiation. In EAE induced with myelin oligodendrocyte glycoprotein (MOG35−55 peptide, GATA3-tg mice had a significantly delayed onset of disease and a less severe maximum clinical score, compared with wild-type C57BL/6 mice. Histologically, GATA3-tg mice had decreased levels of meningitis and demyelination in the spinal cord, and anti-inflammatory cytokine profiles immunologically, however both groups developed similar levels of MOG-specific lymphoproliferative responses. During the early stage, we detected higher levels of interleukin (IL-4 and IL-10, with MOG and mitogen stimulation of regional lymph node cells in GATA3-tg mice. During the late stage, only mitogen stimulation induced higher IL-4 and lower interferon-γ and IL-17 production in GATA3-tg mice. These results suggest that a preexisting bias toward a Th2 immune response may reduce the severity of inflammatory demyelinating diseases, including MS.

  14. GOTHiC, a probabilistic model to resolve complex biases and to identify real interactions in Hi-C data.

    Science.gov (United States)

    Mifsud, Borbala; Martincorena, Inigo; Darbo, Elodie; Sugar, Robert; Schoenfelder, Stefan; Fraser, Peter; Luscombe, Nicholas M

    2017-01-01

    Hi-C is one of the main methods for investigating spatial co-localisation of DNA in the nucleus. However, the raw sequencing data obtained from Hi-C experiments suffer from large biases and spurious contacts, making it difficult to identify true interactions. Existing methods use complex models to account for biases and do not provide a significance threshold for detecting interactions. Here we introduce a simple binomial probabilistic model that resolves complex biases and distinguishes between true and false interactions. The model corrects biases of known and unknown origin and yields a p-value for each interaction, providing a reliable threshold based on significance. We demonstrate this experimentally by testing the method against a random ligation dataset. Our method outperforms previous methods and provides a statistical framework for further data analysis, such as comparisons of Hi-C interactions between different conditions. GOTHiC is available as a BioConductor package (http://www.bioconductor.org/packages/release/bioc/html/GOTHiC.html).

  15. Less healthy, but more active: Opposing selection biases when recruiting older people to a physical activity study through primary care

    Directory of Open Access Journals (Sweden)

    Carey Iain M

    2008-05-01

    Full Text Available Abstract Background Physical activity studies in older people experience poor recruitment. We wished to assess the influence of activity levels and health status on recruitment to a physical activity study in older people. Methods Comparison of participants and non-participants to a physical activity study using accelerometers in patients aged ≥ 65 years registered with a UK primary care centre. Logistic regression was used to calculate odds ratios (OR of participants in the accelerometer study with various adjustments. Analyses were initially adjusted for age, sex and household clustering; the health variables were then adjusted for physical activity levels and vice versa to look for independent effects. Results 43%(240/560 participated in the physical activity study. Age had no effect but males were more likely to participate than females OR 1.4(1.1–1.8. 46% (76/164 of non-participants sent the questionnaire returned it. The 240 participants reported greater physical activity than the 76 non-participants on all measures, eg faster walking OR 3.2(1.4–7.7, or 10.4(3.2–33.3 after adjustment for health variables. Participants reported more health problems; this effect became statistically significant after controlling for physical activity, eg disability OR 2.4(1.1–5.1. Conclusion Physical activity studies on older primary care patients may experience both a strong bias towards participants being more active and a weaker bias towards participants having more health problems and therefore primary care contact. The latter bias could be advantageous for physical activity intervention studies, where those with health problems need targeting.

