WorldWideScience

Sample records for regression bias procedure

  1. Removing Malmquist bias from linear regressions

    Science.gov (United States)

    Verter, Frances

    1993-01-01

    Malmquist bias is present in all astronomical surveys where sources are observed above an apparent brightness threshold. Those sources which can be detected at progressively larger distances are progressively more limited to the intrinsically luminous portion of the true distribution. This bias does not distort any of the measurements, but distorts the sample composition. We have developed the first treatment to correct for Malmquist bias in linear regressions of astronomical data. A demonstration of the corrected linear regression that is computed in four steps is presented.

  2. Tax Evasion, Information Reporting, and the Regressive Bias Hypothesis

    DEFF Research Database (Denmark)

    Boserup, Simon Halphen; Pinje, Jori Veng

    A robust prediction from the tax evasion literature is that optimal auditing induces a regressive bias in effective tax rates compared to statutory rates. If correct, this will have important distributional consequences. Nevertheless, the regressive bias hypothesis has never been tested empirically...

  3. Tax Evasion, Information Reporting, and the Regressive Bias Prediction

    DEFF Research Database (Denmark)

    Boserup, Simon Halphen; Pinje, Jori Veng

    2013-01-01

    evasion and audit probabilities once we account for information reporting in the tax compliance game. When conditioning on information reporting, we find that both reduced-form evidence and simulations exhibit the predicted regressive bias. However, in the overall economy, this bias is negated by the tax......Models of rational tax evasion and optimal enforcement invariably predict a regressive bias in the effective tax system, which reduces redistribution in the economy. Using Danish administrative data, we show that a calibrated structural model of this type replicates moments and correlations of tax...

  4. Regression dilution bias: tools for correction methods and sample size calculation.

    Science.gov (United States)

    Berglund, Lars

    2012-08-01

    Random errors in measurement of a risk factor will introduce downward bias of an estimated association to a disease or a disease marker. This phenomenon is called regression dilution bias. A bias correction may be made with data from a validity study or a reliability study. In this article we give a non-technical description of designs of reliability studies with emphasis on selection of individuals for a repeated measurement, assumptions of measurement error models, and correction methods for the slope in a simple linear regression model where the dependent variable is a continuous variable. Also, we describe situations where correction for regression dilution bias is not appropriate. The methods are illustrated with the association between insulin sensitivity measured with the euglycaemic insulin clamp technique and fasting insulin, where measurement of the latter variable carries noticeable random error. We provide software tools for estimation of a corrected slope in a simple linear regression model assuming data for a continuous dependent variable and a continuous risk factor from a main study and an additional measurement of the risk factor in a reliability study. Also, we supply programs for estimation of the number of individuals needed in the reliability study and for choice of its design. Our conclusion is that correction for regression dilution bias is seldom applied in epidemiological studies. This may cause important effects of risk factors with large measurement errors to be neglected.

  5. Declining Bias and Gender Wage Discrimination? A Meta-Regression Analysis

    Science.gov (United States)

    Jarrell, Stephen B.; Stanley, T. D.

    2004-01-01

    The meta-regression analysis reveals that there is a strong tendency for discrimination estimates to fall and wage discrimination exist against the woman. The biasing effect of researchers' gender of not correcting for selection bias has weakened and changes in labor market have made it less important.

  6. Monte Carlo shielding analyses using an automated biasing procedure

    International Nuclear Information System (INIS)

    Tang, J.S.; Hoffman, T.J.

    1988-01-01

    A systematic and automated approach for biasing Monte Carlo shielding calculations is described. In particular, adjoint fluxes from a one-dimensional discrete ordinates calculation are used to generate biasing parameters for a Monte Carlo calculation. The entire procedure of adjoint calculation, biasing parameters generation, and Monte Carlo calculation has been automated. The automated biasing procedure has been applied to several realistic deep-penetration shipping cask problems. The results obtained for neutron and gamma-ray transport indicate that with the automated biasing procedure Monte Carlo shielding calculations of spent-fuel casks can be easily performed with minimum effort and that accurate results can be obtained at reasonable computing cost

  7. Length bias correction in gene ontology enrichment analysis using logistic regression.

    Science.gov (United States)

    Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

    2012-01-01

    When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.

  8. Bias in regression coefficient estimates upon different treatments of ...

    African Journals Online (AJOL)

    MS and PW consistently overestimated the population parameter. EM and RI, on the other hand, tended to consistently underestimate the population parameter under non-monotonic pattern. Keywords: Missing data, bias, regression, percent missing, non-normality, missing pattern > East African Journal of Statistics Vol.

  9. Bias correction by use of errors-in-variables regression models in studies with K-X-ray fluorescence bone lead measurements.

    Science.gov (United States)

    Lamadrid-Figueroa, Héctor; Téllez-Rojo, Martha M; Angeles, Gustavo; Hernández-Ávila, Mauricio; Hu, Howard

    2011-01-01

    In-vivo measurement of bone lead by means of K-X-ray fluorescence (KXRF) is the preferred biological marker of chronic exposure to lead. Unfortunately, considerable measurement error associated with KXRF estimations can introduce bias in estimates of the effect of bone lead when this variable is included as the exposure in a regression model. Estimates of uncertainty reported by the KXRF instrument reflect the variance of the measurement error and, although they can be used to correct the measurement error bias, they are seldom used in epidemiological statistical analyzes. Errors-in-variables regression (EIV) allows for correction of bias caused by measurement error in predictor variables, based on the knowledge of the reliability of such variables. The authors propose a way to obtain reliability coefficients for bone lead measurements from uncertainty data reported by the KXRF instrument and compare, by the use of Monte Carlo simulations, results obtained using EIV regression models vs. those obtained by the standard procedures. Results of the simulations show that Ordinary Least Square (OLS) regression models provide severely biased estimates of effect, and that EIV provides nearly unbiased estimates. Although EIV effect estimates are more imprecise, their mean squared error is much smaller than that of OLS estimates. In conclusion, EIV is a better alternative than OLS to estimate the effect of bone lead when measured by KXRF. Copyright © 2010 Elsevier Inc. All rights reserved.

  10. Large biases in regression-based constituent flux estimates: causes and diagnostic tools

    Science.gov (United States)

    Hirsch, Robert M.

    2014-01-01

    It has been documented in the literature that, in some cases, widely used regression-based models can produce severely biased estimates of long-term mean river fluxes of various constituents. These models, estimated using sample values of concentration, discharge, and date, are used to compute estimated fluxes for a multiyear period at a daily time step. This study compares results of the LOADEST seven-parameter model, LOADEST five-parameter model, and the Weighted Regressions on Time, Discharge, and Season (WRTDS) model using subsampling of six very large datasets to better understand this bias problem. This analysis considers sample datasets for dissolved nitrate and total phosphorus. The results show that LOADEST-7 and LOADEST-5, although they often produce very nearly unbiased results, can produce highly biased results. This study identifies three conditions that can give rise to these severe biases: (1) lack of fit of the log of concentration vs. log discharge relationship, (2) substantial differences in the shape of this relationship across seasons, and (3) severely heteroscedastic residuals. The WRTDS model is more resistant to the bias problem than the LOADEST models but is not immune to them. Understanding the causes of the bias problem is crucial to selecting an appropriate method for flux computations. Diagnostic tools for identifying the potential for bias problems are introduced, and strategies for resolving bias problems are described.

  11. Two biased estimation techniques in linear regression: Application to aircraft

    Science.gov (United States)

    Klein, Vladislav

    1988-01-01

    Several ways for detection and assessment of collinearity in measured data are discussed. Because data collinearity usually results in poor least squares estimates, two estimation techniques which can limit a damaging effect of collinearity are presented. These two techniques, the principal components regression and mixed estimation, belong to a class of biased estimation techniques. Detection and assessment of data collinearity and the two biased estimation techniques are demonstrated in two examples using flight test data from longitudinal maneuvers of an experimental aircraft. The eigensystem analysis and parameter variance decomposition appeared to be a promising tool for collinearity evaluation. The biased estimators had far better accuracy than the results from the ordinary least squares technique.

  12. A procedure for eliminating additive bias from cross-cultural survey data

    DEFF Research Database (Denmark)

    Scholderer, Joachim; Grunert, Klaus G.; Brunsø, Karen

    2005-01-01

    additive bias from cross-cultural data. The procedure involves four steps: (1) embed a potentially biased item in a factor-analytic measurement model, (2) test for the existence of additive bias between populations, (3) use the factor-analytic model to estimate the magnitude of the bias, and (4) replace......Measurement bias in cross-cultural surveys can seriously threaten the validity of hypothesis tests. Direct comparisons of means depend on the assumption that differences in observed variables reflect differences in the underlying constructs, and not an additive bias that may be caused by cultural...... differences in the understanding of item wording or response category labels. However, experience suggests that additive bias can be found more often than not. Based on the concept of partial measurement invariance (Byrne, Shavelson and Muthén 1989), the present paper develops a procedure for eliminating...

  13. An evaluation of bias in propensity score-adjusted non-linear regression models.

    Science.gov (United States)

    Wan, Fei; Mitra, Nandita

    2018-03-01

    Propensity score methods are commonly used to adjust for observed confounding when estimating the conditional treatment effect in observational studies. One popular method, covariate adjustment of the propensity score in a regression model, has been empirically shown to be biased in non-linear models. However, no compelling underlying theoretical reason has been presented. We propose a new framework to investigate bias and consistency of propensity score-adjusted treatment effects in non-linear models that uses a simple geometric approach to forge a link between the consistency of the propensity score estimator and the collapsibility of non-linear models. Under this framework, we demonstrate that adjustment of the propensity score in an outcome model results in the decomposition of observed covariates into the propensity score and a remainder term. Omission of this remainder term from a non-collapsible regression model leads to biased estimates of the conditional odds ratio and conditional hazard ratio, but not for the conditional rate ratio. We further show, via simulation studies, that the bias in these propensity score-adjusted estimators increases with larger treatment effect size, larger covariate effects, and increasing dissimilarity between the coefficients of the covariates in the treatment model versus the outcome model.

  14. Multivariate Bias Correction Procedures for Improving Water Quality Predictions from the SWAT Model

    Science.gov (United States)

    Arumugam, S.; Libera, D.

    2017-12-01

    Water quality observations are usually not available on a continuous basis for longer than 1-2 years at a time over a decadal period given the labor requirements making calibrating and validating mechanistic models difficult. Further, any physical model predictions inherently have bias (i.e., under/over estimation) and require post-simulation techniques to preserve the long-term mean monthly attributes. This study suggests a multivariate bias-correction technique and compares to a common technique in improving the performance of the SWAT model in predicting daily streamflow and TN loads across the southeast based on split-sample validation. The approach is a dimension reduction technique, canonical correlation analysis (CCA) that regresses the observed multivariate attributes with the SWAT model simulated values. The common approach is a regression based technique that uses an ordinary least squares regression to adjust model values. The observed cross-correlation between loadings and streamflow is better preserved when using canonical correlation while simultaneously reducing individual biases. Additionally, canonical correlation analysis does a better job in preserving the observed joint likelihood of observed streamflow and loadings. 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 compared for the observed period and over a multi-decadal period using loading estimates from the USGS LOADEST model. Lastly, the CCA technique is applied in a forecasting sense by using 1-month ahead forecasts of P & T from ECHAM4.5 as forcings in the SWAT model. Skill in using the SWAT model for forecasting loadings and streamflow at the monthly and seasonal timescale is also discussed.

  15. Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression.

    Science.gov (United States)

    Meng, Yilin; Roux, Benoît

    2015-08-11

    The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost.

  16. A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection.

    Science.gov (United States)

    Kwan, Johnny S H; Kung, Annie W C; Sham, Pak C

    2011-09-01

    Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias.

  17. Development and application of the automated Monte Carlo biasing procedure in SAS4

    International Nuclear Information System (INIS)

    Tang, J.S.; Broadhead, B.L.

    1993-01-01

    An automated approach for biasing Monte Carlo shielding calculations is described. In particular, adjoint fluxes from a one-dimensional discrete-ordinates calculation are used to generate biasing parameters for a three-dimensional Monte Carlo calculation. The automated procedure consisting of cross-section processing, adjoint flux determination, biasing parameter generation, and the initiation of a MORSE-SGC/S Monte Carlo calculation has been implemented in the SAS4 module of the SCALE computer code system. The automated procedure has been used extensively in the investigation of both computational and experimental benchmarks for the NEACRP working group on shielding assessment of transportation packages. The results of these studies indicate that with the automated biasing procedure, Monte Carlo shielding calculations of spent fuel casks can be easily performed with minimum effort and that accurate results can be obtained at reasonable computing cost. The systematic biasing approach described in this paper can also be applied to other similar shielding problems

  18. A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection

    OpenAIRE

    Kwan, Johnny S. H.; Kung, Annie W. C.; Sham, Pak C.

    2011-01-01

    Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias. © The Author(s) 2011.

  19. Bias due to two-stage residual-outcome regression analysis in genetic association studies.

    Science.gov (United States)

    Demissie, Serkalem; Cupples, L Adrienne

    2011-11-01

    Association studies of risk factors and complex diseases require careful assessment of potential confounding factors. Two-stage regression analysis, sometimes referred to as residual- or adjusted-outcome analysis, has been increasingly used in association studies of single nucleotide polymorphisms (SNPs) and quantitative traits. In this analysis, first, a residual-outcome is calculated from a regression of the outcome variable on covariates and then the relationship between the adjusted-outcome and the SNP is evaluated by a simple linear regression of the adjusted-outcome on the SNP. In this article, we examine the performance of this two-stage analysis as compared with multiple linear regression (MLR) analysis. Our findings show that when a SNP and a covariate are correlated, the two-stage approach results in biased genotypic effect and loss of power. Bias is always toward the null and increases with the squared-correlation between the SNP and the covariate (). For example, for , 0.1, and 0.5, two-stage analysis results in, respectively, 0, 10, and 50% attenuation in the SNP effect. As expected, MLR was always unbiased. Since individual SNPs often show little or no correlation with covariates, a two-stage analysis is expected to perform as well as MLR in many genetic studies; however, it produces considerably different results from MLR and may lead to incorrect conclusions when independent variables are highly correlated. While a useful alternative to MLR under , the two -stage approach has serious limitations. Its use as a simple substitute for MLR should be avoided. © 2011 Wiley Periodicals, Inc.

  20. Biased Decision Making in Realistic Extra-Procedural Nuclear Control Room Scenarios

    DEFF Research Database (Denmark)

    Andersen, Emil; Kozin, Igor; Maier, Anja

    In normal operations and emergency situations, operators of nuclear control rooms rely on procedures to guide their decision making. However, in emergency situations, where several interacting problems can cause unpredictable adverse effects, these procedures may be insufficient in guiding...... improve safety by creating procedures that bear the risks of these biases in mind, or by specifically aiming to debias the users. Avenues for debiasing through design are discussed....

  1. Internal correction of spectral interferences and mass bias for selenium metabolism studies using enriched stable isotopes in combination with multiple linear regression.

    Science.gov (United States)

    Lunøe, Kristoffer; Martínez-Sierra, Justo Giner; Gammelgaard, Bente; Alonso, J Ignacio García

    2012-03-01

    The analytical methodology for the in vivo study of selenium metabolism using two enriched selenium isotopes has been modified, allowing for the internal correction of spectral interferences and mass bias both for total selenium and speciation analysis. The method is based on the combination of an already described dual-isotope procedure with a new data treatment strategy based on multiple linear regression. A metabolic enriched isotope ((77)Se) is given orally to the test subject and a second isotope ((74)Se) is employed for quantification. In our approach, all possible polyatomic interferences occurring in the measurement of the isotope composition of selenium by collision cell quadrupole ICP-MS are taken into account and their relative contribution calculated by multiple linear regression after minimisation of the residuals. As a result, all spectral interferences and mass bias are corrected internally allowing the fast and independent quantification of natural abundance selenium ((nat)Se) and enriched (77)Se. In this sense, the calculation of the tracer/tracee ratio in each sample is straightforward. The method has been applied to study the time-related tissue incorporation of (77)Se in male Wistar rats while maintaining the (nat)Se steady-state conditions. Additionally, metabolically relevant information such as selenoprotein synthesis and selenium elimination in urine could be studied using the proposed methodology. In this case, serum proteins were separated by affinity chromatography while reverse phase was employed for urine metabolites. In both cases, (74)Se was used as a post-column isotope dilution spike. The application of multiple linear regression to the whole chromatogram allowed us to calculate the contribution of bromine hydride, selenium hydride, argon polyatomics and mass bias on the observed selenium isotope patterns. By minimising the square sum of residuals for the whole chromatogram, internal correction of spectral interferences and mass

  2. Development and application of the automated Monte Carlo biasing procedure in SAS4

    International Nuclear Information System (INIS)

    Tang, J.S.; Broadhead, B.L.

    1995-01-01

    An automated approach for biasing Monte Carlo shielding calculations is described. In particular, adjoint fluxes from a one-dimensional discrete-ordinates calculation are used to generate biasing parameters for a three-dimensional Monte Carlo calculation. The automated procedure consisting of cross-section processing, adjoint flux determination, biasing parameter generation, and the initiation of a MORSE-SGC/S Monte Carlo calculation has been implemented in the SAS4 module of the SCALE computer code system. (author)

  3. Comparison of some biased estimation methods (including ordinary subset regression) in the linear model

    Science.gov (United States)

    Sidik, S. M.

    1975-01-01

    Ridge, Marquardt's generalized inverse, shrunken, and principal components estimators are discussed in terms of the objectives of point estimation of parameters, estimation of the predictive regression function, and hypothesis testing. It is found that as the normal equations approach singularity, more consideration must be given to estimable functions of the parameters as opposed to estimation of the full parameter vector; that biased estimators all introduce constraints on the parameter space; that adoption of mean squared error as a criterion of goodness should be independent of the degree of singularity; and that ordinary least-squares subset regression is the best overall method.

  4. A Quantile Regression Approach to Estimating the Distribution of Anesthetic Procedure Time during Induction.

    Directory of Open Access Journals (Sweden)

    Hsin-Lun Wu

    Full Text Available Although procedure time analyses are important for operating room management, it is not easy to extract useful information from clinical procedure time data. A novel approach was proposed to analyze procedure time during anesthetic induction. A two-step regression analysis was performed to explore influential factors of anesthetic induction time (AIT. Linear regression with stepwise model selection was used to select significant correlates of AIT and then quantile regression was employed to illustrate the dynamic relationships between AIT and selected variables at distinct quantiles. A total of 1,060 patients were analyzed. The first and second-year residents (R1-R2 required longer AIT than the third and fourth-year residents and attending anesthesiologists (p = 0.006. Factors prolonging AIT included American Society of Anesthesiologist physical status ≧ III, arterial, central venous and epidural catheterization, and use of bronchoscopy. Presence of surgeon before induction would decrease AIT (p < 0.001. Types of surgery also had significant influence on AIT. Quantile regression satisfactorily estimated extra time needed to complete induction for each influential factor at distinct quantiles. Our analysis on AIT demonstrated the benefit of quantile regression analysis to provide more comprehensive view of the relationships between procedure time and related factors. This novel two-step regression approach has potential applications to procedure time analysis in operating room management.

  5. Penalized regression procedures for variable selection in the potential outcomes framework.

    Science.gov (United States)

    Ghosh, Debashis; Zhu, Yeying; Coffman, Donna L

    2015-05-10

    A recent topic of much interest in causal inference is model selection. In this article, we describe a framework in which to consider penalized regression approaches to variable selection for causal effects. The framework leads to a simple 'impute, then select' class of procedures that is agnostic to the type of imputation algorithm as well as penalized regression used. It also clarifies how model selection involves a multivariate regression model for causal inference problems and that these methods can be applied for identifying subgroups in which treatment effects are homogeneous. Analogies and links with the literature on machine learning methods, missing data, and imputation are drawn. A difference least absolute shrinkage and selection operator algorithm is defined, along with its multiple imputation analogs. The procedures are illustrated using a well-known right-heart catheterization dataset. Copyright © 2015 John Wiley & Sons, Ltd.

  6. 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

    small. Model error is accounted for in the weighted nonlinear regression methodology developed to estimate θ* 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...... 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....

  7. Anxiety-linked attentional bias and its modification: Illustrating the importance of distinguishing processes and procedures in experimental psychopathology research.

    Science.gov (United States)

    MacLeod, Colin; Grafton, Ben

    2016-11-01

    In this review of research concerning anxiety-linked attentional bias, we seek to illustrate a general principle that we contend applies across the breadth of experimental psychopathology. Specifically, we highlight how maintenance of a clear distinction between process and procedure serves to enhance the advancement of knowledge and understanding, while failure to maintain this distinction can foster confusion and misconception. We show how such clear differentiation has permitted the continuous refinement of assessment procedures, in ways that have led to growing confidence in the existence of the putative attentional bias process of interest, and also increasing understanding of its nature. In contrast, we show how a failure to consistently differentiate between process and procedure has contributed to confusion concerning whether or not attentional bias modification reliably alters anxiety vulnerability and dysfunction. As we demonstrate, such confusion can be avoided by distinguishing the process of attentional bias modification from the procedures that have been employed with the intention of evoking this target process. Such an approach reveals that procedures adopted with the intention of eliciting the attentional bias modification process do not always do so, but that successful evocation of the attentional bias modification process quite reliably alters anxiety symptomatology. We consider some of the specific implications for future research concerning attentional bias modification, while also pointing to the broader implications for experimental psychopathology research in general. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Wave-optics uncertainty propagation and regression-based bias model in GNSS radio occultation bending angle retrievals

    Directory of Open Access Journals (Sweden)

    M. E. Gorbunov

    2018-01-01

    Full Text Available A new reference occultation processing system (rOPS will include a Global Navigation Satellite System (GNSS radio occultation (RO retrieval chain with integrated uncertainty propagation. In this paper, we focus on wave-optics bending angle (BA retrieval in the lower troposphere and introduce (1 an empirically estimated boundary layer bias (BLB model then employed to reduce the systematic uncertainty of excess phases and bending angles in about the lowest 2 km of the troposphere and (2 the estimation of (residual systematic uncertainties and their propagation together with random uncertainties from excess phase to bending angle profiles. Our BLB model describes the estimated bias of the excess phase transferred from the estimated bias of the bending angle, for which the model is built, informed by analyzing refractivity fluctuation statistics shown to induce such biases. The model is derived from regression analysis using a large ensemble of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC RO observations and concurrent European Centre for Medium-Range Weather Forecasts (ECMWF analysis fields. It is formulated in terms of predictors and adaptive functions (powers and cross products of predictors, where we use six main predictors derived from observations: impact altitude, latitude, bending angle and its standard deviation, canonical transform (CT amplitude, and its fluctuation index. Based on an ensemble of test days, independent of the days of data used for the regression analysis to establish the BLB model, we find the model very effective for bias reduction and capable of reducing bending angle and corresponding refractivity biases by about a factor of 5. The estimated residual systematic uncertainty, after the BLB profile subtraction, is lower bounded by the uncertainty from the (indirect use of ECMWF analysis fields but is significantly lower than the systematic uncertainty without BLB correction. The

  9. Wave-optics uncertainty propagation and regression-based bias model in GNSS radio occultation bending angle retrievals

    Science.gov (United States)

    Gorbunov, Michael E.; Kirchengast, Gottfried

    2018-01-01

    A new reference occultation processing system (rOPS) will include a Global Navigation Satellite System (GNSS) radio occultation (RO) retrieval chain with integrated uncertainty propagation. In this paper, we focus on wave-optics bending angle (BA) retrieval in the lower troposphere and introduce (1) an empirically estimated boundary layer bias (BLB) model then employed to reduce the systematic uncertainty of excess phases and bending angles in about the lowest 2 km of the troposphere and (2) the estimation of (residual) systematic uncertainties and their propagation together with random uncertainties from excess phase to bending angle profiles. Our BLB model describes the estimated bias of the excess phase transferred from the estimated bias of the bending angle, for which the model is built, informed by analyzing refractivity fluctuation statistics shown to induce such biases. The model is derived from regression analysis using a large ensemble of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO observations and concurrent European Centre for Medium-Range Weather Forecasts (ECMWF) analysis fields. It is formulated in terms of predictors and adaptive functions (powers and cross products of predictors), where we use six main predictors derived from observations: impact altitude, latitude, bending angle and its standard deviation, canonical transform (CT) amplitude, and its fluctuation index. Based on an ensemble of test days, independent of the days of data used for the regression analysis to establish the BLB model, we find the model very effective for bias reduction and capable of reducing bending angle and corresponding refractivity biases by about a factor of 5. The estimated residual systematic uncertainty, after the BLB profile subtraction, is lower bounded by the uncertainty from the (indirect) use of ECMWF analysis fields but is significantly lower than the systematic uncertainty without BLB correction. The systematic and

  10. Estimation and correction of visibility bias in aerial surveys of wintering ducks

    Science.gov (United States)

    Pearse, A.T.; Gerard, P.D.; Dinsmore, S.J.; Kaminski, R.M.; Reinecke, K.J.

    2008-01-01

    Incomplete detection of all individuals leading to negative bias in abundance estimates is a pervasive source of error in aerial surveys of wildlife, and correcting that bias is a critical step in improving surveys. We conducted experiments using duck decoys as surrogates for live ducks to estimate bias associated with surveys of wintering ducks in Mississippi, USA. We found detection of decoy groups was related to wetland cover type (open vs. forested), group size (1?100 decoys), and interaction of these variables. Observers who detected decoy groups reported counts that averaged 78% of the decoys actually present, and this counting bias was not influenced by either covariate cited above. We integrated this sightability model into estimation procedures for our sample surveys with weight adjustments derived from probabilities of group detection (estimated by logistic regression) and count bias. To estimate variances of abundance estimates, we used bootstrap resampling of transects included in aerial surveys and data from the bias-correction experiment. When we implemented bias correction procedures on data from a field survey conducted in January 2004, we found bias-corrected estimates of abundance increased 36?42%, and associated standard errors increased 38?55%, depending on species or group estimated. We deemed our method successful for integrating correction of visibility bias in an existing sample survey design for wintering ducks in Mississippi, and we believe this procedure could be implemented in a variety of sampling problems for other locations and species.

  11. Confusing procedures with process when appraising the impact of cognitive bias modification on emotional vulnerability†.

    Science.gov (United States)

    Grafton, Ben; MacLeod, Colin; Rudaizky, Daniel; Holmes, Emily A; Salemink, Elske; Fox, Elaine; Notebaert, Lies

    2017-11-01

    If meta-analysis is to provide valuable answers, then it is critical to ensure clarity about the questions being asked. Here, we distinguish two important questions concerning cognitive bias modification research that are not differentiated in the meta-analysis recently published by Cristea et al (2015) in this journal: (1) do the varying procedures that investigators have employed with the intention of modifying cognitive bias, on average, significantly impact emotional vulnerability?; and (2) does the process of successfully modifying cognitive bias, on average, significantly impact emotional vulnerability? We reanalyse the data from Cristea et al to address this latter question. Our new analyses demonstrate that successfully modifying cognitive bias does significantly alter emotional vulnerability. We revisit Cristea et al 's conclusions in light of these findings. © The Royal College of Psychiatrists 2017.

  12. Unbalanced Regressions and the Predictive Equation

    DEFF Research Database (Denmark)

    Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo

    Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...

  13. A simplified procedure of linear regression in a preliminary analysis

    Directory of Open Access Journals (Sweden)

    Silvia Facchinetti

    2013-05-01

    Full Text Available The analysis of a statistical large data-set can be led by the study of a particularly interesting variable Y – regressed – and an explicative variable X, chosen among the remained variables, conjointly observed. The study gives a simplified procedure to obtain the functional link of the variables y=y(x by a partition of the data-set into m subsets, in which the observations are synthesized by location indices (mean or median of X and Y. Polynomial models for y(x of order r are considered to verify the characteristics of the given procedure, in particular we assume r= 1 and 2. The distributions of the parameter estimators are obtained by simulation, when the fitting is done for m= r + 1. Comparisons of the results, in terms of distribution and efficiency, are made with the results obtained by the ordinary least square methods. The study also gives some considerations on the consistency of the estimated parameters obtained by the given procedure.

  14. New robust statistical procedures for the polytomous logistic regression models.

    Science.gov (United States)

    Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

    2018-05-17

    This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

  15. A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design.

    Science.gov (United States)

    Meaney, Christopher; Moineddin, Rahim

    2014-01-24

    In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the

  16. Comparison of IRT Likelihood Ratio Test and Logistic Regression DIF Detection Procedures

    Science.gov (United States)

    Atar, Burcu; Kamata, Akihito

    2011-01-01

    The Type I error rates and the power of IRT likelihood ratio test and cumulative logit ordinal logistic regression procedures in detecting differential item functioning (DIF) for polytomously scored items were investigated in this Monte Carlo simulation study. For this purpose, 54 simulation conditions (combinations of 3 sample sizes, 2 sample…

  17. Simulating publication bias

    DEFF Research Database (Denmark)

    Paldam, Martin

    is censoring: selection by the size of estimate; SR3 selects the optimal combination of fit and size; and SR4 selects the first satisficing result. The last four SRs are steered by priors and result in bias. The MST and the FAT-PET have been developed for detection and correction of such bias. The simulations......Economic research typically runs J regressions for each selected for publication – it is often selected as the ‘best’ of the regressions. The paper examines five possible meanings of the word ‘best’: SR0 is ideal selection with no bias; SR1 is polishing: selection by statistical fit; SR2...... are made by data variation, while the model is the same. It appears that SR0 generates narrow funnels much at odds with observed funnels, while the other four funnels look more realistic. SR1 to SR4 give the mean a substantial bias that confirms the prior causing the bias. The FAT-PET MRA works well...

  18. Attention bias modification training under working memory load increases the magnitude of change in attentional bias.

    Science.gov (United States)

    Clarke, Patrick J F; Branson, Sonya; Chen, Nigel T M; Van Bockstaele, Bram; Salemink, Elske; MacLeod, Colin; Notebaert, Lies

    2017-12-01

    Attention bias modification (ABM) procedures have shown promise as a therapeutic intervention, however current ABM procedures have proven inconsistent in their ability to reliably achieve the requisite change in attentional bias needed to produce emotional benefits. This highlights the need to better understand the precise task conditions that facilitate the intended change in attention bias in order to realise the therapeutic potential of ABM procedures. Based on the observation that change in attentional bias occurs largely outside conscious awareness, the aim of the current study was to determine if an ABM procedure delivered under conditions likely to preclude explicit awareness of the experimental contingency, via the addition of a working memory load, would contribute to greater change in attentional bias. Bias change was assessed among 122 participants in response to one of four ABM tasks given by the two experimental factors of ABM training procedure delivered either with or without working memory load, and training direction of either attend-negative or avoid-negative. Findings revealed that avoid-negative ABM procedure under working memory load resulted in significantly greater reductions in attentional bias compared to the equivalent no-load condition. The current findings will require replication with clinical samples to determine the utility of the current task for achieving emotional benefits. These present findings are consistent with the position that the addition of a working memory load may facilitate change in attentional bias in response to an ABM training procedure. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Modified Regression Correlation Coefficient for Poisson Regression Model

    Science.gov (United States)

    Kaengthong, Nattacha; Domthong, Uthumporn

    2017-09-01

    This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).

  20. A generalization of voxel-wise procedures for highdimensional statistical inference using ridge regression

    DEFF Research Database (Denmark)

    Sjöstrand, Karl; Cardenas, Valerie A.; Larsen, Rasmus

    2008-01-01

    regression to address this issue, allowing for a gradual introduction of correlation information into the model. We make the connections between ridge regression and voxel-wise procedures explicit and discuss relations to other statistical methods. Results are given on an in-vivo data set of deformation......Whole-brain morphometry denotes a group of methods with the aim of relating clinical and cognitive measurements to regions of the brain. Typically, such methods require the statistical analysis of a data set with many variables (voxels and exogenous variables) paired with few observations (subjects...

  1. Measurement error in epidemiologic studies of air pollution based on land-use regression models.

    Science.gov (United States)

    Basagaña, Xavier; Aguilera, Inmaculada; Rivera, Marcela; Agis, David; Foraster, Maria; Marrugat, Jaume; Elosua, Roberto; Künzli, Nino

    2013-10-15

    Land-use regression (LUR) models are increasingly used to estimate air pollution exposure in epidemiologic studies. These models use air pollution measurements taken at a small set of locations and modeling based on geographical covariates for which data are available at all study participant locations. The process of LUR model development commonly includes a variable selection procedure. When LUR model predictions are used as explanatory variables in a model for a health outcome, measurement error can lead to bias of the regression coefficients and to inflation of their variance. In previous studies dealing with spatial predictions of air pollution, bias was shown to be small while most of the effect of measurement error was on the variance. In this study, we show that in realistic cases where LUR models are applied to health data, bias in health-effect estimates can be substantial. This bias depends on the number of air pollution measurement sites, the number of available predictors for model selection, and the amount of explainable variability in the true exposure. These results should be taken into account when interpreting health effects from studies that used LUR models.

  2. Controlling attribute effect in linear regression

    KAUST Repository

    Calders, Toon; Karim, Asim A.; Kamiran, Faisal; Ali, Wasif Mohammad; Zhang, Xiangliang

    2013-01-01

    In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the biasing effect of a given attribute such as gender or batch number. We show how propensity modeling can be used for factoring out the part of the bias that can be justified by externally provided explanatory attributes. Then we analytically derive linear models that minimize squared error while controlling the bias by imposing constraints on the mean outcome or residuals of the models. Experiments with discrimination-aware crime prediction and batch effect normalization tasks show that the proposed techniques are successful in controlling attribute effects in linear regression models. © 2013 IEEE.

  3. Controlling attribute effect in linear regression

    KAUST Repository

    Calders, Toon

    2013-12-01

    In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the biasing effect of a given attribute such as gender or batch number. We show how propensity modeling can be used for factoring out the part of the bias that can be justified by externally provided explanatory attributes. Then we analytically derive linear models that minimize squared error while controlling the bias by imposing constraints on the mean outcome or residuals of the models. Experiments with discrimination-aware crime prediction and batch effect normalization tasks show that the proposed techniques are successful in controlling attribute effects in linear regression models. © 2013 IEEE.

  4. Efficient bias correction for magnetic resonance image denoising.

    Science.gov (United States)

    Mukherjee, Partha Sarathi; Qiu, Peihua

    2013-05-30

    Magnetic resonance imaging (MRI) is a popular radiology technique that is used for visualizing detailed internal structure of the body. Observed MRI images are generated by the inverse Fourier transformation from received frequency signals of a magnetic resonance scanner system. Previous research has demonstrated that random noise involved in the observed MRI images can be described adequately by the so-called Rician noise model. Under that model, the observed image intensity at a given pixel is a nonlinear function of the true image intensity and of two independent zero-mean random variables with the same normal distribution. Because of such a complicated noise structure in the observed MRI images, denoised images by conventional denoising methods are usually biased, and the bias could reduce image contrast and negatively affect subsequent image analysis. Therefore, it is important to address the bias issue properly. To this end, several bias-correction procedures have been proposed in the literature. In this paper, we study the Rician noise model and the corresponding bias-correction problem systematically and propose a new and more effective bias-correction formula based on the regression analysis and Monte Carlo simulation. Numerical studies show that our proposed method works well in various applications. Copyright © 2012 John Wiley & Sons, Ltd.

  5. Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.

    Science.gov (United States)

    Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

    2016-01-01

    Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.

  6. On concurvity in nonlinear and nonparametric regression models

    Directory of Open Access Journals (Sweden)

    Sonia Amodio

    2014-12-01

    Full Text Available When data are affected by multicollinearity in the linear regression framework, then concurvity will be present in fitting a generalized additive model (GAM. The term concurvity describes nonlinear dependencies among the predictor variables. As collinearity results in inflated variance of the estimated regression coefficients in the linear regression model, the result of the presence of concurvity leads to instability of the estimated coefficients in GAMs. Even if the backfitting algorithm will always converge to a solution, in case of concurvity the final solution of the backfitting procedure in fitting a GAM is influenced by the starting functions. While exact concurvity is highly unlikely, approximate concurvity, the analogue of multicollinearity, is of practical concern as it can lead to upwardly biased estimates of the parameters and to underestimation of their standard errors, increasing the risk of committing type I error. We compare the existing approaches to detect concurvity, pointing out their advantages and drawbacks, using simulated and real data sets. As a result, this paper will provide a general criterion to detect concurvity in nonlinear and non parametric regression models.

  7. Assessment of participation bias in cohort studies: systematic review and meta-regression analysis

    Directory of Open Access Journals (Sweden)

    Sérgio Henrique Almeida da Silva Junior

    2015-11-01

    Full Text Available Abstract The proportion of non-participation in cohort studies, if associated with both the exposure and the probability of occurrence of the event, can introduce bias in the estimates of interest. The aim of this study is to evaluate the impact of participation and its characteristics in longitudinal studies. A systematic review (MEDLINE, Scopus and Web of Science for articles describing the proportion of participation in the baseline of cohort studies was performed. Among the 2,964 initially identified, 50 were selected. The average proportion of participation was 64.7%. Using a meta-regression model with mixed effects, only age, year of baseline contact and study region (borderline were associated with participation. Considering the decrease in participation in recent years, and the cost of cohort studies, it is essential to gather information to assess the potential for non-participation, before committing resources. Finally, journals should require the presentation of this information in the papers.

  8. A Generalized Logistic Regression Procedure to Detect Differential Item Functioning among Multiple Groups

    Science.gov (United States)

    Magis, David; Raiche, Gilles; Beland, Sebastien; Gerard, Paul

    2011-01-01

    We present an extension of the logistic regression procedure to identify dichotomous differential item functioning (DIF) in the presence of more than two groups of respondents. Starting from the usual framework of a single focal group, we propose a general approach to estimate the item response functions in each group and to test for the presence…

  9. Characterization of vegetative and grain filling periods of winter wheat by stepwise regression procedure. II. Grain filling period

    Directory of Open Access Journals (Sweden)

    Pržulj Novo

    2011-01-01

    Full Text Available In wheat, rate and duration of dry matter accumulation and remobilization depend on genotype and growing conditions. The objective of this study was to determine the most appropriate polynomial regression of stepwise regression procedure for describing grain filling period in three winter wheat cultivars. The stepwise regression procedure showed that grain filling is a complex biological process and that it is difficult to offer a simple and appropriate polynomial equation that fits the pattern of changes in dry matter accumulation during the grain filling period, i.e., from anthesis to maximum grain weight, in winter wheat. If grain filling is to be represented with a high power polynomial, quartic and quintic equations showed to be most appropriate. In spite of certain disadvantages, a cubic equation of stepwise regression could be used for describing the pattern of winter wheat grain filling.

  10. Bias and efficiency loss in regression estimates due to duplicated observations: a Monte Carlo simulation

    Directory of Open Access Journals (Sweden)

    Francesco Sarracino

    2017-04-01

    Full Text Available Recent studies documented that survey data contain duplicate records. We assess how duplicate records affect regression estimates, and we evaluate the effectiveness of solutions to deal with duplicate records. Results show that the chances of obtaining unbiased estimates when data contain 40 doublets (about 5% of the sample range between 3.5% and 11.5% depending on the distribution of duplicates. If 7 quintuplets are present in the data (2% of the sample, then the probability of obtaining biased estimates ranges between 11% and 20%. Weighting the duplicate records by the inverse of their multiplicity, or dropping superfluous duplicates outperform other solutions in all considered scenarios. Our results illustrate the risk of using data in presence of duplicate records and call for further research on strategies to analyze affected data.

  11. Procedures for Dealing with Optimism Bias in Transport Planning

    DEFF Research Database (Denmark)

    Flyvbjerg, Bent; Glenting, Carsten; Rønnest, Arne Kvist

    of the document are to provide empirically based optimism bias up-lifts for selected reference classes of transport infrastructure projects and provide guidance on using the established uplifts to produce more realistic forecasts for the individual project's capital expenditures. Furthermore, the underlying...... causes and institutional context for optimism bias in British transport projects are discussed and some possibilities for reducing optimism bias in project preparation and decision-making are identified....

  12. Reduced Rank Regression

    DEFF Research Database (Denmark)

    Johansen, Søren

    2008-01-01

    The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...

  13. Robust mislabel logistic regression without modeling mislabel probabilities.

    Science.gov (United States)

    Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

    2018-03-01

    Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

  14. Unbalanced Regressions and the Predictive Equation

    DEFF Research Database (Denmark)

    Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo

    Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...... in the theoretical predictive equation by suggesting a data generating process, where returns are generated as linear functions of a lagged latent I(0) risk process. The observed predictor is a function of this latent I(0) process, but it is corrupted by a fractionally integrated noise. Such a process may arise due...... to aggregation or unexpected level shifts. In this setup, the practitioner estimates a misspecified, unbalanced, and endogenous predictive regression. We show that the OLS estimate of this regression is inconsistent, but standard inference is possible. To obtain a consistent slope estimate, we then suggest...

  15. Longitudinal drop-out and weighting against its bias

    Directory of Open Access Journals (Sweden)

    Steffen C. E. Schmidt

    2017-12-01

    Full Text Available Abstract Background The bias caused by drop-out is an important factor in large population-based epidemiological studies. Many studies account for it by weighting their longitudinal data, but to date there is no detailed final approach for how to conduct these weights. Methods In this study we describe the observed longitudinal bias and a three-step longitudinal weighting approach used for the longitudinal data in the MoMo baseline (N = 4528, 4–17 years and wave 1 study with 2807 (62% participants between 2003 and 2012. Results The most meaningful drop-out predictors were socioeconomic status of the household, socioeconomic characteristics of the mother and daily TV usage. Weighting reduced the bias between the longitudinal participants and the baseline sample, and also increased variance by 5% to 35% with a final weighting efficiency of 41.67%. Conclusions We conclude that a weighting procedure is important to reduce longitudinal bias in health-oriented epidemiological studies and suggest identifying the most influencing variables in the first step, then use logistic regression modeling to calculate the inverse of the probability of participation in the second step, and finally trim and standardize the weights in the third step.

  16. Accounting for measurement error in log regression models with applications to accelerated testing.

    Science.gov (United States)

    Richardson, Robert; Tolley, H Dennis; Evenson, William E; Lunt, Barry M

    2018-01-01

    In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

  17. Accounting for measurement error in log regression models with applications to accelerated testing.

    Directory of Open Access Journals (Sweden)

    Robert Richardson

    Full Text Available In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

  18. Regression analysis with categorized regression calibrated exposure: some interesting findings

    Directory of Open Access Journals (Sweden)

    Hjartåker Anette

    2006-07-01

    Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a

  19. Reflecting Equity and Diversity. Part I: Guidelines and Procedure for Evaluating Bias in Instructional Materials. Part II: Bias Awareness Training Worksheets. Part III: Bias Awareness and Procedure Training Course.

    Science.gov (United States)

    Bebermeyer, Jim; Edmond, Mary, Ed.

    Reflecting a need to prepare students for working in diverse organizations, this document was developed to increase school officials' awareness of bias in instructional materials and help them select bias-free materials. A number of the examples illustrate situations dealing with diversity in the workplace. The guide is divided into three parts:…

  20. Coupled bias-variance tradeoff for cross-pose face recognition.

    Science.gov (United States)

    Li, Annan; Shan, Shiguang; Gao, Wen

    2012-01-01

    Subspace-based face representation can be looked as a regression problem. From this viewpoint, we first revisited the problem of recognizing faces across pose differences, which is a bottleneck in face recognition. Then, we propose a new approach for cross-pose face recognition using a regressor with a coupled bias-variance tradeoff. We found that striking a coupled balance between bias and variance in regression for different poses could improve the regressor-based cross-pose face representation, i.e., the regressor can be more stable against a pose difference. With the basic idea, ridge regression and lasso regression are explored. Experimental results on CMU PIE, the FERET, and the Multi-PIE face databases show that the proposed bias-variance tradeoff can achieve considerable reinforcement in recognition performance.

  1. Small sample GEE estimation of regression parameters for longitudinal data.

    Science.gov (United States)

    Paul, Sudhir; Zhang, Xuemao

    2014-09-28

    Longitudinal (clustered) response data arise in many bio-statistical applications which, in general, cannot be assumed to be independent. Generalized estimating equation (GEE) is a widely used method to estimate marginal regression parameters for correlated responses. The advantage of the GEE is that the estimates of the regression parameters are asymptotically unbiased even if the correlation structure is misspecified, although their small sample properties are not known. In this paper, two bias adjusted GEE estimators of the regression parameters in longitudinal data are obtained when the number of subjects is small. One is based on a bias correction, and the other is based on a bias reduction. Simulations show that the performances of both the bias-corrected methods are similar in terms of bias, efficiency, coverage probability, average coverage length, impact of misspecification of correlation structure, and impact of cluster size on bias correction. Both these methods show superior properties over the GEE estimates for small samples. Further, analysis of data involving a small number of subjects also shows improvement in bias, MSE, standard error, and length of the confidence interval of the estimates by the two bias adjusted methods over the GEE estimates. For small to moderate sample sizes (N ≤50), either of the bias-corrected methods GEEBc and GEEBr can be used. However, the method GEEBc should be preferred over GEEBr, as the former is computationally easier. For large sample sizes, the GEE method can be used. Copyright © 2014 John Wiley & Sons, Ltd.

  2. Empirical Comparison of Publication Bias Tests in Meta-Analysis.

    Science.gov (United States)

    Lin, Lifeng; Chu, Haitao; Murad, Mohammad Hassan; Hong, Chuan; Qu, Zhiyong; Cole, Stephen R; Chen, Yong

    2018-04-16

    Decision makers rely on meta-analytic estimates to trade off benefits and harms. Publication bias impairs the validity and generalizability of such estimates. The performance of various statistical tests for publication bias has been largely compared using simulation studies and has not been systematically evaluated in empirical data. This study compares seven commonly used publication bias tests (i.e., Begg's rank test, trim-and-fill, Egger's, Tang's, Macaskill's, Deeks', and Peters' regression tests) based on 28,655 meta-analyses available in the Cochrane Library. Egger's regression test detected publication bias more frequently than other tests (15.7% in meta-analyses of binary outcomes and 13.5% in meta-analyses of non-binary outcomes). The proportion of statistically significant publication bias tests was greater for larger meta-analyses, especially for Begg's rank test and the trim-and-fill method. The agreement among Tang's, Macaskill's, Deeks', and Peters' regression tests for binary outcomes was moderately strong (most κ's were around 0.6). Tang's and Deeks' tests had fairly similar performance (κ > 0.9). The agreement among Begg's rank test, the trim-and-fill method, and Egger's regression test was weak or moderate (κ < 0.5). Given the relatively low agreement between many publication bias tests, meta-analysts should not rely on a single test and may apply multiple tests with various assumptions. Non-statistical approaches to evaluating publication bias (e.g., searching clinical trials registries, records of drug approving agencies, and scientific conference proceedings) remain essential.

  3. Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

    Science.gov (United States)

    Gorgees, HazimMansoor; Mahdi, FatimahAssim

    2018-05-01

    This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

  4. Quantile Regression With Measurement Error

    KAUST Repository

    Wei, Ying

    2009-08-27

    Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.

  5. Understanding logistic regression analysis

    OpenAIRE

    Sperandei, Sandro

    2014-01-01

    Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using ex...

  6. The efficiency of modified jackknife and ridge type regression estimators: a comparison

    Directory of Open Access Journals (Sweden)

    Sharad Damodar Gore

    2008-09-01

    Full Text Available A common problem in multiple regression models is multicollinearity, which produces undesirable effects on the least squares estimator. To circumvent this problem, two well known estimation procedures are often suggested in the literature. They are Generalized Ridge Regression (GRR estimation suggested by Hoerl and Kennard iteb8 and the Jackknifed Ridge Regression (JRR estimation suggested by Singh et al. iteb13. The GRR estimation leads to a reduction in the sampling variance, whereas, JRR leads to a reduction in the bias. In this paper, we propose a new estimator namely, Modified Jackknife Ridge Regression Estimator (MJR. It is based on the criterion that combines the ideas underlying both the GRR and JRR estimators. We have investigated standard properties of this new estimator. From a simulation study, we find that the new estimator often outperforms the LASSO, and it is superior to both GRR and JRR estimators, using the mean squared error criterion. The conditions under which the MJR estimator is better than the other two competing estimators have been investigated.