  16. Model free audit methodology for bias evaluation of tumour progression in oncology.

    Science.gov (United States)

    Stone, Andrew; Macpherson, Euan; Smith, Ann; Jennison, Christopher

    2015-01-01

    Many oncology studies incorporate a blinded independent central review (BICR) to make an assessment of the integrity of the primary endpoint, progression free survival. Recently, it has been suggested that, in order to assess the potential for bias amongst investigators, a BICR amongst only a sample of patients could be performed; if evidence of bias is detected, according to a predefined threshold, the BICR is then assessed in all patients, otherwise, it is concluded that the sample was sufficient to rule out meaningful levels of bias. In this paper, we present an approach that adapts a method originally created for defining futility bounds in group sequential designs. The hazard ratio ratio, the ratio of the hazard ratio (HR) for the treatment effect estimated from the BICR to the corresponding HR for the investigator assessments, is used as the metric to define bias. The approach is simple to implement and ensures a high probability that a substantial true bias will be detected. In the absence of bias, there is a high probability of accepting the accuracy of local evaluations based on the sample, in which case an expensive BICR of all patients is avoided. The properties of the approach are demonstrated by retrospective application to a completed Phase III trial in colorectal cancer. The same approach could easily be adapted for other disease settings, and for test statistics other than the hazard ratio.

  17. Inflation model selection meets dark radiation

    Science.gov (United States)

    Tram, Thomas; Vallance, Robert; Vennin, Vincent

    2017-01-01

    We investigate how inflation model selection is affected by the presence of additional free-streaming relativistic degrees of freedom, i.e. dark radiation. We perform a full Bayesian analysis of both inflation parameters and cosmological parameters taking reheating into account self-consistently. We compute the Bayesian evidence for a few representative inflation scenarios in both the standard ΛCDM model and an extension including dark radiation parametrised by its effective number of relativistic species Neff. Using a minimal dataset (Planck low-l polarisation, temperature power spectrum and lensing reconstruction), we find that the observational status of most inflationary models is unchanged. The exceptions are potentials such as power-law inflation that predict large values for the scalar spectral index that can only be realised when Neff is allowed to vary. Adding baryon acoustic oscillations data and the B-mode data from BICEP2/Keck makes power-law inflation disfavoured, while adding local measurements of the Hubble constant H0 makes power-law inflation slightly favoured compared to the best single-field plateau potentials. This illustrates how the dark radiation solution to the H0 tension would have deep consequences for inflation model selection.

  18. Implementing a generic method for bias correction in statistical models using random effects, with spatial and population dynamics examples

    DEFF Research Database (Denmark)

    Thorson, James T.; Kristensen, Kasper

    2016-01-01

    Statistical models play an important role in fisheries science when reconciling ecological theory with available data for wild populations or experimental studies. Ecological models increasingly include both fixed and random effects, and are often estimated using maximum likelihood techniques...... abundance relative to the conventional plug-in estimator, and also gives essentially identical estimates to a sample-based bias-correction estimator. The epsilon-method has been implemented by us as a generic option in the open-source Template Model Builder software, and could be adapted within other....... Quantities of biological or management interest ("derived quantities") are then often calculated as nonlinear functions of fixed and random effect estimates. However, the conventional "plug-in" estimator for a derived quantity in a maximum likelihood mixed-effects model will be biased whenever the estimator...

  19. Biases in modeled surface snow BC mixing ratios in prescribed-aerosol climate model runs

    OpenAIRE

    Doherty, S. J.; C. M. Bitz; M. G. Flanner

    2014-01-01

    Black carbon (BC) in snow lowers its albedo, increasing the absorption of sunlight, leading to positive radiative forcing, climate warming and earlier snowmelt. A series of recent studies have used prescribed-aerosol deposition flux fields in climate model runs to assess the forcing by black carbon in snow. In these studies, the prescribed mass deposition flux of BC to surface snow is decoupled from the mass deposition flux of snow water to the surface. Here we compare progn...

  20. Efficiently adapting graphical models for selectivity estimation

    DEFF Research Database (Denmark)

    Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.

    2013-01-01

    of the selectivities of the constituent predicates. However, this independence assumption is more often than not wrong, and is considered to be the most common cause of sub-optimal query execution plans chosen by modern query optimizers. We take a step towards a principled and practical approach to performing...... cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss......Query optimizers rely on statistical models that succinctly describe the underlying data. Models are used to derive cardinality estimates for intermediate relations, which in turn guide the optimizer to choose the best query execution plan. The quality of the resulting plan is highly dependent...