  7. The Systematic Bias of Ingestible Core Temperature Sensors Requires a Correction by Linear Regression.

    Science.gov (United States)

    Hunt, Andrew P; Bach, Aaron J E; Borg, David N; Costello, Joseph T; Stewart, Ian B

    2017-01-01

    An accurate measure of core body temperature is critical for monitoring individuals, groups and teams undertaking physical activity in situations of high heat stress or prolonged cold exposure. This study examined the range in systematic bias of ingestible temperature sensors compared to a certified and traceable reference thermometer. A total of 119 ingestible temperature sensors were immersed in a circulated water bath at five water temperatures (TEMP A: 35.12 ± 0.60°C, TEMP B: 37.33 ± 0.56°C, TEMP C: 39.48 ± 0.73°C, TEMP D: 41.58 ± 0.97°C, and TEMP E: 43.47 ± 1.07°C) along with a certified traceable reference thermometer. Thirteen sensors (10.9%) demonstrated a systematic bias > ±0.1°C, of which 4 (3.3%) were > ± 0.5°C. Limits of agreement (95%) indicated that systematic bias would likely fall in the range of -0.14 to 0.26°C, highlighting that it is possible for temperatures measured between sensors to differ by more than 0.4°C. The proportion of sensors with systematic bias > ±0.1°C (10.9%) confirms that ingestible temperature sensors require correction to ensure their accuracy. An individualized linear correction achieved a mean systematic bias of 0.00°C, and limits of agreement (95%) to 0.00-0.00°C, with 100% of sensors achieving ±0.1°C accuracy. Alternatively, a generalized linear function (Corrected Temperature (°C) = 1.00375 × Sensor Temperature (°C) - 0.205549), produced as the average slope and intercept of a sub-set of 51 sensors and excluding sensors with accuracy outside ±0.5°C, reduced the systematic bias to Correction of sensor temperature to a reference thermometer by linear function eliminates this systematic bias (individualized functions) or ensures systematic bias is within ±0.1°C in 98% of the sensors (generalized function).

  8. Linear regression and the normality assumption.

    Science.gov (United States)

    Schmidt, Amand F; Finan, Chris

    2017-12-16

    Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates. Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient. Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings. Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. The number of subjects per variable required in linear regression analyses.

    Science.gov (United States)

    Austin, Peter C; Steyerberg, Ewout W

    2015-06-01

    To determine the number of independent variables that can be included in a linear regression model. We used a series of Monte Carlo simulations to examine the impact of the number of subjects per variable (SPV) on the accuracy of estimated regression coefficients and standard errors, on the empirical coverage of estimated confidence intervals, and on the accuracy of the estimated R(2) of the fitted model. A minimum of approximately two SPV tended to result in estimation of regression coefficients with relative bias of less than 10%. Furthermore, with this minimum number of SPV, the standard errors of the regression coefficients were accurately estimated and estimated confidence intervals had approximately the advertised coverage rates. A much higher number of SPV were necessary to minimize bias in estimating the model R(2), although adjusted R(2) estimates behaved well. The bias in estimating the model R(2) statistic was inversely proportional to the magnitude of the proportion of variation explained by the population regression model. Linear regression models require only two SPV for adequate estimation of regression coefficients, standard errors, and confidence intervals. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  10. Simple estimation procedures for regression analysis of interval-censored failure time data under the proportional hazards model.

    Science.gov (United States)

    Sun, Jianguo; Feng, Yanqin; Zhao, Hui

    2015-01-01

    Interval-censored failure time data occur in many fields including epidemiological and medical studies as well as financial and sociological studies, and many authors have investigated their analysis (Sun, The statistical analysis of interval-censored failure time data, 2006; Zhang, Stat Modeling 9:321-343, 2009). In particular, a number of procedures have been developed for regression analysis of interval-censored data arising from the proportional hazards model (Finkelstein, Biometrics 42:845-854, 1986; Huang, Ann Stat 24:540-568, 1996; Pan, Biometrics 56:199-203, 2000). For most of these procedures, however, one drawback is that they involve estimation of both regression parameters and baseline cumulative hazard function. In this paper, we propose two simple estimation approaches that do not need estimation of the baseline cumulative hazard function. The asymptotic properties of the resulting estimates are given, and an extensive simulation study is conducted and indicates that they work well for practical situations.

  11. Bias in the Listeria monocytogenes enrichment procedure: Lineage 2 strains outcompete lineage 1 strains in University of Vermont selective enrichments

    DEFF Research Database (Denmark)

    Bruhn, Jesper Bartholin; Vogel, Birte Fonnesbech; Gram, Lone

    2005-01-01

    compounds in UVM I and II influenced this bias. The results of the present study demonstrate that the selective procedures used for isolation of L. monocytogenes may not allow a true representation of the types present in foods. Our results could have a significant impact on epidemiological studies...

  12. Impact of multicollinearity on small sample hydrologic regression models

    Science.gov (United States)

    Kroll, Charles N.; Song, Peter

    2013-06-01

    Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.

  13. The Systematic Bias of Ingestible Core Temperature Sensors Requires a Correction by Linear Regression

    Directory of Open Access Journals (Sweden)

    Andrew P. Hunt

    2017-04-01

    Full Text Available An accurate measure of core body temperature is critical for monitoring individuals, groups and teams undertaking physical activity in situations of high heat stress or prolonged cold exposure. This study examined the range in systematic bias of ingestible temperature sensors compared to a certified and traceable reference thermometer. A total of 119 ingestible temperature sensors were immersed in a circulated water bath at five water temperatures (TEMP A: 35.12 ± 0.60°C, TEMP B: 37.33 ± 0.56°C, TEMP C: 39.48 ± 0.73°C, TEMP D: 41.58 ± 0.97°C, and TEMP E: 43.47 ± 1.07°C along with a certified traceable reference thermometer. Thirteen sensors (10.9% demonstrated a systematic bias > ±0.1°C, of which 4 (3.3% were > ± 0.5°C. Limits of agreement (95% indicated that systematic bias would likely fall in the range of −0.14 to 0.26°C, highlighting that it is possible for temperatures measured between sensors to differ by more than 0.4°C. The proportion of sensors with systematic bias > ±0.1°C (10.9% confirms that ingestible temperature sensors require correction to ensure their accuracy. An individualized linear correction achieved a mean systematic bias of 0.00°C, and limits of agreement (95% to 0.00–0.00°C, with 100% of sensors achieving ±0.1°C accuracy. Alternatively, a generalized linear function (Corrected Temperature (°C = 1.00375 × Sensor Temperature (°C − 0.205549, produced as the average slope and intercept of a sub-set of 51 sensors and excluding sensors with accuracy outside ±0.5°C, reduced the systematic bias to < ±0.1°C in 98.4% of the remaining sensors (n = 64. In conclusion, these data show that using an uncalibrated ingestible temperature sensor may provide inaccurate data that still appears to be statistically, physiologically, and clinically meaningful. Correction of sensor temperature to a reference thermometer by linear function eliminates this systematic bias (individualized functions or ensures

  14. Effect of Malmquist bias on correlation studies with IRAS data base

    Science.gov (United States)

    Verter, Frances

    1993-01-01

    The relationships between galaxy properties in the sample of Trinchieri et al. (1989) are reexamined with corrections for Malmquist bias. The linear correlations are tested and linear regressions are fit for log-log plots of L(FIR), L(H-alpha), and L(B) as well as ratios of these quantities. The linear correlations for Malmquist bias are corrected using the method of Verter (1988), in which each galaxy observation is weighted by the inverse of its sampling volume. The linear regressions are corrected for Malmquist bias by a new method invented here in which each galaxy observation is weighted by its sampling volume. The results of correlation and regressions among the sample are significantly changed in the anticipated sense that the corrected correlation confidences are lower and the corrected slopes of the linear regressions are lower. The elimination of Malmquist bias eliminates the nonlinear rise in luminosity that has caused some authors to hypothesize additional components of FIR emission.

  15. Predictive market segmentation model: An application of logistic regression model and CHAID procedure

    Directory of Open Access Journals (Sweden)

    Soldić-Aleksić Jasna

    2009-01-01

    Full Text Available Market segmentation presents one of the key concepts of the modern marketing. The main goal of market segmentation is focused on creating groups (segments of customers that have similar characteristics, needs, wishes and/or similar behavior regarding the purchase of concrete product/service. Companies can create specific marketing plan for each of these segments and therefore gain short or long term competitive advantage on the market. Depending on the concrete marketing goal, different segmentation schemes and techniques may be applied. This paper presents a predictive market segmentation model based on the application of logistic regression model and CHAID analysis. The logistic regression model was used for the purpose of variables selection (from the initial pool of eleven variables which are statistically significant for explaining the dependent variable. Selected variables were afterwards included in the CHAID procedure that generated the predictive market segmentation model. The model results are presented on the concrete empirical example in the following form: summary model results, CHAID tree, Gain chart, Index chart, risk and classification tables.

  16. Conditional Monte Carlo randomization tests for regression models.

    Science.gov (United States)

    Parhat, Parwen; Rosenberger, William F; Diao, Guoqing

    2014-08-15

    We discuss the computation of randomization tests for clinical trials of two treatments when the primary outcome is based on a regression model. We begin by revisiting the seminal paper of Gail, Tan, and Piantadosi (1988), and then describe a method based on Monte Carlo generation of randomization sequences. The tests based on this Monte Carlo procedure are design based, in that they incorporate the particular randomization procedure used. We discuss permuted block designs, complete randomization, and biased coin designs. We also use a new technique by Plamadeala and Rosenberger (2012) for simple computation of conditional randomization tests. Like Gail, Tan, and Piantadosi, we focus on residuals from generalized linear models and martingale residuals from survival models. Such techniques do not apply to longitudinal data analysis, and we introduce a method for computation of randomization tests based on the predicted rate of change from a generalized linear mixed model when outcomes are longitudinal. We show, by simulation, that these randomization tests preserve the size and power well under model misspecification. Copyright © 2014 John Wiley & Sons, Ltd.

  17. Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.

    Science.gov (United States)

    Schmidt, Amand F; Klungel, Olaf H; Groenwold, Rolf H H

    2016-01-01

    Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than -100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of -8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.

  18. Evaluation of Bias-Variance Trade-Off for Commonly Used Post-Summarizing Normalization Procedures in Large-Scale Gene Expression Studies

    Science.gov (United States)

    Qiu, Xing; Hu, Rui; Wu, Zhixin

    2014-01-01

    Normalization procedures are widely used in high-throughput genomic data analyses to remove various technological noise and variations. They are known to have profound impact to the subsequent gene differential expression analysis. Although there has been some research in evaluating different normalization procedures, few attempts have been made to systematically evaluate the gene detection performances of normalization procedures from the bias-variance trade-off point of view, especially with strong gene differentiation effects and large sample size. In this paper, we conduct a thorough study to evaluate the effects of normalization procedures combined with several commonly used statistical tests and MTPs under different configurations of effect size and sample size. We conduct theoretical evaluation based on a random effect model, as well as simulation and biological data analyses to verify the results. Based on our findings, we provide some practical guidance for selecting a suitable normalization procedure under different scenarios. PMID:24941114

  19. Spatial measurement error and correction by spatial SIMEX in linear regression models when using predicted air pollution exposures.

    Science.gov (United States)

    Alexeeff, Stacey E; Carroll, Raymond J; Coull, Brent

    2016-04-01

    Spatial modeling of air pollution exposures is widespread in air pollution epidemiology research as a way to improve exposure assessment. However, there are key sources of exposure model uncertainty when air pollution is modeled, including estimation error and model misspecification. We examine the use of predicted air pollution levels in linear health effect models under a measurement error framework. For the prediction of air pollution exposures, we consider a universal Kriging framework, which may include land-use regression terms in the mean function and a spatial covariance structure for the residuals. We derive the bias induced by estimation error and by model misspecification in the exposure model, and we find that a misspecified exposure model can induce asymptotic bias in the effect estimate of air pollution on health. We propose a new spatial simulation extrapolation (SIMEX) procedure, and we demonstrate that the procedure has good performance in correcting this asymptotic bias. We illustrate spatial SIMEX in a study of air pollution and birthweight in Massachusetts. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  20. Retro-regression--another important multivariate regression improvement.

    Science.gov (United States)

    Randić, M

    2001-01-01

    We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

  1. Spatial correlation in Bayesian logistic regression with misclassification

    DEFF Research Database (Denmark)

    Bihrmann, Kristine; Toft, Nils; Nielsen, Søren Saxmose

    2014-01-01

    Standard logistic regression assumes that the outcome is measured perfectly. In practice, this is often not the case, which could lead to biased estimates if not accounted for. This study presents Bayesian logistic regression with adjustment for misclassification of the outcome applied to data...

  2. Normalization Ridge Regression in Practice I: Comparisons Between Ordinary Least Squares, Ridge Regression and Normalization Ridge Regression.

    Science.gov (United States)

    Bulcock, J. W.

    The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…

  3. On a Robust MaxEnt Process Regression Model with Sample-Selection

    Directory of Open Access Journals (Sweden)

    Hea-Jung Kim

    2018-04-01

    Full Text Available In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt process regression model that assumes a MaxEnt prior distribution for its nonparametric regression function and finds that the MaxEnt process regression model includes the well-known Gaussian process regression (GPR model as a special case. Then, this special MaxEnt process regression model, i.e., the GPR model, is generalized to obtain a robust sample-selection Gaussian process regression (RSGPR model that deals with non-normal data in the sample selection. Various properties of the RSGPR model are established, including the stochastic representation, distributional hierarchy, and magnitude of the sample-selection bias. These properties are used in the paper to develop a hierarchical Bayesian methodology to estimate the model. This involves a simple and computationally feasible Markov chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function of the model. The performance of the RSGPR model in terms of the sample-selection bias correction, robustness to non-normality, and prediction, is demonstrated through results in simulations that attest to its good finite-sample performance.

  4. Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling

    Directory of Open Access Journals (Sweden)

    Eric R. Edelman

    2017-06-01

    Full Text Available For efficient utilization of operating rooms (ORs, accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT. We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT. TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related

  5. Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling.

    Science.gov (United States)

    Edelman, Eric R; van Kuijk, Sander M J; Hamaekers, Ankie E W; de Korte, Marcel J M; van Merode, Godefridus G; Buhre, Wolfgang F F A

    2017-01-01

    For efficient utilization of operating rooms (ORs), accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT) per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT) and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA) physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT). We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT). TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related benefits.

  6. Understanding logistic regression analysis.

    Science.gov (United States)

    Sperandei, Sandro

    2014-01-01

    Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

  7. Product Aggregation Bias as a Specification Error in Demand Systems

    OpenAIRE

    George C. Davis

    1997-01-01

    Inherent in all demand studies is some form of product aggregation which can lead to product aggregation bias. This article develops a simple procedure for incorporating product aggregation bias in demand systems that permits testing of product aggregation bias with a standard likelihood ratio test. An empirical illustration of the procedure demonstrates the importance of proper product aggregation. Copyright 1997, Oxford University Press.

  8. Imagine the bright side of life: A randomized controlled trial of two types of interpretation bias modification procedure targeting adolescent anxiety and depression.

    Directory of Open Access Journals (Sweden)

    E L de Voogd

    Full Text Available Anxiety and depression are highly prevalent during adolescence and characterized by negative interpretation biases. Cognitive bias modification of interpretations (CBM-I may reduce such biases and improve emotional functioning. However, as findings have been mixed and the traditional scenario training is experienced as relatively boring, a picture-based type of training might be more engaging and effective.The current study investigated short- and long-term effects (up to 6 months and users' experience of two types of CBM-I procedure in adolescents with heightened symptoms of anxiety or depression (N = 119, aged 12-18 year. Participants were randomized to eight online sessions of text-based scenario training, picture-word imagery training, or neutral control training.No significant group differences were observed on primary or secondary emotional outcomes. A decrease in anxiety and depressive symptoms, and improvements in emotional resilience were observed, irrespective of condition. Scenario training marginally reduced negative interpretation bias on a closely matched assessment task, while no such effects were found on a different task, nor for the picture-word or control group. Subjective evaluations of all training paradigms were relatively negative and the imagery component appeared particularly difficult for adolescents with higher symptom levels.The current results question the preventive efficacy and feasibility of both CBM-I procedures as implemented here in adolescents.

  9. Bootstrap-based procedures for inference in nonparametric receiver-operating characteristic curve regression analysis.

    Science.gov (United States)

    Rodríguez-Álvarez, María Xosé; Roca-Pardiñas, Javier; Cadarso-Suárez, Carmen; Tahoces, Pablo G

    2018-03-01

    Prior to using a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is essential. The receiver-operating characteristic curve is the measure of accuracy most widely used for continuous diagnostic tests. However, the possible impact of extra information about the patient (or even the environment) on diagnostic accuracy also needs to be assessed. In this paper, we focus on an estimator for the covariate-specific receiver-operating characteristic curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of generalised additive models for the receiver-operating characteristic curve. The main aim of the paper is to offer new inferential procedures for testing the effect of covariates on the conditional receiver-operating characteristic curve within the above-mentioned class. Specifically, two different bootstrap-based tests are suggested to check (a) the possible effect of continuous covariates on the receiver-operating characteristic curve and (b) the presence of factor-by-curve interaction terms. The validity of the proposed bootstrap-based procedures is supported by simulations. To facilitate the application of these new procedures in practice, an R-package, known as npROCRegression, is provided and briefly described. Finally, data derived from a computer-aided diagnostic system for the automatic detection of tumour masses in breast cancer is analysed.

  10. The usefulness of the Basic Question Procedure for determining non-response bias in substantive variables - A test of four telephone questionnaires

    NARCIS (Netherlands)

    van Goor, H.; van Goor, A.

    2007-01-01

    The Basic Question Procedure (BQP) is a method for determining non-response bias. The BQP involves asking one basic question - that is, the question relating to the central substantive variable of the study - of those persons who refuse to participate in the survey. We studied the usefulness of this

  11. Application of the step-wise regression procedure to the semi-empirical formulae of the nuclear binding energy

    International Nuclear Information System (INIS)

    Eissa, E.A.; Ayad, M.; Gashier, F.A.B.

    1984-01-01

    Most of the binding energy semi-empirical terms without the deformation corrections used by P.A. Seeger are arranged in a multiple linear regression form. The stepwise regression procedure with 95% confidence levels for acceptance and rejection of variables is applied for seeking a model for calculating binding energies of even-even (E-E) nuclei through a significance testing of each basic term. Partial F-values are taken as estimates for the significance of each term. The residual standard deviation and the overall F-value are used for selecting the best linear regression model. (E-E) nuclei are taken into sets lying between two successive proton and neutron magic numbers. The present work is in favour of the magic number 126 followed by 164 for the neutrons and indecisive in supporting the recently predicted proton magic number 114 rather than the previous one, 126. (author)

  12. Linear regression in astronomy. II

    Science.gov (United States)

    Feigelson, Eric D.; Babu, Gutti J.

    1992-01-01

    A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

  13. Forecaster Behaviour and Bias in Macroeconomic Forecasts

    OpenAIRE

    Roy Batchelor

    2007-01-01

    This paper documents the presence of systematic bias in the real GDP and inflation forecasts of private sector forecasters in the G7 economies in the years 1990–2005. The data come from the monthly Consensus Economics forecasting service, and bias is measured and tested for significance using parametric fixed effect panel regressions and nonparametric tests on accuracy ranks. We examine patterns across countries and forecasters to establish whether the bias reflects the inefficient use of i...

  14. A method for additive bias correction in cross-cultural surveys

    DEFF Research Database (Denmark)

    Scholderer, Joachim; Grunert, Klaus G.; Brunsø, Karen

    2001-01-01

    additive bias from cross-cultural data. The procedure involves four steps: (1) embed a potentially biased item in a factor-analytic measurement model, (2) test for the existence of additive bias between populations, (3) use the factor-analytic model to estimate the magnitude of the bias, and (4) replace......Measurement bias in cross-cultural surveys can seriously threaten the validity of hypothesis tests. Direct comparisons of means depend on the assumption that differences in observed variables reflect differences in the underlying constructs, and not an additive bias that may be caused by cultural...... differences in the understanding of item wording or response category labels. However, experience suggests that additive bias can be found more often than not. Based on the concept of partial measurement invariance (Byrne, Shavelson and Muthén, 1989), the present paper develops a procedure for eliminating...

  15. The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams.

    Science.gov (United States)

    Yu, Yuanyuan; Li, Hongkai; Sun, Xiaoru; Su, Ping; Wang, Tingting; Liu, Yi; Yuan, Zhongshang; Liu, Yanxun; Xue, Fuzhong

    2017-12-28

    Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The "do-calculus" was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the parent nodes of outcome, which

  16. The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

    Directory of Open Access Journals (Sweden)

    Yuanyuan Yu

    2017-12-01

    Full Text Available Abstract Background Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Methods Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM were compared. The “do-calculus” was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Results Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal

  17. Measuring Agricultural Bias

    DEFF Research Database (Denmark)

    Jensen, Henning Tarp; Robinson, Sherman; Tarp, Finn

    The measurement issue is the key issue in the literature on trade policy-induced agri-cultural price incentive bias. This paper introduces a general equilibrium effective rate of protection (GE-ERP) measure, which extends and generalizes earlier partial equilibrium nominal protection measures...... shares and intersectoral linkages - are crucial for determining the sign and magnitude of trade policy bias. The GE-ERP measure is therefore uniquely suited to capture the full impact of trade policies on agricultural price incentives. A Monte Carlo procedure confirms that the results are robust....... For the 15 sample countries, the results indicate that the agricultural price incentive bias, which was generally perceived to exist during the 1980s, was largely eliminated during the 1990s. The results also demonstrate that general equilibrium effects and country-specific characteristics - including trade...

  18. Bias versus bias: harnessing hindsight to reveal paranormal belief change beyond demand characteristics.

    Science.gov (United States)

    Kane, Michael J; Core, Tammy J; Hunt, R Reed

    2010-04-01

    Psychological change is difficult to assess, in part because self-reported beliefs and attitudes may be biased or distorted. The present study probed belief change, in an educational context, by using the hindsight bias to counter another bias that generally plagues assessment of subjective change. Although research has indicated that skepticism courses reduce paranormal beliefs, those findings may reflect demand characteristics (biases toward desired, skeptical responses). Our hindsight-bias procedure circumvented demand by asking students, following semester-long skepticism (and control) courses, to recall their precourse levels of paranormal belief. People typically remember themselves as previously thinking, believing, and acting as they do now, so current skepticism should provoke false recollections of previous skepticism. Given true belief change, therefore, skepticism students should have remembered themselves as having been more skeptical than they were. They did, at least about paranormal topics that were covered most extensively in the course. Our findings thus show hindsight to be useful in evaluating cognitive change beyond demand characteristics.

  19. Expectancy bias in a selective conditioning procedure: trait anxiety increases the threat value of a blocked stimulus.

    Science.gov (United States)

    Boddez, Yannick; Vervliet, Bram; Baeyens, Frank; Lauwers, Stephanie; Hermans, Dirk; Beckers, Tom

    2012-06-01

    In a blocking procedure, a single conditioned stimulus (CS) is paired with an unconditioned stimulus (US), such as electric shock, in the first stage. During the subsequent stage, the CS is presented together with a second CS and this compound is followed by the same US. Fear conditioning studies in non-human animals have demonstrated that fear responding to the added second CS typically remains low, despite its being paired with the US. Accordingly, the blocking procedure is well suited as a laboratory model for studying (deficits in) selective threat appraisal. The present study tested the relation between trait anxiety and blocking in human aversive conditioning. Healthy participants filled in a trait anxiety questionnaire and underwent blocking treatment in the human aversive conditioning paradigm. Threat appraisal was measured through shock expectancy ratings and skin conductance. As hypothesized, trait anxiety was positively associated with shock expectancy ratings to the blocked stimulus. In skin conductance responding, no significant effects of stimulus type could be detected during blocking training or testing. The current study does not allow strong claims to be made regarding the theoretical process underlying the expectancy bias we observed. The observed shock expectancy bias might be one of the mechanisms leading to non-specific fear in individuals at risk for developing anxiety disorders. A deficit in blocking, or a deficit in selective threat appraisal at the more general level, indeed results in fear becoming non-specific and disconnected from the most likely causes or predictors of danger. Copyright © 2011 Elsevier Ltd. All rights reserved.

  20. 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.;

  1. A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

    Science.gov (United States)

    Shen, Jianzhao; Gao, Sujuan

    2008-10-01

    In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

  2. A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression.

    Science.gov (United States)

    Liu, Fang; Eugenio, Evercita C

    2018-04-01

    Beta regression is an increasingly popular statistical technique in medical research for modeling of outcomes that assume values in (0, 1), such as proportions and patient reported outcomes. When outcomes take values in the intervals [0,1), (0,1], or [0,1], zero-or-one-inflated beta (zoib) regression can be used. We provide a thorough review on beta regression and zoib regression in the modeling, inferential, and computational aspects via the likelihood-based and Bayesian approaches. We demonstrate the statistical and practical importance of correctly modeling the inflation at zero/one rather than ad hoc replacing them with values close to zero/one via simulation studies; the latter approach can lead to biased estimates and invalid inferences. We show via simulation studies that the likelihood-based approach is computationally faster in general than MCMC algorithms used in the Bayesian inferences, but runs the risk of non-convergence, large biases, and sensitivity to starting values in the optimization algorithm especially with clustered/correlated data, data with sparse inflation at zero and one, and data that warrant regularization of the likelihood. The disadvantages of the regular likelihood-based approach make the Bayesian approach an attractive alternative in these cases. Software packages and tools for fitting beta and zoib regressions in both the likelihood-based and Bayesian frameworks are also reviewed.

  3. Nonparametric Mixture of Regression Models.

    Science.gov (United States)

    Huang, Mian; Li, Runze; Wang, Shaoli

    2013-07-01

    Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.

  4. Time-adaptive quantile regression

    DEFF Research Database (Denmark)

    Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik

    2008-01-01

    and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....

  5. Standardizing effect size from linear regression models with log-transformed variables for meta-analysis.

    Science.gov (United States)

    Rodríguez-Barranco, Miguel; Tobías, Aurelio; Redondo, Daniel; Molina-Portillo, Elena; Sánchez, María José

    2017-03-17

    Meta-analysis is very useful to summarize the effect of a treatment or a risk factor for a given disease. Often studies report results based on log-transformed variables in order to achieve the principal assumptions of a linear regression model. If this is the case for some, but not all studies, the effects need to be homogenized. We derived a set of formulae to transform absolute changes into relative ones, and vice versa, to allow including all results in a meta-analysis. We applied our procedure to all possible combinations of log-transformed independent or dependent variables. We also evaluated it in a simulation based on two variables either normally or asymmetrically distributed. In all the scenarios, and based on different change criteria, the effect size estimated by the derived set of formulae was equivalent to the real effect size. To avoid biased estimates of the effect, this procedure should be used with caution in the case of independent variables with asymmetric distributions that significantly differ from the normal distribution. We illustrate an application of this procedure by an application to a meta-analysis on the potential effects on neurodevelopment in children exposed to arsenic and manganese. The procedure proposed has been shown to be valid and capable of expressing the effect size of a linear regression model based on different change criteria in the variables. Homogenizing the results from different studies beforehand allows them to be combined in a meta-analysis, independently of whether the transformations had been performed on the dependent and/or independent variables.

  6. Measurement Error in Education and Growth Regressions

    NARCIS (Netherlands)

    Portela, Miguel; Alessie, Rob; Teulings, Coen

    2010-01-01

    The use of the perpetual inventory method for the construction of education data per country leads to systematic measurement error. This paper analyzes its effect on growth regressions. We suggest a methodology for correcting this error. The standard attenuation bias suggests that using these

  7. XSDRNPM-S biasing of MORSE-SGC/S shipping-cask calculations

    International Nuclear Information System (INIS)

    Hoffman, T.J.; Tang, J.S.

    1982-06-01

    This report describes implementation of a systematic approach for biasing a Monte Carlo radiation transport calculation. In particular, the adjoint fluxes from a one-dimensional discrete ordinates calculation with the XSDRNPM-S code are used to generate biasing parameters for the multigroup Monte Carlo code, MORSE-SGC/S. Application of this biasing procedure to several deep penetration spent fuel shipping cask problems is also reported. The results obtained for neutron and gamma-ray transport indicate that relatively inexpensive Monte Carlo calculations are possible for dry and water filled shipping cask problems using these procedures. 5 tables

  8. Combining Alphas via Bounded Regression

    Directory of Open Access Journals (Sweden)

    Zura Kakushadze

    2015-11-01

    Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.

  9. No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.

    Science.gov (United States)

    van Smeden, Maarten; de Groot, Joris A H; Moons, Karel G M; Collins, Gary S; Altman, Douglas G; Eijkemans, Marinus J C; Reitsma, Johannes B

    2016-11-24

    Ten events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth's correction, are compared. The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect ('separation'). We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth's correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

  10. Does neurocognitive function affect cognitive bias toward an emotional stimulus? Association between general attentional ability and attentional bias toward threat

    Directory of Open Access Journals (Sweden)

    Yuko eHakamata

    2014-08-01

    Full Text Available Background: Although poorer cognitive performance has been found to be associated with anxiety, it remains unclear whether neurocognitive function affects biased cognitive processing toward emotional information. We investigated whether general cognitive function evaluated with a standard neuropsychological test predicts biased cognition, focusing on attentional bias toward threat.Methods: One hundred and five healthy young adults completed a dot-probe task measuring attentional bias and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS measuring general cognitive function, which consists of five domains: immediate memory, visuospatial/constructional, language, attention, and delayed memory. Stepwise multiple regression analysis was performed to examine the relationships between attentional bias and cognitive function. Results: The attentional domain was the best predictor of attentional bias toward threat (β = -0.26, p = 0.006. Within the attentional domain, digit symbol coding was negatively correlated with attentional bias (r = -0.28, p = 0.005.Conclusions: The present study provides the first evidence that general attentional ability, which was assessed with a standard neuropsychological test, affects attentional bias toward threatening information. Individual cognitive profiles might be important for the measurement and modification of cognitive biases.

  11. Survival, Look-Ahead Bias and the Persistence in Hedge Fund Performance

    NARCIS (Netherlands)

    G. Baquero; J.R. ter Horst (Jenke); M.J.C.M. Verbeek (Marno)

    2005-01-01

    textabstractWe analyze the performance persistence in hedge funds taking into account look-ahead bias (multi-period sampling bias). We model liquidation of hedge funds by analyzing how it depends upon historical performance. Next, we use a weighting procedure that eliminates look-ahead bias in

  12. Adaptable history biases in human perceptual decisions.

    Science.gov (United States)

    Abrahamyan, Arman; Silva, Laura Luz; Dakin, Steven C; Carandini, Matteo; Gardner, Justin L

    2016-06-21

    When making choices under conditions of perceptual uncertainty, past experience can play a vital role. However, it can also lead to biases that worsen decisions. Consistent with previous observations, we found that human choices are influenced by the success or failure of past choices even in a standard two-alternative detection task, where choice history is irrelevant. The typical bias was one that made the subject switch choices after a failure. These choice history biases led to poorer performance and were similar for observers in different countries. They were well captured by a simple logistic regression model that had been previously applied to describe psychophysical performance in mice. Such irrational biases seem at odds with the principles of reinforcement learning, which would predict exquisite adaptability to choice history. We therefore asked whether subjects could adapt their irrational biases following changes in trial order statistics. Adaptability was strong in the direction that confirmed a subject's default biases, but weaker in the opposite direction, so that existing biases could not be eradicated. We conclude that humans can adapt choice history biases, but cannot easily overcome existing biases even if irrational in the current context: adaptation is more sensitive to confirmatory than contradictory statistics.

  13. Statistical methods for elimination of guarantee-time bias in cohort studies: a simulation study

    Directory of Open Access Journals (Sweden)

    In Sung Cho

    2017-08-01

    Full Text Available Abstract Background Aspirin has been considered to be beneficial in preventing cardiovascular diseases and cancer. Several pharmaco-epidemiology cohort studies have shown protective effects of aspirin on diseases using various statistical methods, with the Cox regression model being the most commonly used approach. However, there are some inherent limitations to the conventional Cox regression approach such as guarantee-time bias, resulting in an overestimation of the drug effect. To overcome such limitations, alternative approaches, such as the time-dependent Cox model and landmark methods have been proposed. This study aimed to compare the performance of three methods: Cox regression, time-dependent Cox model and landmark method with different landmark times in order to address the problem of guarantee-time bias. Methods Through statistical modeling and simulation studies, the performance of the above three methods were assessed in terms of type I error, bias, power, and mean squared error (MSE. In addition, the three statistical approaches were applied to a real data example from the Korean National Health Insurance Database. Effect of cumulative rosiglitazone dose on the risk of hepatocellular carcinoma was used as an example for illustration. Results In the simulated data, time-dependent Cox regression outperformed the landmark method in terms of bias and mean squared error but the type I error rates were similar. The results from real-data example showed the same patterns as the simulation findings. Conclusions While both time-dependent Cox regression model and landmark analysis are useful in resolving the problem of guarantee-time bias, time-dependent Cox regression is the most appropriate method for analyzing cumulative dose effects in pharmaco-epidemiological studies.

  14. Two Paradoxes in Linear Regression Analysis

    Science.gov (United States)

    FENG, Ge; PENG, Jing; TU, Dongke; ZHENG, Julia Z.; FENG, Changyong

    2016-01-01

    Summary Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection. PMID:28638214

  15. On a problematic procedure to manipulate response biases in recognition experiments: the case of "implied" base rates.

    Science.gov (United States)

    Bröder, Arndt; Malejka, Simone

    2017-07-01

    The experimental manipulation of response biases in recognition-memory tests is an important means for testing recognition models and for estimating their parameters. The textbook manipulations for binary-response formats either vary the payoff scheme or the base rate of targets in the recognition test, with the latter being the more frequently applied procedure. However, some published studies reverted to implying different base rates by instruction rather than actually changing them. Aside from unnecessarily deceiving participants, this procedure may lead to cognitive conflicts that prompt response strategies unknown to the experimenter. To test our objection, implied base rates were compared to actual base rates in a recognition experiment followed by a post-experimental interview to assess participants' response strategies. The behavioural data show that recognition-memory performance was estimated to be lower in the implied base-rate condition. The interview data demonstrate that participants used various second-order response strategies that jeopardise the interpretability of the recognition data. We thus advice researchers against substituting actual base rates with implied base rates.

  16. Apparatus bias and place conditioning with ethanol in mice.

    Science.gov (United States)

    Cunningham, Christopher L; Ferree, Nikole K; Howard, MacKenzie A

    2003-12-01

    Although the distinction between "biased" and "unbiased" is generally recognized as an important methodological issue in place conditioning, previous studies have not adequately addressed the distinction between a biased/unbiased apparatus and a biased/unbiased stimulus assignment procedure. Moreover, a review of the recent literature indicates that many reports (70% of 76 papers published in 2001) fail to provide adequate information about apparatus bias. This issue is important because the mechanisms underlying a drug's effect in the place-conditioning procedure may differ depending on whether the apparatus is biased or unbiased. The present studies were designed to assess the impact of apparatus bias and stimulus assignment procedure on ethanol-induced place conditioning in mice (DBA/2 J). A secondary goal was to compare various dependent variables commonly used to index conditioned place preference. Apparatus bias was manipulated by varying the combination of tactile (floor) cues available during preference tests. Experiment 1 used an unbiased apparatus in which the stimulus alternatives were equally preferred during a pre-test as indicated by the group average. Experiment 2 used a biased apparatus in which one of the stimuli was strongly preferred by most mice (mean % time on cue = 67%) during the pre-test. In both studies, the stimulus paired with drug (CS+) was assigned randomly (i.e., an "unbiased" stimulus assignment procedure). Experimental mice received four pairings of CS+ with ethanol (2 g/kg, i.p.) and four pairings of the alternative stimulus (CS-) with saline; control mice received saline on both types of trial. Each experiment concluded with a 60-min choice test. With the unbiased apparatus (experiment 1), significant place conditioning was obtained regardless of whether drug was paired with the subject's initially preferred or non-preferred stimulus. However, with the biased apparatus (experiment 2), place conditioning was apparent only when

  17. Adjustment of regional regression models of urban-runoff quality using data for Chattanooga, Knoxville, and Nashville, Tennessee

    Science.gov (United States)

    Hoos, Anne B.; Patel, Anant R.

    1996-01-01

    Model-adjustment procedures were applied to the combined data bases of storm-runoff quality for Chattanooga, Knoxville, and Nashville, Tennessee, to improve predictive accuracy for storm-runoff quality for urban watersheds in these three cities and throughout Middle and East Tennessee. Data for 45 storms at 15 different sites (five sites in each city) constitute the data base. Comparison of observed values of storm-runoff load and event-mean concentration to the predicted values from the regional regression models for 10 constituents shows prediction errors, as large as 806,000 percent. Model-adjustment procedures, which combine the regional model predictions with local data, are applied to improve predictive accuracy. Standard error of estimate after model adjustment ranges from 67 to 322 percent. Calibration results may be biased due to sampling error in the Tennessee data base. The relatively large values of standard error of estimate for some of the constituent models, although representing significant reduction (at least 50 percent) in prediction error compared to estimation with unadjusted regional models, may be unacceptable for some applications. The user may wish to collect additional local data for these constituents and repeat the analysis, or calibrate an independent local regression model.

  18. Use of Quantile Regression to Determine the Impact on Total Health Care Costs of Surgical Site Infections Following Common Ambulatory Procedures.

    Science.gov (United States)

    Olsen, Margaret A; Tian, Fang; Wallace, Anna E; Nickel, Katelin B; Warren, David K; Fraser, Victoria J; Selvam, Nandini; Hamilton, Barton H

    2017-02-01

    To determine the impact of surgical site infections (SSIs) on health care costs following common ambulatory surgical procedures throughout the cost distribution. Data on costs of SSIs following ambulatory surgery are sparse, particularly variation beyond just mean costs. We performed a retrospective cohort study of persons undergoing cholecystectomy, breast-conserving surgery, anterior cruciate ligament reconstruction, and hernia repair from December 31, 2004 to December 31, 2010 using commercial insurer claims data. SSIs within 90 days post-procedure were identified; infections during a hospitalization or requiring surgery were considered serious. We used quantile regression, controlling for patient, operative, and postoperative factors to examine the impact of SSIs on 180-day health care costs throughout the cost distribution. The incidence of serious and nonserious SSIs was 0.8% and 0.2%, respectively, after 21,062 anterior cruciate ligament reconstruction, 0.5% and 0.3% after 57,750 cholecystectomy, 0.6% and 0.5% after 60,681 hernia, and 0.8% and 0.8% after 42,489 breast-conserving surgery procedures. Serious SSIs were associated with significantly higher costs than nonserious SSIs for all 4 procedures throughout the cost distribution. The attributable cost of serious SSIs increased for both cholecystectomy and hernia repair as the quantile of total costs increased ($38,410 for cholecystectomy with serious SSI vs no SSI at the 70th percentile of costs, up to $89,371 at the 90th percentile). SSIs, particularly serious infections resulting in hospitalization or surgical treatment, were associated with significantly increased health care costs after 4 common surgical procedures. Quantile regression illustrated the differential effect of serious SSIs on health care costs at the upper end of the cost distribution.

  19. Model-based bootstrapping when correcting for measurement error with application to logistic regression.

    Science.gov (United States)

    Buonaccorsi, John P; Romeo, Giovanni; Thoresen, Magne

    2018-03-01

    When fitting regression models, measurement error in any of the predictors typically leads to biased coefficients and incorrect inferences. A plethora of methods have been proposed to correct for this. Obtaining standard errors and confidence intervals using the corrected estimators can be challenging and, in addition, there is concern about remaining bias in the corrected estimators. The bootstrap, which is one option to address these problems, has received limited attention in this context. It has usually been employed by simply resampling observations, which, while suitable in some situations, is not always formally justified. In addition, the simple bootstrap does not allow for estimating bias in non-linear models, including logistic regression. Model-based bootstrapping, which can potentially estimate bias in addition to being robust to the original sampling or whether the measurement error variance is constant or not, has received limited attention. However, it faces challenges that are not present in handling regression models with no measurement error. This article develops new methods for model-based bootstrapping when correcting for measurement error in logistic regression with replicate measures. The methodology is illustrated using two examples, and a series of simulations are carried out to assess and compare the simple and model-based bootstrap methods, as well as other standard methods. While not always perfect, the model-based approaches offer some distinct improvements over the other methods. © 2017, The International Biometric Society.

  20. Principal component regression analysis with SPSS.

    Science.gov (United States)

    Liu, R X; Kuang, J; Gong, Q; Hou, X L

    2003-06-01

    The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

  1. Regression filter for signal resolution

    International Nuclear Information System (INIS)

    Matthes, W.

    1975-01-01

    The problem considered is that of resolving a measured pulse height spectrum of a material mixture, e.g. gamma ray spectrum, Raman spectrum, into a weighed sum of the spectra of the individual constituents. The model on which the analytical formulation is based is described. The problem reduces to that of a multiple linear regression. A stepwise linear regression procedure was constructed. The efficiency of this method was then tested by transforming the procedure in a computer programme which was used to unfold test spectra obtained by mixing some spectra, from a library of arbitrary chosen spectra, and adding a noise component. (U.K.)

  2. Effects of Inventory Bias on Landslide Susceptibility Calculations

    Science.gov (United States)

    Stanley, T. A.; Kirschbaum, D. B.

    2017-01-01

    Many landslide inventories are known to be biased, especially inventories for large regions such as Oregon's SLIDO or NASA's Global Landslide Catalog. These biases must affect the results of empirically derived susceptibility models to some degree. We evaluated the strength of the susceptibility model distortion from postulated biases by truncating an unbiased inventory. We generated a synthetic inventory from an existing landslide susceptibility map of Oregon, then removed landslides from this inventory to simulate the effects of reporting biases likely to affect inventories in this region, namely population and infrastructure effects. Logistic regression models were fitted to the modified inventories. Then the process of biasing a susceptibility model was repeated with SLIDO data. We evaluated each susceptibility model with qualitative and quantitative methods. Results suggest that the effects of landslide inventory bias on empirical models should not be ignored, even if those models are, in some cases, useful. We suggest fitting models in well-documented areas and extrapolating across the study region as a possible approach to modeling landslide susceptibility with heavily biased inventories.

  3. Common method biases in behavioral research: a critical review of the literature and recommended remedies.

    Science.gov (United States)

    Podsakoff, Philip M; MacKenzie, Scott B; Lee, Jeong-Yeon; Podsakoff, Nathan P

    2003-10-01

    Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.

  4. Investigating d-cycloserine as a potential pharmacological enhancer of an emotional bias learning procedure.

    Science.gov (United States)

    Woud, Marcella L; Blackwell, Simon E; Steudte-Schmiedgen, Susann; Browning, Michael; Holmes, Emily A; Harmer, Catherine J; Margraf, Jürgen; Reinecke, Andrea

    2018-05-01

    The partial N-methyl-D-aspartate receptor agonist d-cycloserine may enhance psychological therapies. However, its exact mechanism of action is still being investigated. Cognitive bias modification techniques allow isolation of cognitive processes and thus investigation of how they may be affected by d-cycloserine. We used a cognitive bias modification paradigm targeting appraisals of a stressful event, Cognitive Bias Modification-Appraisal, to investigate whether d-cycloserine enhanced the modification of appraisal, and whether it caused greater reduction in indices of psychopathology. Participants received either 250 mg of d-cycloserine ( n=19) or placebo ( n=19). As a stressor task, participants recalled a negative life event, followed by positive Cognitive Bias Modification-Appraisal training. Before and after Cognitive Bias Modification-Appraisal, appraisals and indices of psychopathology related to the stressor were assessed. Cognitive Bias Modification-Appraisal successfully modified appraisals, but d-cycloserine did not affect appraisals post-training. There were no post-training group differences in frequency of intrusions. Interestingly, d-cycloserine led to a greater reduction in distress and impact on state mood from recalling the event, and lower distress post-training was associated with fewer intrusions. Therefore, d-cycloserine may affect emotional reactivity to recalling a negative event when combined with induction of a positive appraisal style, but via a mechanism other than enhanced learning of the appraisal style.

  5. No rationale for 1 variable per 10 events criterion for binary logistic regression analysis

    Directory of Open Access Journals (Sweden)

    Maarten van Smeden

    2016-11-01

    Full Text Available Abstract Background Ten events per variable (EPV is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. Methods The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth’s correction, are compared. Results The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect (‘separation’. We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth’s correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. Conclusions The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

  6. Bias-corrected Pearson estimating functions for Taylor's power law applied to benthic macrofauna data

    DEFF Research Database (Denmark)

    Jørgensen, Bent; Demétrio, Clarice G. B.; Kristensen, Erik

    2011-01-01

    Estimation of Taylor’s power law for species abundance data may be performed by linear regression of the log empirical variances on the log means, but this method suffers from a problem of bias for sparse data. We show that the bias may be reduced by using a bias-corrected Pearson estimating...

  7. Comparison between Linear and Nonlinear Regression in a Laboratory Heat Transfer Experiment

    Science.gov (United States)

    Gonçalves, Carine Messias; Schwaab, Marcio; Pinto, José Carlos

    2013-01-01

    In order to interpret laboratory experimental data, undergraduate students are used to perform linear regression through linearized versions of nonlinear models. However, the use of linearized models can lead to statistically biased parameter estimates. Even so, it is not an easy task to introduce nonlinear regression and show for the students…

  8. Assessment of passive drag in swimming by numerical simulation and analytical procedure.

    Science.gov (United States)

    Barbosa, Tiago M; Ramos, Rui; Silva, António J; Marinho, Daniel A

    2018-03-01

    The aim was to compare the passive drag-gliding underwater by a numerical simulation and an analytical procedure. An Olympic swimmer was scanned by computer tomography and modelled gliding at a 0.75-m depth in the streamlined position. Steady-state computer fluid dynamics (CFD) analyses were performed on Fluent. A set of analytical procedures was selected concurrently. Friction drag (D f ), pressure drag (D pr ), total passive drag force (D f +pr ) and drag coefficient (C D ) were computed between 1.3 and 2.5 m · s -1 by both techniques. D f +pr ranged from 45.44 to 144.06 N with CFD, from 46.03 to 167.06 N with the analytical procedure (differences: from 1.28% to 13.77%). C D ranged between 0.698 and 0.622 by CFD, 0.657 and 0.644 by analytical procedures (differences: 0.40-6.30%). Linear regression models showed a very high association for D f +pr plotted in absolute values (R 2  = 0.98) and after log-log transformation (R 2  = 0.99). The C D also obtained a very high adjustment for both absolute (R 2  = 0.97) and log-log plots (R 2  = 0.97). The bias for the D f +pr was 8.37 N and 0.076 N after logarithmic transformation. D f represented between 15.97% and 18.82% of the D f +pr by the CFD, 14.66% and 16.21% by the analytical procedures. Therefore, despite the bias, analytical procedures offer a feasible way of gathering insight on one's hydrodynamics characteristics.

  9. Noise Induces Biased Estimation of the Correction Gain.

    Directory of Open Access Journals (Sweden)

    Jooeun Ahn

    Full Text Available The detection of an error in the motor output and the correction in the next movement are critical components of any form of motor learning. Accordingly, a variety of iterative learning models have assumed that a fraction of the error is adjusted in the next trial. This critical fraction, the correction gain, learning rate, or feedback gain, has been frequently estimated via least-square regression of the obtained data set. Such data contain not only the inevitable noise from motor execution, but also noise from measurement. It is generally assumed that this noise averages out with large data sets and does not affect the parameter estimation. This study demonstrates that this is not the case and that in the presence of noise the conventional estimate of the correction gain has a significant bias, even with the simplest model. Furthermore, this bias does not decrease with increasing length of the data set. This study reveals this limitation of current system identification methods and proposes a new method that overcomes this limitation. We derive an analytical form of the bias from a simple regression method (Yule-Walker and develop an improved identification method. This bias is discussed as one of other examples for how the dynamics of noise can introduce significant distortions in data analysis.

  10. A robust background regression based score estimation algorithm for hyperspectral anomaly detection

    Science.gov (United States)

    Zhao, Rui; Du, Bo; Zhang, Liangpei; Zhang, Lefei

    2016-12-01

    Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement

  11. A machine learning model with human cognitive biases capable of learning from small and biased datasets.

    Science.gov (United States)

    Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

    2018-05-09

    Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

  12. Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches.

    Science.gov (United States)

    Valle, Denis; Lima, Joanna M Tucker; Millar, Justin; Amratia, Punam; Haque, Ubydul

    2015-11-04

    Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.