  1. The Markowitz model for portfolio selection

    Directory of Open Access Journals (Sweden)

    MARIAN ZUBIA ZUBIAURRE

    2002-06-01

    Full Text Available Since its first appearance, The Markowitz model for portfolio selection has been a basic theoretical reference, opening several new development options. However, practically it has not been used among portfolio managers and investment analysts in spite of its success in the theoretical field. With our paper we would like to show how The Markowitz model may be of great help in real stock markets. Through an empirical study we want to verify the capability of Markowitz’s model to present portfolios with higher profitability and lower risk than the portfolio represented by IBEX-35 and IGBM indexes. Furthermore, we want to test suggested efficiency of these indexes as representatives of market theoretical-portfolio.

  2. Model selection for Poisson processes with covariates

    CERN Document Server

    Sart, Mathieu

    2011-01-01

    We observe $n$ inhomogeneous Poisson processes with covariates and aim at estimating their intensities. To handle this problem, we assume that the intensity of each Poisson process is of the form $s (\\cdot, x)$ where $x$ is the covariate and where $s$ is an unknown function. We propose a model selection approach where the models are used to approximate the multivariate function $s$. We show that our estimator satisfies an oracle-type inequality under very weak assumptions both on the intensities and the models. By using an Hellinger-type loss, we establish non-asymptotic risk bounds and specify them under various kind of assumptions on the target function $s$ such as being smooth or composite. Besides, we show that our estimation procedure is robust with respect to these assumptions.

  3. Information criteria for astrophysical model selection

    CERN Document Server

    Liddle, A R

    2007-01-01

    Model selection is the problem of distinguishing competing models, perhaps featuring different numbers of parameters. The statistics literature contains two distinct sets of tools, those based on information theory such as the Akaike Information Criterion (AIC), and those on Bayesian inference such as the Bayesian evidence and Bayesian Information Criterion (BIC). The Deviance Information Criterion combines ideas from both heritages; it is readily computed from Monte Carlo posterior samples and, unlike the AIC and BIC, allows for parameter degeneracy. I describe the properties of the information criteria, and as an example compute them from WMAP3 data for several cosmological models. I find that at present the information theory and Bayesian approaches give significantly different conclusions from that data.

  4. Entropic Priors and Bayesian Model Selection

    CERN Document Server

    Brewer, Brendon J

    2009-01-01

    We demonstrate that the principle of maximum relative entropy (ME), used judiciously, can ease the specification of priors in model selection problems. The resulting effect is that models that make sharp predictions are disfavoured, weakening the usual Bayesian "Occam's Razor". This is illustrated with a simple example involving what Jaynes called a "sure thing" hypothesis. Jaynes' resolution of the situation involved introducing a large number of alternative "sure thing" hypotheses that were possible before we observed the data. However, in more complex situations, it may not be possible to explicitly enumerate large numbers of alternatives. The entropic priors formalism produces the desired result without modifying the hypothesis space or requiring explicit enumeration of alternatives; all that is required is a good model for the prior predictive distribution for the data. This idea is illustrated with a simple rigged-lottery example, and we outline how this idea may help to resolve a recent debate amongst ...

  5. On commercial media bias

    OpenAIRE

    Germano, Fabrizio

    2008-01-01

    Within the spokes model of Chen and Riordan (2007) that allows for non-localized competition among arbitrary numbers of media outlets, we quantify the effect of concentration of ownership on quality and bias of media content. A main result shows that too few commercial outlets, or better, too few separate owners of commercial outlets can lead to substantial bias in equilibrium. Increasing the number of outlets (commercial and non-commercial) tends to bring down this bias; but the strongest ef...