  13. The Collinearity Free and Bias Reduced Regression Estimation Project: The Theory of Normalization Ridge Regression. Report No. 2.

    Science.gov (United States)

    Bulcock, J. W.; And Others

    Multicollinearity refers to the presence of highly intercorrelated independent variables in structural equation models, that is, models estimated by using techniques such as least squares regression and maximum likelihood. There is a problem of multicollinearity in both the natural and social sciences where theory formulation and estimation is in…

  14. Measuring implicit attitudes: A positive framing bias flaw in the Implicit Relational Assessment Procedure (IRAP).

    Science.gov (United States)

    O'Shea, Brian; Watson, Derrick G; Brown, Gordon D A

    2016-02-01

    How can implicit attitudes best be measured? The Implicit Relational Assessment Procedure (IRAP), unlike the Implicit Association Test (IAT), claims to measure absolute, not just relative, implicit attitudes. In the IRAP, participants make congruent (Fat Person-Active: false; Fat Person-Unhealthy: true) or incongruent (Fat Person-Active: true; Fat Person-Unhealthy: false) responses in different blocks of trials. IRAP experiments have reported positive or neutral implicit attitudes (e.g., neutral attitudes toward fat people) in cases in which negative attitudes are normally found on explicit or other implicit measures. It was hypothesized that these results might reflect a positive framing bias (PFB) that occurs when participants complete the IRAP. Implicit attitudes toward categories with varying prior associations (nonwords, social systems, flowers and insects, thin and fat people) were measured. Three conditions (standard, positive framing, and negative framing) were used to measure whether framing influenced estimates of implicit attitudes. It was found that IRAP scores were influenced by how the task was framed to the participants, that the framing effect was modulated by the strength of prior stimulus associations, and that a default PFB led to an overestimation of positive implicit attitudes when measured by the IRAP. Overall, the findings question the validity of the IRAP as a tool for the measurement of absolute implicit attitudes. A new tool (Simple Implicit Procedure:SIP) for measuring absolute, not just relative, implicit attitudes is proposed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  15. Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting

    Directory of Open Access Journals (Sweden)

    C. Wu

    2018-03-01

    Full Text Available Linear regression techniques are widely used in atmospheric science, but they are often improperly applied due to lack of consideration or inappropriate handling of measurement uncertainty. In this work, numerical experiments are performed to evaluate the performance of five linear regression techniques, significantly extending previous works by Chu and Saylor. The five techniques are ordinary least squares (OLS, Deming regression (DR, orthogonal distance regression (ODR, weighted ODR (WODR, and York regression (YR. We first introduce a new data generation scheme that employs the Mersenne twister (MT pseudorandom number generator. The numerical simulations are also improved by (a refining the parameterization of nonlinear measurement uncertainties, (b inclusion of a linear measurement uncertainty, and (c inclusion of WODR for comparison. Results show that DR, WODR and YR produce an accurate slope, but the intercept by WODR and YR is overestimated and the degree of bias is more pronounced with a low R2 XY dataset. The importance of a properly weighting parameter λ in DR is investigated by sensitivity tests, and it is found that an improper λ in DR can lead to a bias in both the slope and intercept estimation. Because the λ calculation depends on the actual form of the measurement error, it is essential to determine the exact form of measurement error in the XY data during the measurement stage. If a priori error in one of the variables is unknown, or the measurement error described cannot be trusted, DR, WODR and YR can provide the least biases in slope and intercept among all tested regression techniques. For these reasons, DR, WODR and YR are recommended for atmospheric studies when both X and Y data have measurement errors. An Igor Pro-based program (Scatter Plot was developed to facilitate the implementation of error-in-variables regressions.

  16. Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting

    Science.gov (United States)

    Wu, Cheng; Zhen Yu, Jian

    2018-03-01

    Linear regression techniques are widely used in atmospheric science, but they are often improperly applied due to lack of consideration or inappropriate handling of measurement uncertainty. In this work, numerical experiments are performed to evaluate the performance of five linear regression techniques, significantly extending previous works by Chu and Saylor. The five techniques are ordinary least squares (OLS), Deming regression (DR), orthogonal distance regression (ODR), weighted ODR (WODR), and York regression (YR). We first introduce a new data generation scheme that employs the Mersenne twister (MT) pseudorandom number generator. The numerical simulations are also improved by (a) refining the parameterization of nonlinear measurement uncertainties, (b) inclusion of a linear measurement uncertainty, and (c) inclusion of WODR for comparison. Results show that DR, WODR and YR produce an accurate slope, but the intercept by WODR and YR is overestimated and the degree of bias is more pronounced with a low R2 XY dataset. The importance of a properly weighting parameter λ in DR is investigated by sensitivity tests, and it is found that an improper λ in DR can lead to a bias in both the slope and intercept estimation. Because the λ calculation depends on the actual form of the measurement error, it is essential to determine the exact form of measurement error in the XY data during the measurement stage. If a priori error in one of the variables is unknown, or the measurement error described cannot be trusted, DR, WODR and YR can provide the least biases in slope and intercept among all tested regression techniques. For these reasons, DR, WODR and YR are recommended for atmospheric studies when both X and Y data have measurement errors. An Igor Pro-based program (Scatter Plot) was developed to facilitate the implementation of error-in-variables regressions.

  17. Investigation of the UK37' vs. SST relationship for Atlantic Ocean suspended particulate alkenones: An alternative regression model and discussion of possible sampling bias

    Science.gov (United States)

    Gould, Jessica; Kienast, Markus; Dowd, Michael

    2017-05-01

    Alkenone unsaturation, expressed as the UK37' index, is closely related to growth temperature of prymnesiophytes, thus providing a reliable proxy to infer past sea surface temperatures (SSTs). Here we address two lingering uncertainties related to this SST proxy. First, calibration models developed for core-top sediments and those developed for surface suspended particulates organic material (SPOM) show systematic offsets, raising concerns regarding the transfer of the primary signal into the sedimentary record. Second, questions remain regarding changes in slope of the UK37' vs. growth temperature relationship at the temperature extremes. Based on (re)analysis of 31 new and 394 previously published SPOM UK37' data from the Atlantic Ocean, a new regression model to relate UK37' to SST is introduced; the Richards curve (Richards, 1959). This non-linear regression model provides a robust calibration of the UK37' vs. SST relationship for Atlantic SPOM samples and uniquely accounts for both the fact that the UK37' index is a proportion, and so must lie between 0 and 1, as well as for the observed reduction in slope at the warm and cold ends of the temperature range. As with prior fits of SPOM UK37' vs. SST, the Richards model is offset from traditional regression models of sedimentary UK37' vs. SST. We posit that (some of) this offset can be attributed to the seasonally and depth biased sampling of SPOM material.

  18. Linear regression in astronomy. I

    Science.gov (United States)

    Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh

    1990-01-01

    Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.

  19. Logic regression and its extensions.

    Science.gov (United States)

    Schwender, Holger; Ruczinski, Ingo

    2010-01-01

    Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.

  20. Correction of Selection Bias in Survey Data: Is the Statistical Cure Worse Than the Bias?

    Science.gov (United States)

    Hanley, James A

    2017-04-01

    In previous articles in the American Journal of Epidemiology (Am J Epidemiol. 2013;177(5):431-442) and American Journal of Public Health (Am J Public Health. 2013;103(10):1895-1901), Masters et al. reported age-specific hazard ratios for the contrasts in mortality rates between obesity categories. They corrected the observed hazard ratios for selection bias caused by what they postulated was the nonrepresentativeness of the participants in the National Health Interview Study that increased with age, obesity, and ill health. However, it is possible that their regression approach to remove the alleged bias has not produced, and in general cannot produce, sensible hazard ratio estimates. First, we must consider how many nonparticipants there might have been in each category of obesity and of age at entry and how much higher the mortality rates would have to be in nonparticipants than in participants in these same categories. What plausible set of numerical values would convert the ("biased") decreasing-with-age hazard ratios seen in the data into the ("unbiased") increasing-with-age ratios that they computed? Can these values be encapsulated in (and can sensible values be recovered from) one additional internal variable in a regression model? Second, one must examine the age pattern of the hazard ratios that have been adjusted for selection. Without the correction, the hazard ratios are attenuated with increasing age. With it, the hazard ratios at older ages are considerably higher, but those at younger ages are well below one. Third, one must test whether the regression approach suggested by Masters et al. would correct the nonrepresentativeness that increased with age and ill health that I introduced into real and hypothetical data sets. I found that the approach did not recover the hazard ratio patterns present in the unselected data sets: the corrections overshot the target at older ages and undershot it at lower ages.

  1. Bias against research on gender bias.

    Science.gov (United States)

    Cislak, Aleksandra; Formanowicz, Magdalena; Saguy, Tamar

    2018-01-01

    The bias against women in academia is a documented phenomenon that has had detrimental consequences, not only for women, but also for the quality of science. First, gender bias in academia affects female scientists, resulting in their underrepresentation in academic institutions, particularly in higher ranks. The second type of gender bias in science relates to some findings applying only to male participants, which produces biased knowledge. Here, we identify a third potentially powerful source of gender bias in academia: the bias against research on gender bias. In a bibliometric investigation covering a broad range of social sciences, we analyzed published articles on gender bias and race bias and established that articles on gender bias are funded less often and published in journals with a lower Impact Factor than articles on comparable instances of social discrimination. This result suggests the possibility of an underappreciation of the phenomenon of gender bias and related research within the academic community. Addressing this meta-bias is crucial for the further examination of gender inequality, which severely affects many women across the world.

  2. Methodological Issues in Cross-Cultural Counseling Research: Equivalence, Bias, and Translations

    Science.gov (United States)

    Aegisdottir, Stefania; Gerstein, Lawrence A.; Cinarbas, Deniz Canel

    2008-01-01

    Concerns about the cross-cultural validity of constructs are discussed, including equivalence, bias, and translation procedures. Methods to enhance equivalence are described, as are strategies to evaluate and minimize types of bias. Recommendations for translating instruments are also presented. To illustrate some challenges of cross-cultural…

  3. Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression.

    Science.gov (United States)

    Mones, Letif; Bernstein, Noam; Csányi, Gábor

    2016-10-11

    Practical free energy reconstruction algorithms involve three separate tasks: biasing, measuring some observable, and finally reconstructing the free energy surface from those measurements. In more than one dimension, adaptive schemes make it possible to explore only relatively low lying regions of the landscape by progressively building up the bias toward the negative of the free energy surface so that free energy barriers are eliminated. Most schemes use the final bias as their best estimate of the free energy surface. We show that large gains in computational efficiency, as measured by the reduction of time to solution, can be obtained by separating the bias used for dynamics from the final free energy reconstruction itself. We find that biasing with metadynamics, measuring a free energy gradient estimator, and reconstructing using Gaussian process regression can give an order of magnitude reduction in computational cost.

  4. Modification of cognitive biases related to posttraumatic stress: A systematic review and research agenda.

    Science.gov (United States)

    Woud, Marcella L; Verwoerd, Johan; Krans, Julie

    2017-06-01

    Cognitive models of Posttraumatic Stress Disorder (PTSD) postulate that cognitive biases in attention, interpretation, and memory represent key factors involved in the onset and maintenance of PTSD. Developments in experimental research demonstrate that it may be possible to manipulate such biases by means of Cognitive Bias Modification (CBM). In the present paper, we summarize studies assessing cognitive biases in posttraumatic stress to serve as a theoretical and methodological background. However, our main aim was to provide an overview of the scientific literature on CBM in (analogue) posttraumatic stress. Results of our systematic literature review showed that most CBM studies targeted attentional and interpretation biases (attention: five studies; interpretation: three studies), and one study modified memory biases. Overall, results showed that CBM can indeed modify cognitive biases and affect (analog) trauma symptoms in a training congruent manner. Interpretation bias procedures seemed effective in analog samples, and memory bias training proved preliminary success in a clinical PTSD sample. Studies of attention bias modification provided more mixed results. This heterogeneous picture may be explained by differences in the type of population or variations in the CBM procedure. Therefore, we sketched a detailed research agenda targeting the challenges for CBM in posttraumatic stress. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. SEPARATION PHENOMENA LOGISTIC REGRESSION

    Directory of Open Access Journals (Sweden)

    Ikaro Daniel de Carvalho Barreto

    2014-03-01

    Full Text Available This paper proposes an application of concepts about the maximum likelihood estimation of the binomial logistic regression model to the separation phenomena. It generates bias in the estimation and provides different interpretations of the estimates on the different statistical tests (Wald, Likelihood Ratio and Score and provides different estimates on the different iterative methods (Newton-Raphson and Fisher Score. It also presents an example that demonstrates the direct implications for the validation of the model and validation of variables, the implications for estimates of odds ratios and confidence intervals, generated from the Wald statistics. Furthermore, we present, briefly, the Firth correction to circumvent the phenomena of separation.

  6. Regression methods for medical research

    CERN Document Server

    Tai, Bee Choo

    2013-01-01

    Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the

  7. Optimism Bias in Fans and Sports Reporters.

    Science.gov (United States)

    Love, Bradley C; Kopeć, Łukasz; Guest, Olivia

    2015-01-01

    People are optimistic about their prospects relative to others. However, existing studies can be difficult to interpret because outcomes are not zero-sum. For example, one person avoiding cancer does not necessitate that another person develops cancer. Ideally, optimism bias would be evaluated within a closed formal system to establish with certainty the extent of the bias and the associated environmental factors, such that optimism bias is demonstrated when a population is internally inconsistent. Accordingly, we asked NFL fans to predict how many games teams they liked and disliked would win in the 2015 season. Fans, like ESPN reporters assigned to cover a team, were overly optimistic about their team's prospects. The opposite pattern was found for teams that fans disliked. Optimism may flourish because year-to-year team results are marked by auto-correlation and regression to the group mean (i.e., good teams stay good, but bad teams improve).

  8. Principles of Quantile Regression and an Application

    Science.gov (United States)

    Chen, Fang; Chalhoub-Deville, Micheline

    2014-01-01

    Newer statistical procedures are typically introduced to help address the limitations of those already in practice or to deal with emerging research needs. Quantile regression (QR) is introduced in this paper as a relatively new methodology, which is intended to overcome some of the limitations of least squares mean regression (LMR). QR is more…

  9. A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

    Science.gov (United States)

    Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

    2013-06-01

    Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric

  10. The Bland-Altman Method Should Not Be Used in Regression Cross-Validation Studies

    Science.gov (United States)

    O'Connor, Daniel P.; Mahar, Matthew T.; Laughlin, Mitzi S.; Jackson, Andrew S.

    2011-01-01

    The purpose of this study was to demonstrate the bias in the Bland-Altman (BA) limits of agreement method when it is used to validate regression models. Data from 1,158 men were used to develop three regression equations to estimate maximum oxygen uptake (R[superscript 2] = 0.40, 0.61, and 0.82, respectively). The equations were evaluated in a…

  11. Anticipated Regret and Omission Bias in HPV Vaccination Decisions

    DEFF Research Database (Denmark)

    Jensen, Niels Holm

    2017-01-01

    This study investigated effects of anticipated regret on parents’ HPV vaccination intentions and effects of omission bias on HPV vaccination intentions and vaccine uptake. An online survey was completed by 851 parents of adolescent girls in Denmark, a country where HPV vaccine safety is currently...... heavily debated. Multivariate regression analyses revealed anticipated inaction regret as a significant positive predictor of vaccination intentions, and, anticipated action regret as a significant negative predictor of vaccination intentions. Multivariate analyses also revealed omission bias...... in a hypothetical vaccination vignette as a significant negative predictor of HPV vaccination intention as well as vaccine uptake. Finally, the study tested effects of anticipated regret and omission bias on evaluations of two extisting Danish pro-vaccine campaign videos. Here, the result revealed anticipated...

  12. Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications

    Directory of Open Access Journals (Sweden)

    Guoqi Qian

    2016-01-01

    Full Text Available Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method.

  13. Logistic regression modelling: procedures and pitfalls in developing and interpreting prediction models

    Directory of Open Access Journals (Sweden)

    Nataša Šarlija

    2017-01-01

    Full Text Available This study sheds light on the most common issues related to applying logistic regression in prediction models for company growth. The purpose of the paper is 1 to provide a detailed demonstration of the steps in developing a growth prediction model based on logistic regression analysis, 2 to discuss common pitfalls and methodological errors in developing a model, and 3 to provide solutions and possible ways of overcoming these issues. Special attention is devoted to the question of satisfying logistic regression assumptions, selecting and defining dependent and independent variables, using classification tables and ROC curves, for reporting model strength, interpreting odds ratios as effect measures and evaluating performance of the prediction model. Development of a logistic regression model in this paper focuses on a prediction model of company growth. The analysis is based on predominantly financial data from a sample of 1471 small and medium-sized Croatian companies active between 2009 and 2014. The financial data is presented in the form of financial ratios divided into nine main groups depicting following areas of business: liquidity, leverage, activity, profitability, research and development, investing and export. The growth prediction model indicates aspects of a business critical for achieving high growth. In that respect, the contribution of this paper is twofold. First, methodological, in terms of pointing out pitfalls and potential solutions in logistic regression modelling, and secondly, theoretical, in terms of identifying factors responsible for high growth of small and medium-sized companies.

  14. Optimism Bias in Fans and Sports Reporters

    Science.gov (United States)

    Love, Bradley C.

    2015-01-01

    People are optimistic about their prospects relative to others. However, existing studies can be difficult to interpret because outcomes are not zero-sum. For example, one person avoiding cancer does not necessitate that another person develops cancer. Ideally, optimism bias would be evaluated within a closed formal system to establish with certainty the extent of the bias and the associated environmental factors, such that optimism bias is demonstrated when a population is internally inconsistent. Accordingly, we asked NFL fans to predict how many games teams they liked and disliked would win in the 2015 season. Fans, like ESPN reporters assigned to cover a team, were overly optimistic about their team’s prospects. The opposite pattern was found for teams that fans disliked. Optimism may flourish because year-to-year team results are marked by auto-correlation and regression to the group mean (i.e., good teams stay good, but bad teams improve). PMID:26352146

  15. Left cheek bias for emotion perception, but not expression, is established in children aged 3-7 years.

    Science.gov (United States)

    Lindell, Annukka K; Tenenbaum, Harriet R; Aznar, Ana

    2017-01-01

    As the left hemiface is controlled by the emotion-dominant right hemisphere, emotion is expressed asymmetrically. Portraits showing a model's left cheek consequently appear more emotive. Though the left cheek bias is well established in adults, it has not been investigated in children. To determine whether the left cheek biases for emotion perception and expression are present and/or develop between the ages of 3 and 7 years, 145 children (71 male, 74 female; M age = 65.49 months) completed two experimental tasks: one assessing biases in emotion perception, and the other assessing biases in emotion expression. Regression analysis confirmed that children aged 3-7 years find left cheek portraits happier than right cheek portraits, and age does not predict the magnitude of the bias. In contrast when asked to pose for a photo expressing happiness children did not show a left cheek bias, with logistic regression confirming that age did not predict posing orientations. These findings indicate that though the left cheek bias for emotion perception is established by age 3, a similar bias for emotion expression is not evident by age 7. This implies that tacit knowledge of the left cheek's greater expressivity is not innate but develops in later childhood/adolescence.

  16. Working memory biasing of visual perception without awareness.

    Science.gov (United States)

    Pan, Yi; Lin, Bingyuan; Zhao, Yajun; Soto, David

    2014-10-01

    Previous research has demonstrated that the contents of visual working memory can bias visual processing in favor of matching stimuli in the scene. However, the extent to which such top-down, memory-driven biasing of visual perception is contingent on conscious awareness remains unknown. Here we showed that conscious awareness of critical visual cues is dispensable for working memory to bias perceptual selection mechanisms. Using the procedure of continuous flash suppression, we demonstrated that "unseen" visual stimuli during interocular suppression can gain preferential access to awareness if they match the contents of visual working memory. Strikingly, the very same effect occurred even when the visual cue to be held in memory was rendered nonconscious by masking. Control experiments ruled out the alternative accounts of repetition priming and different detection criteria. We conclude that working memory biases of visual perception can operate in the absence of conscious awareness.

  17. Considerations for analysis of time-to-event outcomes measured with error: Bias and correction with SIMEX.

    Science.gov (United States)

    Oh, Eric J; Shepherd, Bryan E; Lumley, Thomas; Shaw, Pamela A

    2018-04-15

    For time-to-event outcomes, a rich literature exists on the bias introduced by covariate measurement error in regression models, such as the Cox model, and methods of analysis to address this bias. By comparison, less attention has been given to understanding the impact or addressing errors in the failure time outcome. For many diseases, the timing of an event of interest (such as progression-free survival or time to AIDS progression) can be difficult to assess or reliant on self-report and therefore prone to measurement error. For linear models, it is well known that random errors in the outcome variable do not bias regression estimates. With nonlinear models, however, even random error or misclassification can introduce bias into estimated parameters. We compare the performance of 2 common regression models, the Cox and Weibull models, in the setting of measurement error in the failure time outcome. We introduce an extension of the SIMEX method to correct for bias in hazard ratio estimates from the Cox model and discuss other analysis options to address measurement error in the response. A formula to estimate the bias induced into the hazard ratio by classical measurement error in the event time for a log-linear survival model is presented. Detailed numerical studies are presented to examine the performance of the proposed SIMEX method under varying levels and parametric forms of the error in the outcome. We further illustrate the method with observational data on HIV outcomes from the Vanderbilt Comprehensive Care Clinic. Copyright © 2017 John Wiley & Sons, Ltd.

  18. JT-60 configuration parameters for feedback control determined by regression analysis

    Energy Technology Data Exchange (ETDEWEB)

    Matsukawa, Makoto; Hosogane, Nobuyuki; Ninomiya, Hiromasa (Japan Atomic Energy Research Inst., Naka, Ibaraki (Japan). Naka Fusion Research Establishment)

    1991-12-01

    The stepwise regression procedure was applied to obtain measurement formulas for equilibrium parameters used in the feedback control of JT-60. This procedure automatically selects variables necessary for the measurements, and selects a set of variables which are not likely to be picked up by physical considerations. Regression equations with stable and small multicollinearity were obtained and it was experimentally confirmed that the measurement formulas obtained through this procedure were accurate enough to be applicable to the feedback control of plasma configurations in JT-60. (author).

  19. JT-60 configuration parameters for feedback control determined by regression analysis

    International Nuclear Information System (INIS)

    Matsukawa, Makoto; Hosogane, Nobuyuki; Ninomiya, Hiromasa

    1991-12-01

    The stepwise regression procedure was applied to obtain measurement formulas for equilibrium parameters used in the feedback control of JT-60. This procedure automatically selects variables necessary for the measurements, and selects a set of variables which are not likely to be picked up by physical considerations. Regression equations with stable and small multicollinearity were obtained and it was experimentally confirmed that the measurement formulas obtained through this procedure were accurate enough to be applicable to the feedback control of plasma configurations in JT-60. (author)

  20. Regression modeling of ground-water flow

    Science.gov (United States)

    Cooley, R.L.; Naff, R.L.

    1985-01-01

    Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

  1. Multiple linear regression analysis

    Science.gov (United States)

    Edwards, T. R.

    1980-01-01

    Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.

  2. Effect of oxygen on the bias-enhanced nucleation of diamond on silicon

    DEFF Research Database (Denmark)

    Schreck, M.; Christensen, Carsten; Stritzker, B.

    1999-01-01

    The influence of traces of oxygen in the process gas on the bias-enhanced nucleation (BEN) of diamond on silicon has been studied in the present work. CO2 in concentrations ranging from 0 to 3000 ppm was added during the nucleation procedure at U-bias = -200 V in microwave plasma chemical vapour...

  3. Meta-analytical synthesis of regression coefficients under different categorization scheme of continuous covariates.

    Science.gov (United States)

    Yoneoka, Daisuke; Henmi, Masayuki

    2017-11-30

    Recently, the number of clinical prediction models sharing the same regression task has increased in the medical literature. However, evidence synthesis methodologies that use the results of these regression models have not been sufficiently studied, particularly in meta-analysis settings where only regression coefficients are available. One of the difficulties lies in the differences between the categorization schemes of continuous covariates across different studies. In general, categorization methods using cutoff values are study specific across available models, even if they focus on the same covariates of interest. Differences in the categorization of covariates could lead to serious bias in the estimated regression coefficients and thus in subsequent syntheses. To tackle this issue, we developed synthesis methods for linear regression models with different categorization schemes of covariates. A 2-step approach to aggregate the regression coefficient estimates is proposed. The first step is to estimate the joint distribution of covariates by introducing a latent sampling distribution, which uses one set of individual participant data to estimate the marginal distribution of covariates with categorization. The second step is to use a nonlinear mixed-effects model with correction terms for the bias due to categorization to estimate the overall regression coefficients. Especially in terms of precision, numerical simulations show that our approach outperforms conventional methods, which only use studies with common covariates or ignore the differences between categorization schemes. The method developed in this study is also applied to a series of WHO epidemiologic studies on white blood cell counts. Copyright © 2017 John Wiley & Sons, Ltd.

  4. Statistical methods for accurately determining criticality code bias

    International Nuclear Information System (INIS)

    Trumble, E.F.; Kimball, K.D.

    1997-01-01

    A system of statistically treating validation calculations for the purpose of determining computer code bias is provided in this paper. The following statistical treatments are described: weighted regression analysis, lower tolerance limit, lower tolerance band, and lower confidence band. These methods meet the criticality code validation requirements of ANS 8.1. 8 refs., 5 figs., 4 tabs

  5. Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: insights into spatial variability using high-resolution satellite data.

    Science.gov (United States)

    Alexeeff, Stacey E; Schwartz, Joel; Kloog, Itai; Chudnovsky, Alexandra; Koutrakis, Petros; Coull, Brent A

    2015-01-01

    Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1 km × 1 km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R(2) yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with >0.9 out-of-sample R(2) yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the SEs. Land use regression models performed better in chronic effect simulations. These results can help researchers when interpreting health effect estimates in these types of studies.

  6. Performance of a New Restricted Biased Estimator in Logistic Regression

    Directory of Open Access Journals (Sweden)

    Yasin ASAR

    2017-12-01

    Full Text Available It is known that the variance of the maximum likelihood estimator (MLE inflates when the explanatory variables are correlated. This situation is called the multicollinearity problem. As a result, the estimations of the model may not be trustful. Therefore, this paper introduces a new restricted estimator (RLTE that may be applied to get rid of the multicollinearity when the parameters lie in some linear subspace  in logistic regression. The mean squared errors (MSE and the matrix mean squared errors (MMSE of the estimators considered in this paper are given. A Monte Carlo experiment is designed to evaluate the performances of the proposed estimator, the restricted MLE (RMLE, MLE and Liu-type estimator (LTE. The criterion of performance is chosen to be MSE. Moreover, a real data example is presented. According to the results, proposed estimator has better performance than MLE, RMLE and LTE.

  7. Operator Bias in the Estimation of Arc Efficiency in Gas Tungsten Arc Welding

    Directory of Open Access Journals (Sweden)

    Fredrik Sikström

    2015-03-01

    Full Text Available In this paper the operator bias in the measurement process of arc efficiency in stationary direct current electrode negative gas tungsten arc welding is discussed. An experimental study involving 15 operators (enough to reach statistical significance has been carried out with the purpose to estimate the arc efficiency from a specific procedure for calorimetric experiments. The measurement procedure consists of three manual operations which introduces operator bias in the measurement process. An additional relevant experiment highlights the consequences of estimating the arc voltage by measuring the potential between the terminals of the welding power source instead of measuring the potential between the electrode contact tube and the workpiece. The result of the study is a statistical evaluation of the operator bias influence on the estimate, showing that operator bias is negligible in the estimate considered here. On the contrary the consequences of neglecting welding leads voltage drop results in a significant under estimation of the arc efficiency.

  8. Tutorial on Using Regression Models with Count Outcomes Using R

    Directory of Open Access Journals (Sweden)

    A. Alexander Beaujean

    2016-02-01

    Full Text Available Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix.

  9. Investigating vulnerability to eating disorders: biases in emotional processing.

    Science.gov (United States)

    Pringle, A; Harmer, C J; Cooper, M J

    2010-04-01

    Biases in emotional processing and cognitions about the self are thought to play a role in the maintenance of eating disorders (EDs). However, little is known about whether these difficulties exist pre-morbidly and how they might contribute to risk. Female dieters (n=82) completed a battery of tasks designed to assess the processing of social cues (facial emotion recognition), cognitions about the self [Self-Schema Processing Task (SSPT)] and ED-specific cognitions about eating, weight and shape (emotional Stroop). The 26-item Eating Attitudes Test (EAT-26; Garner et al. 1982) was used to assess subclinical ED symptoms; this was used as an index of vulnerability within this at-risk group. Regression analyses showed that biases in the processing of both neutral and angry faces were predictive of our measure of vulnerability (EAT-26). In the self-schema task, biases in the processing of negative self descriptors previously found to be common in EDs predicted vulnerability. Biases in the processing of shape-related words on the Stroop task were also predictive; however, these biases were more important in dieters who also displayed biases in the self-schema task. We were also able to demonstrate that these biases are specific and separable from more general negative biases that could be attributed to depressive symptoms. These results suggest that specific biases in the processing of social cues, cognitions about the self, and also about eating, weight and shape information, may be important in understanding risk and preventing relapse in EDs.

  10. Testing for marginal linear effects in quantile regression

    KAUST Repository

    Wang, Huixia Judy

    2017-10-23

    The paper develops a new marginal testing procedure to detect significant predictors that are associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then to base the test on the t-statistics that are associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behaviour due to the selection of the most predictive variables. Asymptotic validity of the procedure is established in a general quantile regression setting in which the marginal quantile regression models can be misspecified. Even though a fixed dimension is assumed to derive the asymptotic results, the test proposed is applicable and computationally feasible for large dimensional predictors. The method is more flexible than existing marginal screening test methods based on mean regression and has the added advantage of being robust against outliers in the response. The approach is illustrated by using an application to a human immunodeficiency virus drug resistance data set.

  11. Testing for marginal linear effects in quantile regression

    KAUST Repository

    Wang, Huixia Judy; McKeague, Ian W.; Qian, Min

    2017-01-01

    The paper develops a new marginal testing procedure to detect significant predictors that are associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then to base the test on the t-statistics that are associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behaviour due to the selection of the most predictive variables. Asymptotic validity of the procedure is established in a general quantile regression setting in which the marginal quantile regression models can be misspecified. Even though a fixed dimension is assumed to derive the asymptotic results, the test proposed is applicable and computationally feasible for large dimensional predictors. The method is more flexible than existing marginal screening test methods based on mean regression and has the added advantage of being robust against outliers in the response. The approach is illustrated by using an application to a human immunodeficiency virus drug resistance data set.

  12. Practical estimate of gradient nonlinearity for implementation of apparent diffusion coefficient bias correction.

    Science.gov (United States)

    Malkyarenko, Dariya I; Chenevert, Thomas L

    2014-12-01

    To describe an efficient procedure to empirically characterize gradient nonlinearity and correct for the corresponding apparent diffusion coefficient (ADC) bias on a clinical magnetic resonance imaging (MRI) scanner. Spatial nonlinearity scalars for individual gradient coils along superior and right directions were estimated via diffusion measurements of an isotropicic e-water phantom. Digital nonlinearity model from an independent scanner, described in the literature, was rescaled by system-specific scalars to approximate 3D bias correction maps. Correction efficacy was assessed by comparison to unbiased ADC values measured at isocenter. Empirically estimated nonlinearity scalars were confirmed by geometric distortion measurements of a regular grid phantom. The applied nonlinearity correction for arbitrarily oriented diffusion gradients reduced ADC bias from 20% down to 2% at clinically relevant offsets both for isotropic and anisotropic media. Identical performance was achieved using either corrected diffusion-weighted imaging (DWI) intensities or corrected b-values for each direction in brain and ice-water. Direction-average trace image correction was adequate only for isotropic medium. Empiric scalar adjustment of an independent gradient nonlinearity model adequately described DWI bias for a clinical scanner. Observed efficiency of implemented ADC bias correction quantitatively agreed with previous theoretical predictions and numerical simulations. The described procedure provides an independent benchmark for nonlinearity bias correction of clinical MRI scanners.

  13. Quasi-experimental study designs series-paper 6: risk of bias assessment.

    Science.gov (United States)

    Waddington, Hugh; Aloe, Ariel M; Becker, Betsy Jane; Djimeu, Eric W; Hombrados, Jorge Garcia; Tugwell, Peter; Wells, George; Reeves, Barney

    2017-09-01

    Rigorous and transparent bias assessment is a core component of high-quality systematic reviews. We assess modifications to existing risk of bias approaches to incorporate rigorous quasi-experimental approaches with selection on unobservables. These are nonrandomized studies using design-based approaches to control for unobservable sources of confounding such as difference studies, instrumental variables, interrupted time series, natural experiments, and regression-discontinuity designs. We review existing risk of bias tools. Drawing on these tools, we present domains of bias and suggest directions for evaluation questions. The review suggests that existing risk of bias tools provide, to different degrees, incomplete transparent criteria to assess the validity of these designs. The paper then presents an approach to evaluating the internal validity of quasi-experiments with selection on unobservables. We conclude that tools for nonrandomized studies of interventions need to be further developed to incorporate evaluation questions for quasi-experiments with selection on unobservables. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. Variable Selection for Regression Models of Percentile Flows

    Science.gov (United States)

    Fouad, G.

    2017-12-01

    Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high

  15. Generalized allometric regression to estimate biomass of Populus in short-rotation coppice

    Energy Technology Data Exchange (ETDEWEB)

    Ben Brahim, Mohammed; Gavaland, Andre; Cabanettes, Alain [INRA Centre de Toulouse, Castanet-Tolosane Cedex (France). Unite Agroforesterie et Foret Paysanne

    2000-07-01

    Data from four different stands were combined to establish a single generalized allometric equation to estimate above-ground biomass of individual Populus trees grown on short-rotation coppice. The generalized model was performed using diameter at breast height, the mean diameter and the mean height of each site as dependent variables and then compared with the stand-specific regressions using F-test. Results showed that this single regression estimates tree biomass well at each stand and does not introduce bias with increasing diameter.

  16. The relationship between attentional bias toward safety and driving behavior.

    Science.gov (United States)

    Zheng, Tingting; Qu, Weina; Zhang, Kan; Ge, Yan

    2016-11-01

    As implicit cognitive processes garner more and more importance, studies in the fields of healthy psychology and organizational safety research have focused on attentional bias, a kind of selective allocation of attentional resources in the early stage of cognitive processing. However, few studies have explored the role of attentional bias on driving behavior. This study assessed drivers' attentional bias towards safety-related words (ABS) using the dot-probe paradigm and self-reported daily driving behaviors. The results revealed significant negative correlations between attentional bias scores and several indicators of dangerous driving. Drivers with fewer dangerous driving behaviors showed greater ABS. We also built a significant linear regression model between ABS and the total DDDI score, as well as ABS and the number of accidents. Finally, we discussed the possible mechanism underlying these associations and several limitations of our study. This study opens up a new topic for the exploration of implicit processes in driving safety research. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. Fuzzy multiple linear regression: A computational approach

    Science.gov (United States)

    Juang, C. H.; Huang, X. H.; Fleming, J. W.

    1992-01-01

    This paper presents a new computational approach for performing fuzzy regression. In contrast to Bardossy's approach, the new approach, while dealing with fuzzy variables, closely follows the conventional regression technique. In this approach, treatment of fuzzy input is more 'computational' than 'symbolic.' The following sections first outline the formulation of the new approach, then deal with the implementation and computational scheme, and this is followed by examples to illustrate the new procedure.

  18. Propensity Score Estimation with Data Mining Techniques: Alternatives to Logistic Regression

    Science.gov (United States)

    Keller, Bryan S. B.; Kim, Jee-Seon; Steiner, Peter M.

    2013-01-01

    Propensity score analysis (PSA) is a methodological technique which may correct for selection bias in a quasi-experiment by modeling the selection process using observed covariates. Because logistic regression is well understood by researchers in a variety of fields and easy to implement in a number of popular software packages, it has…

  19. A forecasting method to reduce estimation bias in self-reported cell phone data.

    Science.gov (United States)

    Redmayne, Mary; Smith, Euan; Abramson, Michael J

    2013-01-01

    There is ongoing concern that extended exposure to cell phone electromagnetic radiation could be related to an increased risk of negative health effects. Epidemiological studies seek to assess this risk, usually relying on participants' recalled use, but recall is notoriously poor. Our objectives were primarily to produce a forecast method, for use by such studies, to reduce estimation bias in the recalled extent of cell phone use. The method we developed, using Bayes' rule, is modelled with data we collected in a cross-sectional cluster survey exploring cell phone user-habits among New Zealand adolescents. Participants recalled their recent extent of SMS-texting and retrieved from their provider the current month's actual use-to-date. Actual use was taken as the gold standard in the analyses. Estimation bias arose from a large random error, as observed in all cell phone validation studies. We demonstrate that this seriously exaggerates upper-end forecasts of use when used in regression models. This means that calculations using a regression model will lead to underestimation of heavy-users' relative risk. Our Bayesian method substantially reduces estimation bias. In cases where other studies' data conforms to our method's requirements, application should reduce estimation bias, leading to a more accurate relative risk calculation for mid-to-heavy users.

  20. Model selection with multiple regression on distance matrices leads to incorrect inferences.

    Directory of Open Access Journals (Sweden)

    Ryan P Franckowiak

    Full Text Available In landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike's information criterion (AIC, its small-sample correction (AICc, and the Bayesian information criterion (BIC to reliably rank candidate models when applied with MRM while varying the sample size. The results showed a serious problem: all three criteria exhibit a systematic bias toward selecting unnecessarily complex models containing spurious random variables and erroneously suggest a high level of support for the incorrectly ranked best model. These problems effectively increased with increasing sample size. The failure of AIC, AICc, and BIC was likely driven by the inflated sample size and different sum-of-squares partitioned by MRM, and the resulting effect on delta values. Based on these findings, we strongly discourage the continued application of AIC, AICc, and BIC for model selection with MRM.

  1. The effect of metacognitive self on confirmation bias revealed in relation to community and competence

    Directory of Open Access Journals (Sweden)

    Brycz Hanna

    2014-09-01

    Full Text Available The main goal of our study was to investigate the role of insight into one’s own biases (metacognitive self in the process of hypothesis validation in accordance to the two fundamental social perception domains (community and competence on the example of confirmation bias. The study was conducted on a group of 593 participants with the use of a confirmation bias procedure, a free recall procedure and the Metacognitive Self scale. We manipulated with the domain and the value of information given to the respondents. We suspected that individuals with a high metacognitive self, in opposition to low metacognitive self ones, would not process the given information according to the two fundamental social perception domains. The results verified the existence of an interaction effect of the metacognitive self (MCS and the domain of the information given about a perceived person on the susceptibility to follow the confirmation bias. Contrary to the low metacognitive self individuals, who show a higher tendency for the confirmation bias within the competence than the community domain, persons with a high insight into their own biases express the same level of confirmation bias in no respect to the domain of the information. The value of the information has no significant influence.

  2. A method of bias correction for maximal reliability with dichotomous measures.

    Science.gov (United States)

    Penev, Spiridon; Raykov, Tenko

    2010-02-01

    This paper is concerned with the reliability of weighted combinations of a given set of dichotomous measures. Maximal reliability for such measures has been discussed in the past, but the pertinent estimator exhibits a considerable bias and mean squared error for moderate sample sizes. We examine this bias, propose a procedure for bias correction, and develop a more accurate asymptotic confidence interval for the resulting estimator. In most empirically relevant cases, the bias correction and mean squared error correction can be performed simultaneously. We propose an approximate (asymptotic) confidence interval for the maximal reliability coefficient, discuss the implementation of this estimator, and investigate the mean squared error of the associated asymptotic approximation. We illustrate the proposed methods using a numerical example.

  3. Estimating the price elasticity of beer: meta-analysis of data with heterogeneity, dependence, and publication bias.

    Science.gov (United States)

    Nelson, Jon P

    2014-01-01

    Precise estimates of price elasticities are important for alcohol tax policy. Using meta-analysis, this paper corrects average beer elasticities for heterogeneity, dependence, and publication selection bias. A sample of 191 estimates is obtained from 114 primary studies. Simple and weighted means are reported. Dependence is addressed by restricting number of estimates per study, author-restricted samples, and author-specific variables. Publication bias is addressed using funnel graph, trim-and-fill, and Egger's intercept model. Heterogeneity and selection bias are examined jointly in meta-regressions containing moderator variables for econometric methodology, primary data, and precision of estimates. Results for fixed- and random-effects regressions are reported. Country-specific effects and sample time periods are unimportant, but several methodology variables help explain the dispersion of estimates. In models that correct for selection bias and heterogeneity, the average beer price elasticity is about -0.20, which is less elastic by 50% compared to values commonly used in alcohol tax policy simulations. Copyright © 2013 Elsevier B.V. All rights reserved.

  4. Strengthening forensic DNA decision making through a better understanding of the influence of cognitive bias.

    Science.gov (United States)

    Jeanguenat, Amy M; Budowle, Bruce; Dror, Itiel E

    2017-11-01

    Cognitive bias may influence process flows and decision making steps in forensic DNA analyses and interpretation. Currently, seven sources of bias have been identified that may affect forensic decision making with roots in human nature; environment, culture, and experience; and case specific information. Most of the literature and research on cognitive bias in forensic science has focused on patterned evidence; however, forensic DNA testing is not immune to bias, especially when subjective interpretation is involved. DNA testing can be strengthened by recognizing the existence of bias, evaluating where it influences decision making, and, when applicable, implementing practices to reduce or control its effects. Elements that may improve forensic decision making regarding bias include cognitively informed education and training, quality assurance procedures, review processes, analysis and interpretation, and context management of irrelevant information. Although bias exists, reliable results often can be (and have been) produced. However, at times bias can (and has) impacted the interpretation of DNA results negatively. Therefore, being aware of the dangers of bias and implementing measures to control its potential impact should be considered. Measures and procedures that handicap the workings of the crime laboratory or add little value to improving the operation are not advocated, but simple yet effective measures are suggested. This article is meant to raise awareness of cognitive bias contamination in forensic DNA testing and to give laboratories possible pathways to make sound decisions to address its influences. Copyright © 2017 The Chartered Society of Forensic Sciences. Published by Elsevier B.V. All rights reserved.

  5. Social desirability bias in dietary self-report may compromise the validity of dietary intake measures.

    Science.gov (United States)

    Hebert, J R; Clemow, L; Pbert, L; Ockene, I S; Ockene, J K

    1995-04-01

    Self-report of dietary intake could be biased by social desirability or social approval thus affecting risk estimates in epidemiological studies. These constructs produce response set biases, which are evident when testing in domains characterized by easily recognizable correct or desirable responses. Given the social and psychological value ascribed to diet, assessment methodologies used most commonly in epidemiological studies are particularly vulnerable to these biases. Social desirability and social approval biases were tested by comparing nutrient scores derived from multiple 24-hour diet recalls (24HR) on seven randomly assigned days with those from two 7-day diet recalls (7DDR) (similar in some respects to commonly used food frequency questionnaires), one administered at the beginning of the test period (pre) and one at the end (post). Statistical analysis included correlation and multiple linear regression. Cross-sectionally, no relationships between social approval score and the nutritional variables existed. Social desirability score was negatively correlated with most nutritional variables. In linear regression analysis, social desirability score produced a large downward bias in nutrient estimation in the 7DDR relative to the 24HR. For total energy, this bias equalled about 50 kcal/point on the social desirability scale or about 450 kcal over its interquartile range. The bias was approximately twice as large for women as for men and only about half as large in the post measures. Individuals having the highest 24HR-derived fat and total energy intake scores had the largest downward bias due to social desirability. We observed a large downward bias in reporting food intake related to social desirability score. These results are consistent with the theoretical constructs on which the hypothesis is based. The effect of social desirability bias is discussed in terms of its influence on epidemiological estimates of effect. Suggestions are made for future work

  6. A new method for mapping perceptual biases across visual space.

    Science.gov (United States)

    Finlayson, Nonie J; Papageorgiou, Andriani; Schwarzkopf, D Samuel

    2017-08-01

    How we perceive the environment is not stable and seamless. Recent studies found that how a person qualitatively experiences even simple visual stimuli varies dramatically across different locations in the visual field. Here we use a method we developed recently that we call multiple alternatives perceptual search (MAPS) for efficiently mapping such perceptual biases across several locations. This procedure reliably quantifies the spatial pattern of perceptual biases and also of uncertainty and choice. We show that these measurements are strongly correlated with those from traditional psychophysical methods and that exogenous attention can skew biases without affecting overall task performance. Taken together, MAPS is an efficient method to measure how an individual's perceptual experience varies across space.

  7. Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data.

    Science.gov (United States)

    Ying, Gui-Shuang; Maguire, Maureen G; Glynn, Robert; Rosner, Bernard

    2017-04-01

    To describe and demonstrate appropriate linear regression methods for analyzing correlated continuous eye data. We describe several approaches to regression analysis involving both eyes, including mixed effects and marginal models under various covariance structures to account for inter-eye correlation. We demonstrate, with SAS statistical software, applications in a study comparing baseline refractive error between one eye with choroidal neovascularization (CNV) and the unaffected fellow eye, and in a study determining factors associated with visual field in the elderly. When refractive error from both eyes were analyzed with standard linear regression without accounting for inter-eye correlation (adjusting for demographic and ocular covariates), the difference between eyes with CNV and fellow eyes was 0.15 diopters (D; 95% confidence interval, CI -0.03 to 0.32D, p = 0.10). Using a mixed effects model or a marginal model, the estimated difference was the same but with narrower 95% CI (0.01 to 0.28D, p = 0.03). Standard regression for visual field data from both eyes provided biased estimates of standard error (generally underestimated) and smaller p-values, while analysis of the worse eye provided larger p-values than mixed effects models and marginal models. In research involving both eyes, ignoring inter-eye correlation can lead to invalid inferences. Analysis using only right or left eyes is valid, but decreases power. Worse-eye analysis can provide less power and biased estimates of effect. Mixed effects or marginal models using the eye as the unit of analysis should be used to appropriately account for inter-eye correlation and maximize power and precision.

  8. A constrained polynomial regression procedure for estimating the local False Discovery Rate

    Directory of Open Access Journals (Sweden)

    Broët Philippe

    2007-06-01

    Full Text Available Abstract Background In the context of genomic association studies, for which a large number of statistical tests are performed simultaneously, the local False Discovery Rate (lFDR, which quantifies the evidence of a specific gene association with a clinical or biological variable of interest, is a relevant criterion for taking into account the multiple testing problem. The lFDR not only allows an inference to be made for each gene through its specific value, but also an estimate of Benjamini-Hochberg's False Discovery Rate (FDR for subsets of genes. Results In the framework of estimating procedures without any distributional assumption under the alternative hypothesis, a new and efficient procedure for estimating the lFDR is described. The results of a simulation study indicated good performances for the proposed estimator in comparison to four published ones. The five different procedures were applied to real datasets. Conclusion A novel and efficient procedure for estimating lFDR was developed and evaluated.

  9. Analysis and correction of gradient nonlinearity bias in apparent diffusion coefficient measurements.

    Science.gov (United States)

    Malyarenko, Dariya I; Ross, Brian D; Chenevert, Thomas L

    2014-03-01

    Gradient nonlinearity of MRI systems leads to spatially dependent b-values and consequently high non-uniformity errors (10-20%) in apparent diffusion coefficient (ADC) measurements over clinically relevant field-of-views. This work seeks practical correction procedure that effectively reduces observed ADC bias for media of arbitrary anisotropy in the fewest measurements. All-inclusive bias analysis considers spatial and time-domain cross-terms for diffusion and imaging gradients. The proposed correction is based on rotation of the gradient nonlinearity tensor into the diffusion gradient frame where spatial bias of b-matrix can be approximated by its Euclidean norm. Correction efficiency of the proposed procedure is numerically evaluated for a range of model diffusion tensor anisotropies and orientations. Spatial dependence of nonlinearity correction terms accounts for the bulk (75-95%) of ADC bias for FA = 0.3-0.9. Residual ADC non-uniformity errors are amplified for anisotropic diffusion. This approximation obviates need for full diffusion tensor measurement and diagonalization to derive a corrected ADC. Practical scenarios are outlined for implementation of the correction on clinical MRI systems. The proposed simplified correction algorithm appears sufficient to control ADC non-uniformity errors in clinical studies using three orthogonal diffusion measurements. The most efficient reduction of ADC bias for anisotropic medium is achieved with non-lab-based diffusion gradients. Copyright © 2013 Wiley Periodicals, Inc.