  6. Possible role of warm SST bias in the simulation of boreal summer monsoon in SINTEX-F2 coupled model

    Energy Technology Data Exchange (ETDEWEB)

    Joseph, Susmitha; Sahai, A.K.; Goswami, B.N. [Indian Institute of Tropical Meteorology, Pune (India); Terray, Pascal; Masson, Sebastian [LOCEAN, Paris (France); Luo, J.J. [RIGC, Yokohama (Japan)

    2012-04-15

    Reasonably realistic climatology of atmospheric and oceanic parameters over the Asian monsoon region is a pre-requisite for models used for monsoon studies. The biases in representing these features lead to problems in representing the strength and variability of Indian summer monsoon (ISM). This study attempts to unravel the ability of a state-of-the-art coupled model, SINTEX-F2, in simulating these characteristics of ISM. The coupled model reproduces the precipitation and circulation climatology reasonably well. However, the mean ISM is weaker than observed, as evident from various monsoon indices. A wavenumber-frequency spectrum analysis reveals that the model intraseasonal oscillations are also weaker-than-observed. One possible reason for the weaker-than-observed ISM arises from the warm bias, over the tropical oceans, especially over the equatorial western Indian Ocean, inherent in the model. This warm bias is not only confined to the surface layers, but also extends through most of the troposphere. As a result of this warm bias, the coupled model has too weak meridional tropospheric temperature gradient to drive a realistic monsoon circulation. This in turn leads to a weakening of the moisture gradient as well as the vertical shear of easterlies required for sustained northward propagation of rain band, resulting in weak monsoon circulation. It is also noted that the recently documented interaction between the interannual and intraseasonal variabilities of ISM through very long breaks (VLBs) is poor in the model. This seems to be related to the inability of the model in simulating the eastward propagating Madden-Julian oscillation during VLBs. (orig.)

  7. Performance of criteria for selecting evolutionary models in phylogenetics: a comprehensive study based on simulated datasets

    Directory of Open Access Journals (Sweden)

    Luo Arong

    2010-08-01

    Full Text Available Abstract Background Explicit evolutionary models are required in maximum-likelihood and Bayesian inference, the two methods that are overwhelmingly used in phylogenetic studies of DNA sequence data. Appropriate selection of nucleotide substitution models is important because the use of incorrect models can mislead phylogenetic inference. To better understand the performance of different model-selection criteria, we used 33,600 simulated data sets to analyse the accuracy, precision, dissimilarity, and biases of the hierarchical likelihood-ratio test, Akaike information criterion, Bayesian information criterion, and decision theory. Results We demonstrate that the Bayesian information criterion and decision theory are the most appropriate model-selection criteria because of their high accuracy and precision. Our results also indicate that in some situations different models are selected by different criteria for the same dataset. Such dissimilarity was the highest between the hierarchical likelihood-ratio test and Akaike information criterion, and lowest between the Bayesian information criterion and decision theory. The hierarchical likelihood-ratio test performed poorly when the true model included a proportion of invariable sites, while the Bayesian information criterion and decision theory generally exhibited similar performance to each other. Conclusions Our results indicate that the Bayesian information criterion and decision theory should be preferred for model selection. Together with model-adequacy tests, accurate model selection will serve to improve the reliability of phylogenetic inference and related analyses.

  8. Irrational beliefs, biases and gambling: exploring the role of animal models in elucidating vulnerabilities for the development of pathological gambling.

    Science.gov (United States)

    Cocker, P J; Winstanley, C A

    2015-02-15

    Gambling is a heterogeneous and complex disorder. Multiple factors may lead to problem gambling, yet one of the most important appears to be the increased presence of cognitive biases or distortions. These biases are thought to precipitate gambling as they can lead to dysfunctional decision making under risk or ambiguity. Modelling these cognitive perturbations in animals can improve our understanding of their neurobiological bases, and potentially stimulate novel treatment options. The first aim of this review is to give a broad overview of some of the cognitive biases that are most commonly associated with gambling. Secondly, we will discuss several animal models that we have developed in which rodent decision-making appears hallmarked by the same cognitive inconsistencies as human choice. In particular, we will discuss two tasks that capture elements of risk and loss averse decision making, and another in which rats appear susceptible to the 'near-miss' effect. To date, findings from both human and non-human studies suggest that these different biases are neuropharmacologically and neurostructurally dissociable, and that dopamine plays a key role in their expression. Lastly, we will briefly discuss areas in both human and animal research where limitations within the field may be hampering a more complete understanding of pathological gambling as a disorder.