  10. Normalization Approaches for Removing Systematic Biases Associated with Mass Spectrometry and Label-Free Proteomics

    Energy Technology Data Exchange (ETDEWEB)

    Callister, Stephen J.; Barry, Richard C.; Adkins, Joshua N.; Johnson, Ethan T.; Qian, Weijun; Webb-Robertson, Bobbie-Jo M.; Smith, Richard D.; Lipton, Mary S.

    2006-02-01

    Central tendency, linear regression, locally weighted regression, and quantile techniques were investigated for normalization of peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR MS). Arbitrary abundances of peptides were obtained from three sample sets, including a standard protein sample, two Deinococcus radiodurans samples taken from different growth phases, and two mouse striatum samples from control and methamphetamine-stressed mice (strain C57BL/6). The selected normalization techniques were evaluated in both the absence and presence of biological variability by estimating extraneous variability prior to and following normalization. Prior to normalization, replicate runs from each sample set were observed to be statistically different, while following normalization replicate runs were no longer statistically different. Although all techniques reduced systematic bias, assigned ranks among the techniques revealed significant trends. For most LC-FTICR MS analyses, linear regression normalization ranked either first or second among the four techniques, suggesting that this technique was more generally suitable for reducing systematic biases.

  11. Method for nonlinear exponential regression analysis

    Science.gov (United States)

    Junkin, B. G.

    1972-01-01

    Two computer programs developed according to two general types of exponential models for conducting nonlinear exponential regression analysis are described. Least squares procedure is used in which the nonlinear problem is linearized by expanding in a Taylor series. Program is written in FORTRAN 5 for the Univac 1108 computer.

  12. Sources of method bias in social science research and recommendations on how to control it.

    Science.gov (United States)

    Podsakoff, Philip M; MacKenzie, Scott B; Podsakoff, Nathan P

    2012-01-01

    Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms "method" and "method bias" and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.

  13. Size Reduction of a DC Link Choke Using Saturation Gap and Biasing with Permanent Magnets

    DEFF Research Database (Denmark)

    Aguilar, Andres Revilla; Munk-Nielsen, Stig; Zuccherato, Marco

    2014-01-01

    This document describes the design procedure of permanent magnet biased DC inductors using the Saturation-gap technique [1]. This biasing configuration can provide a 50% reduction in either the core volume or the number of turns, while meeting its current and inductance requirements. A design exa...

  14. Room temperature exchange bias in SmFeO_3 single crystal

    International Nuclear Information System (INIS)

    Wang, Xiaoxiong; Cheng, Xiangyi; Gao, Shang; Song, Junda; Ruan, Keqing; Li, Xiaoguang

    2016-01-01

    Exchange bias phenomenon is generally ascribed to the unidirectional magnetic shift along the field axes at interface of two magnetic materials. Room temperature exchange bias is found in SmFeO_3 single crystal. The behavior after different cooling procedure is regular, and the training behavior is attributed to the athermal training and its pinning origin is attributed to the antiferromagnetic clusters. Its being single phase and occurring at room temperature make it an appropriate candidate for application. - Graphical abstract: Room temperature exchange bias was found in oxide single crystal. Highlights: • Room temperature exchange bias has been discovered in single-crystalline SmFeO_3. • Its pinning origin is attributed to the antiferromagnetic clusters. • Its being single phase and occurring at room temperature make it an appropriate candidate for application.

  15. The role of experience on techno-entrepreneurs’ decision making biases

    Directory of Open Access Journals (Sweden)

    Pouria Nouri

    2012-10-01

    Full Text Available Entrepreneurs are the driving force behind the prospect and growth of the societies. Sound and wise decisions pave the way for them to carry out these highly important functions. Entrepreneurs are to discover and exploit opportunities. Therefore, they must gather sufficient and pertinent information. Entrepreneurs, like most human beings face complex and ambiguous decision-making situations, not to mention their lack of time and source to gather and process the data. Under these circumstances, entrepreneurs are prone making biases decisions. There are many reasons identified for this entrepreneurial decision making biases, such as the high cost of rational decision making, limitations in information processing, differences in their styles and procedures, or information overload, environmental complexity, environmental uncertainty. These biases are neither totally harmful nor completely useful and have to be seen as natural human characteristics. What makes entrepreneurial decision-making biases important is their effects on the decisions and thus the outcome of the enterprises. Entrepreneurial decision-making biases, deliberate or unintentional can seal the fate of the enterprises, therefore studying them meticulously is crucial. Literature has shown that experience could be an effective factor in decision-making biases. In this paper, we try to find out the impact of experience in Iranian high tech entrepreneurs’ major decision-making biases by a qualitative approach. Finally, it was concluded that experience is influential in shaping overconfidence bias.

  16. An importance biasing for 1-D deep-penetration problem by Monte Carlo

    International Nuclear Information System (INIS)

    Gupta, H.C.; Dwivedi, S.R.

    1988-01-01

    Using the itegral equations for the first and second moments of the 'total score' in an analogue and non-analogue simulations zero-variance biasing schemes have been obtained for all the commonly used reaction rate estimators. For partial score estimators a new zero-variance biasing scheme has been obtained as a special case. The new zero-variance scheme developed for partial score estimators has been used to develop an importance biasing scheme for use with expectation estimator in one dimensional deep-penetration problems with isotropic scattering. The importance biasing scheme has been studied for variance reduction in shields with anisotropic scattering. It is observed that the scheme not only results into a significant reduction in variance over the exponential biasing but also simplifies the complicated sampling procedure for the particle's outgoing direction at collision point. (author). 27 tables, 79 refs

  17. On the calibration process of film dosimetry: OLS inverse regression versus WLS inverse prediction

    International Nuclear Information System (INIS)

    Crop, F; Thierens, H; Rompaye, B Van; Paelinck, L; Vakaet, L; Wagter, C De

    2008-01-01

    The purpose of this study was both putting forward a statistically correct model for film calibration and the optimization of this process. A reliable calibration is needed in order to perform accurate reference dosimetry with radiographic (Gafchromic) film. Sometimes, an ordinary least squares simple linear (in the parameters) regression is applied to the dose-optical-density (OD) curve with the dose as a function of OD (inverse regression) or sometimes OD as a function of dose (inverse prediction). The application of a simple linear regression fit is an invalid method because heteroscedasticity of the data is not taken into account. This could lead to erroneous results originating from the calibration process itself and thus to a lower accuracy. In this work, we compare the ordinary least squares (OLS) inverse regression method with the correct weighted least squares (WLS) inverse prediction method to create calibration curves. We found that the OLS inverse regression method could lead to a prediction bias of up to 7.3 cGy at 300 cGy and total prediction errors of 3% or more for Gafchromic EBT film. Application of the WLS inverse prediction method resulted in a maximum prediction bias of 1.4 cGy and total prediction errors below 2% in a 0-400 cGy range. We developed a Monte-Carlo-based process to optimize calibrations, depending on the needs of the experiment. This type of thorough analysis can lead to a higher accuracy for film dosimetry

  18. Synthesis of linear regression coefficients by recovering the within-study covariance matrix from summary statistics.

    Science.gov (United States)

    Yoneoka, Daisuke; Henmi, Masayuki

    2017-06-01

    Recently, the number of regression models has dramatically increased in several academic fields. However, within the context of meta-analysis, synthesis methods for such models have not been developed in a commensurate trend. One of the difficulties hindering the development is the disparity in sets of covariates among literature models. If the sets of covariates differ across models, interpretation of coefficients will differ, thereby making it difficult to synthesize them. Moreover, previous synthesis methods for regression models, such as multivariate meta-analysis, often have problems because covariance matrix of coefficients (i.e. within-study correlations) or individual patient data are not necessarily available. This study, therefore, proposes a brief explanation regarding a method to synthesize linear regression models under different covariate sets by using a generalized least squares method involving bias correction terms. Especially, we also propose an approach to recover (at most) threecorrelations of covariates, which is required for the calculation of the bias term without individual patient data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  19. Two-Sample Tests for High-Dimensional Linear Regression with an Application to Detecting Interactions.

    Science.gov (United States)

    Xia, Yin; Cai, Tianxi; Cai, T Tony

    2018-01-01

    Motivated by applications in genomics, we consider in this paper global and multiple testing for the comparisons of two high-dimensional linear regression models. A procedure for testing the equality of the two regression vectors globally is proposed and shown to be particularly powerful against sparse alternatives. We then introduce a multiple testing procedure for identifying unequal coordinates while controlling the false discovery rate and false discovery proportion. Theoretical justifications are provided to guarantee the validity of the proposed tests and optimality results are established under sparsity assumptions on the regression coefficients. The proposed testing procedures are easy to implement. Numerical properties of the procedures are investigated through simulation and data analysis. The results show that the proposed tests maintain the desired error rates under the null and have good power under the alternative at moderate sample sizes. The procedures are applied to the Framingham Offspring study to investigate the interactions between smoking and cardiovascular related genetic mutations important for an inflammation marker.

  20. Automated-biasing approach to Monte Carlo shipping-cask calculations

    International Nuclear Information System (INIS)

    Hoffman, T.J.; Tang, J.S.; Parks, C.V.; Childs, R.L.

    1982-01-01

    Computer Sciences at Oak Ridge National Laboratory, under a contract with the Nuclear Regulatory Commission, has developed the SCALE system for performing standardized criticality, shielding, and heat transfer analyses of nuclear systems. During the early phase of shielding development in SCALE, it was established that Monte Carlo calculations of radiation levels exterior to a spent fuel shipping cask would be extremely expensive. This cost can be substantially reduced by proper biasing of the Monte Carlo histories. The purpose of this study is to develop and test an automated biasing procedure for the MORSE-SGC/S module of the SCALE system

  1. Automated importance generation and biasing techniques for Monte Carlo shielding techniques by the TRIPOLI-3 code

    International Nuclear Information System (INIS)

    Both, J.P.; Nimal, J.C.; Vergnaud, T.

    1990-01-01

    We discuss an automated biasing procedure for generating the parameters necessary to achieve efficient Monte Carlo biasing shielding calculations. The biasing techniques considered here are exponential transform and collision biasing deriving from the concept of the biased game based on the importance function. We use a simple model of the importance function with exponential attenuation as the distance to the detector increases. This importance function is generated on a three-dimensional mesh including geometry and with graph theory algorithms. This scheme is currently being implemented in the third version of the neutron and gamma ray transport code TRIPOLI-3. (author)

  2. A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

    Science.gov (United States)

    Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

    2016-04-01

    Existing evidence suggests that ambient ultrafine particles (UFPs) (regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

  3. A Hybrid Framework to Bias Correct and Empirically Downscale Daily Temperature and Precipitation from Regional Climate Models

    Science.gov (United States)

    Tan, P.; Abraham, Z.; Winkler, J. A.; Perdinan, P.; Zhong, S. S.; Liszewska, M.

    2013-12-01

    Bias correction and statistical downscaling are widely used approaches for postprocessing climate simulations generated by global and/or regional climate models. The skills of these approaches are typically assessed in terms of their ability to reproduce historical climate conditions as well as the plausibility and consistency of the derived statistical indicators needed by end users. Current bias correction and downscaling approaches often do not adequately satisfy the two criteria of accurate prediction and unbiased estimation. To overcome this limitation, a hybrid regression framework was developed to both minimize prediction errors and preserve the distributional characteristics of climate observations. Specifically, the framework couples the loss functions of standard (linear or nonlinear) regression methods with a regularization term that penalizes for discrepancies between the predicted and observed distributions. The proposed framework can also be extended to generate physically-consistent outputs across multiple response variables, and to incorporate both reanalysis-driven and GCM-driven RCM outputs into a unified learning framework. The effectiveness of the framework is demonstrated using daily temperature and precipitation simulations from the North American Regional Climate Change Program (NARCCAP) . The accuracy of the framework is comparable to standard regression methods, but, unlike the standard regression methods, the proposed framework is able to preserve many of the distribution properties of the response variables, akin to bias correction approaches such as quantile mapping and bivariate geometric quantile mapping.

  4. A Methodology for Generating Placement Rules that Utilizes Logistic Regression

    Science.gov (United States)

    Wurtz, Keith

    2008-01-01

    The purpose of this article is to provide the necessary tools for institutional researchers to conduct a logistic regression analysis and interpret the results. Aspects of the logistic regression procedure that are necessary to evaluate models are presented and discussed with an emphasis on cutoff values and choosing the appropriate number of…

  5. Analyzing Right-Censored Length-Biased Data with Additive Hazards Model

    Institute of Scientific and Technical Information of China (English)

    Mu ZHAO; Cun-jie LIN; Yong ZHOU

    2017-01-01

    Length-biased data are often encountered in observational studies,when the survival times are left-truncated and right-censored and the truncation times follow a uniform distribution.In this article,we propose to analyze such data with the additive hazards model,which specifies that the hazard function is the sum of an arbitrary baseline hazard function and a regression function of covariates.We develop estimating equation approaches to estimate the regression parameters.The resultant estimators are shown to be consistent and asymptotically normal.Some simulation studies and a real data example are used to evaluate the finite sample properties of the proposed estimators.

  6. A simplified calculation procedure for mass isotopomer distribution analysis (MIDA) based on multiple linear regression.

    Science.gov (United States)

    Fernández-Fernández, Mario; Rodríguez-González, Pablo; García Alonso, J Ignacio

    2016-10-01

    We have developed a novel, rapid and easy calculation procedure for Mass Isotopomer Distribution Analysis based on multiple linear regression which allows the simultaneous calculation of the precursor pool enrichment and the fraction of newly synthesized labelled proteins (fractional synthesis) using linear algebra. To test this approach, we used the peptide RGGGLK as a model tryptic peptide containing three subunits of glycine. We selected glycine labelled in two 13 C atoms ( 13 C 2 -glycine) as labelled amino acid to demonstrate that spectral overlap is not a problem in the proposed methodology. The developed methodology was tested first in vitro by changing the precursor pool enrichment from 10 to 40% of 13 C 2 -glycine. Secondly, a simulated in vivo synthesis of proteins was designed by combining the natural abundance RGGGLK peptide and 10 or 20% 13 C 2 -glycine at 1 : 1, 1 : 3 and 3 : 1 ratios. Precursor pool enrichments and fractional synthesis values were calculated with satisfactory precision and accuracy using a simple spreadsheet. This novel approach can provide a relatively rapid and easy means to measure protein turnover based on stable isotope tracers. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  7. Classification and regression trees

    CERN Document Server

    Breiman, Leo; Olshen, Richard A; Stone, Charles J

    1984-01-01

    The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

  8. Rater bias in psychological research: when is it a problem and what can we do about it?

    Science.gov (United States)

    Hoyt, W T

    2000-03-01

    Rater bias is a substantial source of error in psychological research. Bias distorts observed effect sizes beyond the expected level of attenuation due to intrarater error, and the impact of bias is not accurately estimated using conventional methods of correction for attenuation. Using a model based on multivariate generalizability theory, this article illustrates how bias affects research results. The model identifies 4 types of bias that may affect findings in research using observer ratings, including the biases traditionally termed leniency and halo errors. The impact of bias depends on which of 4 classes of rating design is used, and formulas are derived for correcting observed effect sizes for attenuation (due to bias variance) and inflation (due to bias covariance) in each of these classes. The rater bias model suggests procedures for researchers seeking to minimize adverse impact of bias on study findings.

  9. Correcting for multivariate measurement error by regression calibration in meta-analyses of epidemiological studies.

    NARCIS (Netherlands)

    Kromhout, D.

    2009-01-01

    Within-person variability in measured values of multiple risk factors can bias their associations with disease. The multivariate regression calibration (RC) approach can correct for such measurement error and has been applied to studies in which true values or independent repeat measurements of the

  10. Testing overall and moderator effects meta-regression

    NARCIS (Netherlands)

    Huizenga, H.M.; Visser, I.; Dolan, C.V.

    2011-01-01

    Random effects meta-regression is a technique to synthesize results of multiple studies. It allows for a test of an overall effect, as well as for tests of effects of study characteristics, that is, (discrete or continuous) moderator effects. We describe various procedures to test moderator effects:

  11. Avoiding and Correcting Bias in Score-Based Latent Variable Regression with Discrete Manifest Items

    Science.gov (United States)

    Lu, Irene R. R.; Thomas, D. Roland

    2008-01-01

    This article considers models involving a single structural equation with latent explanatory and/or latent dependent variables where discrete items are used to measure the latent variables. Our primary focus is the use of scores as proxies for the latent variables and carrying out ordinary least squares (OLS) regression on such scores to estimate…

  12. Isotopic biases for actinide-only burnup credit

    International Nuclear Information System (INIS)

    Rahimi, M.; Lancaster, D.; Hoeffer, B.; Nichols, M.

    1997-01-01

    The primary purpose of this paper is to present the new methodology for establishing bias and uncertainty associated with isotopic prediction in spent fuel assemblies for burnup credit analysis. The analysis applies to the design of criticality control systems for spent fuel casks. A total of 54 spent fuel samples were modeled and analyzed using the Shielding Analyses Sequence (SAS2H). Multiple regression analysis and a trending test were performed to develop isotopic correction factors for 10 actinide burnup credit isotopes. 5 refs., 1 tab

  13. Enhancing Rationality: Heuristics, Biases, and The Critical Thinking Project

    Directory of Open Access Journals (Sweden)

    Mark Battersby

    2016-07-01

    Full Text Available Abstract: This paper develops four related claims: 1. Critical thinking should focus more on decision making, 2. the heuristics and bias literature developed by cognitive psychologists and behavioral economists provides many insights into human irrationality which can be useful in critical thinking instruction, 3. unfortunately the “rational choice” norms used by behavioral economists to identify “biased” decision making narrowly equate rational decision making with the efficient pursuit of individual satisfaction; deviations from these norms should not be treated as an irrational bias, 4. a richer, procedural theory of rational decision making should be the basis for critical thinking instruction in decision making.

  14. Continuous validation of ASTEC containment models and regression testing

    International Nuclear Information System (INIS)

    Nowack, Holger; Reinke, Nils; Sonnenkalb, Martin

    2014-01-01

    The focus of the ASTEC (Accident Source Term Evaluation Code) development at GRS is primarily on the containment module CPA (Containment Part of ASTEC), whose modelling is to a large extent based on the GRS containment code COCOSYS (COntainment COde SYStem). Validation is usually understood as the approval of the modelling capabilities by calculations of appropriate experiments done by external users different from the code developers. During the development process of ASTEC CPA, bugs and unintended side effects may occur, which leads to changes in the results of the initially conducted validation. Due to the involvement of a considerable number of developers in the coding of ASTEC modules, validation of the code alone, even if executed repeatedly, is not sufficient. Therefore, a regression testing procedure has been implemented in order to ensure that the initially obtained validation results are still valid with succeeding code versions. Within the regression testing procedure, calculations of experiments and plant sequences are performed with the same input deck but applying two different code versions. For every test-case the up-to-date code version is compared to the preceding one on the basis of physical parameters deemed to be characteristic for the test-case under consideration. In the case of post-calculations of experiments also a comparison to experimental data is carried out. Three validation cases from the regression testing procedure are presented within this paper. The very good post-calculation of the HDR E11.1 experiment shows the high quality modelling of thermal-hydraulics in ASTEC CPA. Aerosol behaviour is validated on the BMC VANAM M3 experiment, and the results show also a very good agreement with experimental data. Finally, iodine behaviour is checked in the validation test-case of the THAI IOD-11 experiment. Within this test-case, the comparison of the ASTEC versions V2.0r1 and V2.0r2 shows how an error was detected by the regression testing

  15. Model-based Quantile Regression for Discrete Data

    KAUST Repository

    Padellini, Tullia

    2018-04-10

    Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite the fact that this leads to a proper posterior for the regression coefficients, the resulting posterior variance is however affected by an unidentifiable parameter, hence any inferential procedure beside point estimation is unreliable. We propose a model-based approach for quantile regression that considers quantiles of the generating distribution directly, and thus allows for a proper uncertainty quantification. We then create a link between quantile regression and generalised linear models by mapping the quantiles to the parameter of the response variable, and we exploit it to fit the model with R-INLA. We extend it also in the case of discrete responses, where there is no 1-to-1 relationship between quantiles and distribution\\'s parameter, by introducing continuous generalisations of the most common discrete variables (Poisson, Binomial and Negative Binomial) to be exploited in the fitting.

  16. Potential for Bias When Estimating Critical Windows for Air Pollution in Children's Health.

    Science.gov (United States)

    Wilson, Ander; Chiu, Yueh-Hsiu Mathilda; Hsu, Hsiao-Hsien Leon; Wright, Robert O; Wright, Rosalind J; Coull, Brent A

    2017-12-01

    Evidence supports an association between maternal exposure to air pollution during pregnancy and children's health outcomes. Recent interest has focused on identifying critical windows of vulnerability. An analysis based on a distributed lag model (DLM) can yield estimates of a critical window that are different from those from an analysis that regresses the outcome on each of the 3 trimester-average exposures (TAEs). Using a simulation study, we assessed bias in estimates of critical windows obtained using 3 regression approaches: 1) 3 separate models to estimate the association with each of the 3 TAEs; 2) a single model to jointly estimate the association between the outcome and all 3 TAEs; and 3) a DLM. We used weekly fine-particulate-matter exposure data for 238 births in a birth cohort in and around Boston, Massachusetts, and a simulated outcome and time-varying exposure effect. Estimates using separate models for each TAE were biased and identified incorrect windows. This bias arose from seasonal trends in particulate matter that induced correlation between TAEs. Including all TAEs in a single model reduced bias. DLM produced unbiased estimates and added flexibility to identify windows. Analysis of body mass index z score and fat mass in the same cohort highlighted inconsistent estimates from the 3 methods. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  17. Use of bias correction techniques to improve seasonal forecasts for reservoirs - A case-study in northwestern Mediterranean.

    Science.gov (United States)

    Marcos, Raül; Llasat, Ma Carmen; Quintana-Seguí, Pere; Turco, Marco

    2018-01-01

    In this paper, we have compared different bias correction methodologies to assess whether they could be advantageous for improving the performance of a seasonal prediction model for volume anomalies in the Boadella reservoir (northwestern Mediterranean). The bias correction adjustments have been applied on precipitation and temperature from the European Centre for Middle-range Weather Forecasting System 4 (S4). We have used three bias correction strategies: two linear (mean bias correction, BC, and linear regression, LR) and one non-linear (Model Output Statistics analogs, MOS-analog). The results have been compared with climatology and persistence. The volume-anomaly model is a previously computed Multiple Linear Regression that ingests precipitation, temperature and in-flow anomaly data to simulate monthly volume anomalies. The potential utility for end-users has been assessed using economic value curve areas. We have studied the S4 hindcast period 1981-2010 for each month of the year and up to seven months ahead considering an ensemble of 15 members. We have shown that the MOS-analog and LR bias corrections can improve the original S4. The application to volume anomalies points towards the possibility to introduce bias correction methods as a tool to improve water resource seasonal forecasts in an end-user context of climate services. Particularly, the MOS-analog approach gives generally better results than the other approaches in late autumn and early winter. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. A re-examination of the effects of biased lineup instructions in eyewitness identification.

    Science.gov (United States)

    Clark, Steven E

    2005-10-01

    A meta-analytic review of research comparing biased and unbiased instructions in eyewitness identification experiments showed an asymmetry; specifically, that biased instructions led to a large and consistent decrease in accuracy in target-absent lineups, but produced inconsistent results for target-present lineups, with an average effect size near zero (Steblay, 1997). The results for target-present lineups are surprising, and are inconsistent with statistical decision theories (i.e., Green & Swets, 1966). A re-examination of the relevant studies and the meta-analysis of those studies shows clear evidence that correct identification rates do increase with biased lineup instructions, and that biased witnesses make correct identifications at a rate considerably above chance. Implications for theory, as well as police procedure and policy, are discussed.

  19. Some Cochrane risk of bias items are not important in osteoarthritis trials

    DEFF Research Database (Denmark)

    Bolvig, Julie; Juhl, Carsten B; Boutron, Isabelle

    2018-01-01

    of the risk of bias tool (RoB), trial size, single vs multi-site, and source of funding. Effect sizes were calculated as standardized mean differences (SMDs). Meta-regression was performed to identify "relevant study-level covariates" that decreases the between-study variance (τˆ2). RESULTS: Twenty reviews...