  9. Appropriate model selection methods for nonstationary generalized extreme value models

    Science.gov (United States)

    Kim, Hanbeen; Kim, Sooyoung; Shin, Hongjoon; Heo, Jun-Haeng

    2017-04-01

    Several evidences of hydrologic data series being nonstationary in nature have been found to date. This has resulted in the conduct of many studies in the area of nonstationary frequency analysis. Nonstationary probability distribution models involve parameters that vary over time. Therefore, it is not a straightforward process to apply conventional goodness-of-fit tests to the selection of an appropriate nonstationary probability distribution model. Tests that are generally recommended for such a selection include the Akaike's information criterion (AIC), corrected Akaike's information criterion (AICc), Bayesian information criterion (BIC), and likelihood ratio test (LRT). In this study, the Monte Carlo simulation was performed to compare the performances of these four tests, with regard to nonstationary as well as stationary generalized extreme value (GEV) distributions. Proper model selection ratios and sample sizes were taken into account to evaluate the performances of all the four tests. The BIC demonstrated the best performance with regard to stationary GEV models. In case of nonstationary GEV models, the AIC proved to be better than the other three methods, when relatively small sample sizes were considered. With larger sample sizes, the AIC, BIC, and LRT presented the best performances for GEV models which have nonstationary location and/or scale parameters, respectively. Simulation results were then evaluated by applying all four tests to annual maximum rainfall data of selected sites, as observed by the Korea Meteorological Administration.

  10. Reducing bias in maintenance energy expected progeny difference by accounting for selection on weaning and yearling weights.

    Science.gov (United States)

    Williams, J L; Garrick, D J; Speidel, S E

    2009-05-01

    Maintenance energy requirements of cattle can be predicted from published equations utilizing metabolic BW and milk production potential. Metabolic BW is a function of BW at a constant fat percentage or BCS. Pedigree and performance records can be used in random regression models to predict genetic merit for metabolic BW and milk production potentials. The purpose of this study was to present a methodology for predicting mature cow maintenance energy EPD using mature cow BW and BCS and accounting for prior selection of replacement females at weaning and yearling ages. Variance components were obtained for direct and maternal effects on weaning weight, direct effects on postweaning BW gain, and direct coefficients for random regression on mature weights (MW) adjusted for BCS. These BW were transformed into metabolic BW by taking BW to the power of 0.75, variance components were estimated for metabolic BW, and were then used to predict breeding values from which cow maintenance energy EPD could be derived. Data used in this analysis were obtained from the Red Angus Association of America and limited to herds with MW and corresponding BCS observations. The data set included 52,338 BW records on 21,103 individuals. Weaning and yearling contemporaries to those with MW observations, but with no MW records themselves, were included to account for selection occurring before maturity. Heritability estimates for weaning weight direct, weaning weight maternal, and postweaning BW gain were 0.18 +/- 0.02, 0.16 +/- 0.02, and 0.18 +/- 0.02, respectively. Mature BW observed at 2, 3, 4, 5, and 6 yr of age had heritability estimates of 0.45 +/- 0.03, 0.44 +/- 0.03, 0.49 +/- 0.03, 0.66 +/- 0.04, and 0.62 +/- 0.05, respectively. Correlations between weaning weight direct and MW ranged from 0.65 +/- 0.07 to 0.82 +/- 0.04, and correlations between MW at different ages ranged from 0.95 +/- 0.03 to 0.99 +/- 0.01. The genetic correlations between postweaning BW gain and MW ranged from 0

  11. Biases in the diurnal temperature range in Central Europe in an ensemble of regional climate models and their possible causes

    Energy Technology Data Exchange (ETDEWEB)

    Kysely, Jan [Institute of Atmospheric Physics AS CR, Prague 4 (Czech Republic); Plavcova, Eva [Institute of Atmospheric Physics AS CR, Prague 4 (Czech Republic); Charles University, Faculty of Mathematics and Physics, Prague (Czech Republic)

    2012-09-15

    The study examines how regional climate models (RCMs) reproduce the diurnal temperature range (DTR) in their control simulations over Central Europe. We evaluate 30-year runs driven by perfect boundary conditions (the ERA40 reanalysis, 1961-1990) and a global climate model (ECHAM5) of an ensemble of RCMs with 25-km resolution from the ENSEMBLES project. The RCMs' performance is compared against the dataset gridded from a high-density stations network. We find that all RCMs underestimate DTR in all seasons, notwithstanding whether driven by ERA40 or ECHAM5. Underestimation is largest in summer and smallest in winter in most RCMs. The relationship of the models' errors to indices of atmospheric circulation and cloud cover is discussed to reveal possible causes of the biases. In all seasons and all simulations driven by ERA40 and ECHAM5, underestimation of DTR is larger under anticyclonic circulation and becomes smaller or negligible for cyclonic circulation. In summer and transition seasons, underestimation tends to be largest for the southeast to south flow associated with warm advection, while in winter it does not depend on flow direction. We show that the biases in DTR, which seem common to all examined RCMs, are also related to cloud cover simulation. However, there is no general tendency to overestimate total cloud amount under anticyclonic conditions in the RCMs, which suggests the large negative bias in DTR for anticyclonic circulation cannot be explained by a bias in cloudiness. Errors in simulating heat and moisture fluxes between land surface and atmosphere probably contribute to the biases in DTR as well. (orig.)

  12. Effects of belief and logic on syllogistic reasoning: Eye-movement evidence for selective processing models.

    Science.gov (United States)

    Ball, Linden J; Phillips, Peter; Wade, Caroline N; Quayle, Jeremy D

    2006-01-01

    Studies of syllogistic reasoning have demonstrated a nonlogical tendency for people to endorse more believable conclusions than unbelievable ones. This belief bias effect is more dominant on invalid syllogisms than valid ones, giving rise to a logic by belief interaction. We report an experiment in which participants' eye movements were recorded in order to provide insights into the nature and time course of the reasoning processes associated with manipulations of conclusion validity and believability. Our main dependent measure was people's inspection times for syllogistic premises, and we tested predictions deriving from three contemporary mental-models accounts of the logic by belief interaction. Results supported recent "selective processing" theories of belief bias (e.g., Evans, 2000; Klauer, Musch, & Naumer, 2000), which assume that the believability of a conclusion biases model construction processes, rather than biasing the search for falsifying models (e.g., Oakhill & Johnson-Laird, 1985) or a response stage of reasoning arising from subjective uncertainty (e.g., Quayle & Ball, 2000). We conclude by suggesting that the eye-movement analyses in reasoning research may provide a useful adjunct to other process-tracing techniques such as verbal protocol analysis.

  13. Ancestral process and diffusion model with selection

    CERN Document Server

    Mano, Shuhei

    2008-01-01

    The ancestral selection graph in population genetics introduced by Krone and Neuhauser (1997) is an analogue to the coalescent genealogy. The number of ancestral particles, backward in time, of a sample of genes is an ancestral process, which is a birth and death process with quadratic death and linear birth rate. In this paper an explicit form of the number of ancestral particle is obtained, by using the density of the allele frequency in the corresponding diffusion model obtained by Kimura (1955). It is shown that fixation is convergence of the ancestral process to the stationary measure. The time to fixation of an allele is studied in terms of the ancestral process.

  14. A study of the Pythia 8 description of ATLAS minimum bias measurements with the Donnachie-Landshoff diffractive model

    CERN Document Server

    The ATLAS collaboration

    2016-01-01

    We present a new tune of the Pythia8 event generator, titled ``A3'' and suitable for inclusive QCD modelling, including minimum bias physics and pile-up overlay. The tuning uses the early Run~2 charged particle distribution and inelastic cross section results from ATLAS in addition to the Run~1 data used to construct previous minimum-bias tunes. For the first time, the tuning included a consideration of diffraction modelling parameters and a diffractive model other than the Pythia8 default is used in the final configuration. That resulted in a better descriptions of the measured inelastic cross-sections, and similar or better level of agreement compared to the currently used A2 tune for other distributions considered.

  15. A semiparametric censoring bias model f