  20. Cognitive Reflection, Decision Biases, and Response Times.

    Science.gov (United States)

    Alós-Ferrer, Carlos; Garagnani, Michele; Hügelschäfer, Sabine

    2016-01-01

    We present novel evidence on response times and personality traits in standard questions from the decision-making literature where responses are relatively slow (medians around half a minute or above). To this end, we measured response times in a number of incentivized, framed items (decisions from description) including the Cognitive Reflection Test, two additional questions following the same logic, and a number of classic questions used to study decision biases in probability judgments (base-rate neglect, the conjunction fallacy, and the ratio bias). All questions create a conflict between an intuitive process and more deliberative thinking. For each item, we then created a non-conflict version by either making the intuitive impulse correct (resulting in an alignment question), shutting it down (creating a neutral question), or making it dominant (creating a heuristic question). For CRT questions, the differences in response times are as predicted by dual-process theories, with alignment and heuristic variants leading to faster responses and neutral questions to slower responses than the original, conflict questions. For decision biases (where responses are slower), evidence is mixed. To explore the possible influence of personality factors on both choices and response times, we used standard personality scales including the Rational-Experiential Inventory and the Big Five, and used them as controls in regression analysis.

  1. A method for nonlinear exponential regression analysis

    Science.gov (United States)

    Junkin, B. G.

    1971-01-01

    A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.

  2. Bias correction of risk estimates in vaccine safety studies with rare adverse events using a self-controlled case series design.

    Science.gov (United States)

    Zeng, Chan; Newcomer, Sophia R; Glanz, Jason M; Shoup, Jo Ann; Daley, Matthew F; Hambidge, Simon J; Xu, Stanley

    2013-12-15

    The self-controlled case series (SCCS) method is often used to examine the temporal association between vaccination and adverse events using only data from patients who experienced such events. Conditional Poisson regression models are used to estimate incidence rate ratios, and these models perform well with large or medium-sized case samples. However, in some vaccine safety studies, the adverse events studied are rare and the maximum likelihood estimates may be biased. Several bias correction methods have been examined in case-control studies using conditional logistic regression, but none of these methods have been evaluated in studies using the SCCS design. In this study, we used simulations to evaluate 2 bias correction approaches-the Firth penalized maximum likelihood method and Cordeiro and McCullagh's bias reduction after maximum likelihood estimation-with small sample sizes in studies using the SCCS design. The simulations showed that the bias under the SCCS design with a small number of cases can be large and is also sensitive to a short risk period. The Firth correction method provides finite and less biased estimates than the maximum likelihood method and Cordeiro and McCullagh's method. However, limitations still exist when the risk period in the SCCS design is short relative to the entire observation period.

  3. Modeling bias and variation in the stochastic processes of small RNA sequencing.

    Science.gov (United States)

    Argyropoulos, Christos; Etheridge, Alton; Sakhanenko, Nikita; Galas, David

    2017-06-20

    The use of RNA-seq as the preferred method for the discovery and validation of small RNA biomarkers has been hindered by high quantitative variability and biased sequence counts. In this paper we develop a statistical model for sequence counts that accounts for ligase bias and stochastic variation in sequence counts. This model implies a linear quadratic relation between the mean and variance of sequence counts. Using a large number of sequencing datasets, we demonstrate how one can use the generalized additive models for location, scale and shape (GAMLSS) distributional regression framework to calculate and apply empirical correction factors for ligase bias. Bias correction could remove more than 40% of the bias for miRNAs. Empirical bias correction factors appear to be nearly constant over at least one and up to four orders of magnitude of total RNA input and independent of sample composition. Using synthetic mixes of known composition, we show that the GAMLSS approach can analyze differential expression with greater accuracy, higher sensitivity and specificity than six existing algorithms (DESeq2, edgeR, EBSeq, limma, DSS, voom) for the analysis of small RNA-seq data. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  4. Refractive regression after laser in situ keratomileusis.

    Science.gov (United States)

    Yan, Mabel K; Chang, John Sm; Chan, Tommy Cy

    2018-04-26

    Uncorrected refractive errors are a leading cause of visual impairment across the world. In today's society, laser in situ keratomileusis (LASIK) has become the most commonly performed surgical procedure to correct refractive errors. However, regression of the initially achieved refractive correction has been a widely observed phenomenon following LASIK since its inception more than two decades ago. Despite technological advances in laser refractive surgery and various proposed management strategies, post-LASIK regression is still frequently observed and has significant implications for the long-term visual performance and quality of life of patients. This review explores the mechanism of refractive regression after both myopic and hyperopic LASIK, predisposing risk factors and its clinical course. In addition, current preventative strategies and therapies are also reviewed. © 2018 Royal Australian and New Zealand College of Ophthalmologists.

  5. Regularized multivariate regression models with skew-t error distributions

    KAUST Repository

    Chen, Lianfu; Pourahmadi, Mohsen; Maadooliat, Mehdi

    2014-01-01

    We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both

  6. bayesQR: A Bayesian Approach to Quantile Regression

    Directory of Open Access Journals (Sweden)

    Dries F. Benoit

    2017-01-01

    Full Text Available After its introduction by Koenker and Basset (1978, quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution. The package contains functions for the typical quantile regression with continuous dependent variable, but also supports quantile regression for binary dependent variables. For both types of dependent variables, an approach to variable selection using the adaptive lasso approach is provided. For the binary quantile regression model, the package also contains a routine that calculates the fitted probabilities for each vector of predictors. In addition, functions for summarizing the results, creating traceplots, posterior histograms and drawing quantile plots are included. This paper starts with a brief overview of the theoretical background of the models used in the bayesQR package. The main part of this paper discusses the computational problems that arise in the implementation of the procedure and illustrates the usefulness of the package through selected examples.

  7. RELIC: a novel dye-bias correction method for Illumina Methylation BeadChip.

    Science.gov (United States)

    Xu, Zongli; Langie, Sabine A S; De Boever, Patrick; Taylor, Jack A; Niu, Liang

    2017-01-03

    The Illumina Infinium HumanMethylation450 BeadChip and its successor, Infinium MethylationEPIC BeadChip, have been extensively utilized in epigenome-wide association studies. Both arrays use two fluorescent dyes (Cy3-green/Cy5-red) to measure methylation level at CpG sites. However, performance difference between dyes can result in biased estimates of methylation levels. Here we describe a novel method, called REgression on Logarithm of Internal Control probes (RELIC) to correct for dye bias on whole array by utilizing the intensity values of paired internal control probes that monitor the two color channels. We evaluate the method in several datasets against other widely used dye-bias correction methods. Results on data quality improvement showed that RELIC correction statistically significantly outperforms alternative dye-bias correction methods. We incorporated the method into the R package ENmix, which is freely available from the Bioconductor website ( https://www.bioconductor.org/packages/release/bioc/html/ENmix.html ). RELIC is an efficient and robust method to correct for dye-bias in Illumina Methylation BeadChip data. It outperforms other alternative methods and conveniently implemented in R package ENmix to facilitate DNA methylation studies.

  8. Performance of bias-correction methods for exposure measurement error using repeated measurements with and without missing data.

    Science.gov (United States)

    Batistatou, Evridiki; McNamee, Roseanne

    2012-12-10

    It is known that measurement error leads to bias in assessing exposure effects, which can however, be corrected if independent replicates are available. For expensive replicates, two-stage (2S) studies that produce data 'missing by design', may be preferred over a single-stage (1S) study, because in the second stage, measurement of replicates is restricted to a sample of first-stage subjects. Motivated by an occupational study on the acute effect of carbon black exposure on respiratory morbidity, we compare the performance of several bias-correction methods for both designs in a simulation study: an instrumental variable method (EVROS IV) based on grouping strategies, which had been recommended especially when measurement error is large, the regression calibration and the simulation extrapolation methods. For the 2S design, either the problem of 'missing' data was ignored or the 'missing' data were imputed using multiple imputations. Both in 1S and 2S designs, in the case of small or moderate measurement error, regression calibration was shown to be the preferred approach in terms of root mean square error. For 2S designs, regression calibration as implemented by Stata software is not recommended in contrast to our implementation of this method; the 'problematic' implementation of regression calibration although substantially improved with use of multiple imputations. The EVROS IV method, under a good/fairly good grouping, outperforms the regression calibration approach in both design scenarios when exposure mismeasurement is severe. Both in 1S and 2S designs with moderate or large measurement error, simulation extrapolation severely failed to correct for bias. Copyright © 2012 John Wiley & Sons, Ltd.

  9. Multiple regression for physiological data analysis: the problem of multicollinearity.

    Science.gov (United States)

    Slinker, B K; Glantz, S A

    1985-07-01

    Multiple linear regression, in which several predictor variables are related to a response variable, is a powerful statistical tool for gaining quantitative insight into complex in vivo physiological systems. For these insights to be correct, all predictor variables must be uncorrelated. However, in many physiological experiments the predictor variables cannot be precisely controlled and thus change in parallel (i.e., they are highly correlated). There is a redundancy of information about the response, a situation called multicollinearity, that leads to numerical problems in estimating the parameters in regression equations; the parameters are often of incorrect magnitude or sign or have large standard errors. Although multicollinearity can be avoided with good experimental design, not all interesting physiological questions can be studied without encountering multicollinearity. In these cases various ad hoc procedures have been proposed to mitigate multicollinearity. Although many of these procedures are controversial, they can be helpful in applying multiple linear regression to some physiological problems.

  10. Sympathetic bias.

    Science.gov (United States)

    Levy, David M; Peart, Sandra J

    2008-06-01

    We wish to deal with investigator bias in a statistical context. We sketch how a textbook solution to the problem of "outliers" which avoids one sort of investigator bias, creates the temptation for another sort. We write down a model of the approbation seeking statistician who is tempted by sympathy for client to violate the disciplinary standards. We give a simple account of one context in which we might expect investigator bias to flourish. Finally, we offer tentative suggestions to deal with the problem of investigator bias which follow from our account. As we have given a very sparse and stylized account of investigator bias, we ask what might be done to overcome this limitation.

  11. Regularized multivariate regression models with skew-t error distributions

    KAUST Repository

    Chen, Lianfu

    2014-06-01

    We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both the regression coefficient and inverse scale matrices simultaneously. The sparsity is introduced through penalizing the negative log-likelihood by adding L1-penalties on the entries of the two matrices. Taking advantage of the hierarchical representation of skew-t distributions, and using the expectation conditional maximization (ECM) algorithm, we reduce the problem to penalized normal likelihood and develop a procedure to minimize the ensuing objective function. Using a simulation study the performance of the method is assessed, and the methodology is illustrated using a real data set with a 24-dimensional response vector. © 2014 Elsevier B.V.

  12. Bias modification training can alter approach bias and chocolate consumption.

    Science.gov (United States)

    Schumacher, Sophie E; Kemps, Eva; Tiggemann, Marika

    2016-01-01

    Recent evidence has demonstrated that bias modification training has potential to reduce cognitive biases for attractive targets and affect health behaviours. The present study investigated whether cognitive bias modification training could be applied to reduce approach bias for chocolate and affect subsequent chocolate consumption. A sample of 120 women (18-27 years) were randomly assigned to an approach-chocolate condition or avoid-chocolate condition, in which they were trained to approach or avoid pictorial chocolate stimuli, respectively. Training had the predicted effect on approach bias, such that participants trained to approach chocolate demonstrated an increased approach bias to chocolate stimuli whereas participants trained to avoid such stimuli showed a reduced bias. Further, participants trained to avoid chocolate ate significantly less of a chocolate muffin in a subsequent taste test than participants trained to approach chocolate. Theoretically, results provide support for the dual process model's conceptualisation of consumption as being driven by implicit processes such as approach bias. In practice, approach bias modification may be a useful component of interventions designed to curb the consumption of unhealthy foods. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. A baseline-free procedure for transformation models under interval censorship.

    Science.gov (United States)

    Gu, Ming Gao; Sun, Liuquan; Zuo, Guoxin

    2005-12-01

    An important property of Cox regression model is that the estimation of regression parameters using the partial likelihood procedure does not depend on its baseline survival function. We call such a procedure baseline-free. Using marginal likelihood, we show that an baseline-free procedure can be derived for a class of general transformation models under interval censoring framework. The baseline-free procedure results a simplified and stable computation algorithm for some complicated and important semiparametric models, such as frailty models and heteroscedastic hazard/rank regression models, where the estimation procedures so far available involve estimation of the infinite dimensional baseline function. A detailed computational algorithm using Markov Chain Monte Carlo stochastic approximation is presented. The proposed procedure is demonstrated through extensive simulation studies, showing the validity of asymptotic consistency and normality. We also illustrate the procedure with a real data set from a study of breast cancer. A heuristic argument showing that the score function is a mean zero martingale is provided.

  14. Animal models of maternal high fat diet exposure and effects on metabolism in offspring: a meta-regression analysis.

    Science.gov (United States)

    Ribaroff, G A; Wastnedge, E; Drake, A J; Sharpe, R M; Chambers, T J G

    2017-06-01

    Animal models of maternal high fat diet (HFD) demonstrate perturbed offspring metabolism although the effects differ markedly between models. We assessed studies investigating metabolic parameters in the offspring of HFD fed mothers to identify factors explaining these inter-study differences. A total of 171 papers were identified, which provided data from 6047 offspring. Data were extracted regarding body weight, adiposity, glucose homeostasis and lipidaemia. Information regarding the macronutrient content of diet, species, time point of exposure and gestational weight gain were collected and utilized in meta-regression models to explore predictive factors. Publication bias was assessed using Egger's regression test. Maternal HFD exposure did not affect offspring birthweight but increased weaning weight, final bodyweight, adiposity, triglyceridaemia, cholesterolaemia and insulinaemia in both female and male offspring. Hyperglycaemia was found in female offspring only. Meta-regression analysis identified lactational HFD exposure as a key moderator. The fat content of the diet did not correlate with any outcomes. There was evidence of significant publication bias for all outcomes except birthweight. Maternal HFD exposure was associated with perturbed metabolism in offspring but between studies was not accounted for by dietary constituents, species, strain or maternal gestational weight gain. Specific weaknesses in experimental design predispose many of the results to bias. © 2017 The Authors. Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation.

  15. Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions

    NARCIS (Netherlands)

    Ernst, Anja F.; Albers, Casper J.

    2017-01-01

    Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated

  16. A Note on Three Statistical Tests in the Logistic Regression DIF Procedure

    Science.gov (United States)

    Paek, Insu

    2012-01-01

    Although logistic regression became one of the well-known methods in detecting differential item functioning (DIF), its three statistical tests, the Wald, likelihood ratio (LR), and score tests, which are readily available under the maximum likelihood, do not seem to be consistently distinguished in DIF literature. This paper provides a clarifying…

  17. Combination of biased forecasts: Bias correction or bias based weights?

    OpenAIRE

    Wenzel, Thomas

    1999-01-01

    Most of the literature on combination of forecasts deals with the assumption of unbiased individual forecasts. Here, we consider the case of biased forecasts and discuss two different combination techniques resulting in an unbiased forecast. On the one hand we correct the individual forecasts, and on the other we calculate bias based weights. A simulation study gives some insight in the situations where we should use the different methods.

  18. Estimation bias and bias correction in reduced rank autoregressions

    DEFF Research Database (Denmark)

    Nielsen, Heino Bohn

    2017-01-01

    This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias at the MLE. The other is an iterative root...

  19. Performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data.

    Science.gov (United States)

    Yelland, Lisa N; Salter, Amy B; Ryan, Philip

    2011-10-15

    Modified Poisson regression, which combines a log Poisson regression model with robust variance estimation, is a useful alternative to log binomial regression for estimating relative risks. Previous studies have shown both analytically and by simulation that modified Poisson regression is appropriate for independent prospective data. This method is often applied to clustered prospective data, despite a lack of evidence to support its use in this setting. The purpose of this article is to evaluate the performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data, by using generalized estimating equations to account for clustering. A simulation study is conducted to compare log binomial regression and modified Poisson regression for analyzing clustered data from intervention and observational studies. Both methods generally perform well in terms of bias, type I error, and coverage. Unlike log binomial regression, modified Poisson regression is not prone to convergence problems. The methods are contrasted by using example data sets from 2 large studies. The results presented in this article support the use of modified Poisson regression as an alternative to log binomial regression for analyzing clustered prospective data when clustering is taken into account by using generalized estimating equations.

  20. Emotional Issues and Peer Relations in Gifted Elementary Students: Regression Analysis of National Data

    Science.gov (United States)

    Wiley, Kristofor R.

    2013-01-01

    Many of the social and emotional needs that have historically been associated with gifted students have been questioned on the basis of recent empirical evidence. Research on the topic, however, is often limited by sample size, selection bias, or definition. This study addressed these limitations by applying linear regression methodology to data…

  1. Comparison of the linear bias models in the light of the Dark Energy Survey

    Science.gov (United States)

    Papageorgiou, A.; Basilakos, S.; Plionis, M.

    2018-05-01

    The evolution of the linear and scale independent bias, based on the most popular dark matter bias models within the Λ cold dark matter (ΛCDM) cosmology, is confronted to that of the Dark Energy Survey (DES) luminous red galaxies (LRGs). Applying a χ2 minimization procedure between models and data, we find that all the considered linear bias models reproduce well the LRG bias data. The differences among the bias models are absorbed in the predicted mass of the dark-matter halo in which LRGs live and which ranges between ˜6 × 1012 and 1.4 × 1013 h-1 M⊙, for the different bias models. Similar results, reaching however a maximum value of ˜2 × 1013 h-1 M⊙, are found by confronting the SDSS (2SLAQ) Large Red Galaxies clustering with theoretical clustering models, which also include the evolution of bias. This later analysis also provides a value of Ωm = 0.30 ± 0.01, which is in excellent agreement with recent joint analyses of different cosmological probes and the reanalysis of the Planck data.

  2. Differentiating regressed melanoma from regressed lichenoid keratosis.

    Science.gov (United States)

    Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A

    2017-04-01

    Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  3. A SAS-macro for estimation of the cumulative incidence using Poisson regression

    DEFF Research Database (Denmark)

    Waltoft, Berit Lindum

    2009-01-01

    the hazard rates, and the hazard rates are often estimated by the Cox regression. This procedure may not be suitable for large studies due to limited computer resources. Instead one uses Poisson regression, which approximates the Cox regression. Rosthøj et al. presented a SAS-macro for the estimation...... of the cumulative incidences based on the Cox regression. I present the functional form of the probabilities and variances when using piecewise constant hazard rates and a SAS-macro for the estimation using Poisson regression. The use of the macro is demonstrated through examples and compared to the macro presented...

  4. Normalization Ridge Regression in Practice II: The Estimation of Multiple Feedback Linkages.

    Science.gov (United States)

    Bulcock, J. W.

    The use of the two-stage least squares (2 SLS) procedure for estimating nonrecursive social science models is often impractical when multiple feedback linkages are required. This is because 2 SLS is extremely sensitive to multicollinearity. The standard statistical solution to the multicollinearity problem is a biased, variance reduced procedure…

  5. HMO marketing and selection bias: are TEFRA HMOs skimming?

    Science.gov (United States)

    Lichtenstein, R; Thomas, J W; Watkins, B; Puto, C; Lepkowski, J; Adams-Watson, J; Simone, B; Vest, D

    1992-04-01

    The research evidence indicates that health maintenance organizations (HMOs) participating in the Tax Equity and Fiscal Responsibility Act of 1982 (TEFRA) At-Risk Program tend to experience favorable selection. Although favorable selection might result from patient decisions, a common conjecture is that it can be induced by HMOs through their marketing activities. The purpose of this study is to examine the relationship between HMO marketing strategies and selection bias in TEFRA At-Risk HMOs. A purposive sample of 22 HMOs that were actively marketing their TEFRA programs was selected and data on organizational characteristics, market area characteristics, and HMO marketing decisions were collected. To measure selection bias in these HMOs, the functional health status of approximately 300 enrollees in each HMO was compared to that of 300 non-enrolling beneficiaries in the same area. Three dependent variables, reflecting selection bias at the mean, the low health tail, and the high health tail of the health status distribution were created. Weighted least squares regressions were then used to identify relationships between marketing elements and selection bias. Subject to the statistical limitations of the study, our conclusion is that it is doubtful that HMO marketing decisions are responsible for the prevalence of favorable selection in HMO enrollment. It also appears unlikely that HMOs were differentially targeting healthy and unhealthy segments of the Medicare market.

  6. [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.

  7. Accounting for response misclassification and covariate measurement error improves power and reduces bias in epidemiologic studies.

    Science.gov (United States)

    Cheng, Dunlei; Branscum, Adam J; Stamey, James D

    2010-07-01

    To quantify the impact of ignoring misclassification of a response variable and measurement error in a covariate on statistical power, and to develop software for sample size and power analysis that accounts for these flaws in epidemiologic data. A Monte Carlo simulation-based procedure is developed to illustrate the differences in design requirements and inferences between analytic methods that properly account for misclassification and measurement error to those that do not in regression models for cross-sectional and cohort data. We found that failure to account for these flaws in epidemiologic data can lead to a substantial reduction in statistical power, over 25% in some cases. The proposed method substantially reduced bias by up to a ten-fold margin compared to naive estimates obtained by ignoring misclassification and mismeasurement. We recommend as routine practice that researchers account for errors in measurement of both response and covariate data when determining sample size, performing power calculations, or analyzing data from epidemiological studies. 2010 Elsevier Inc. All rights reserved.

  8. Cognitive Reflection, Decision Biases, and Response Times

    Directory of Open Access Journals (Sweden)

    Carlos Alos-Ferrer

    2016-09-01

    Full Text Available We present novel evidence on decision times and personality traits in standard questions from the decision-making literature where responses are relatively slow (medians around half a minute or above. To this end, we measured decision times in a number of incentivized, framed items (decisions from description including the Cognitive Reflection Test, two additional questions following the same logic, and a number of classic questions used to study decision biases in probability judgments (base-rate neglect, the conjunction fallacy, and the ratio bias. All questions create a conflict between an intuitive process and more deliberative thinking. For each item, we then created a non-conflict version by either making the intuitive impulse correct (resulting in an alignment question, shutting it down (creating a neutral question, or making it dominant (creating a heuristic question. For CRT questions, the differences in decision times are as predicted by dual-process theories, with alignment and heuristic variants leading to faster responses and neutral questions to slower responses than the original, conflict questions. For decision biases (where responses are slower, evidence is mixed. To explore the possible influence of personality factors on both choices and decision times, we used standard personality scales including the Rational-Experiential Inventory and the Big Five, and used the mas controls in regression analysis.

  9. Investigation of the Performance of Multidimensional Equating Procedures for Common-Item Nonequivalent Groups Design

    Directory of Open Access Journals (Sweden)

    Burcu ATAR

    2017-12-01

    Full Text Available In this study, the performance of the multidimensional extentions of Stocking-Lord, mean/mean, and mean/sigma equating procedures under common-item nonequivalent groups design was investigated. The performance of those three equating procedures was examined under the combination of various conditions including sample size, ability distribution, correlation between two dimensions, and percentage of anchor items in the test. Item parameter recovery was evaluated calculating RMSE (root man squared error and BIAS values. It was found that Stocking-Lord procedure provided the smaller RMSE and BIAS values for both item discrimination and item difficulty parameter estimates across most conditions.

  10. Attention bias for chocolate increases chocolate consumption--an attention bias modification study.

    Science.gov (United States)

    Werthmann, Jessica; Field, Matt; Roefs, Anne; Nederkoorn, Chantal; Jansen, Anita

    2014-03-01

    The current study examined experimentally whether a manipulated attention bias for food cues increases craving, chocolate intake and motivation to search for hidden chocolates. To test the effect of attention for food on subsequent chocolate intake, attention for chocolate was experimentally modified by instructing participants to look at chocolate stimuli ("attend chocolate" group) or at non-food stimuli ("attend shoes" group) during a novel attention bias modification task (antisaccade task). Chocolate consumption, changes in craving and search time for hidden chocolates were assessed. Eye-movement recordings were used to monitor the accuracy during the experimental attention modification task as possible moderator of effects. Regression analyses were conducted to test the effect of attention modification and modification accuracy on chocolate intake, craving and motivation to search for hidden chocolates. Results showed that participants with higher accuracy (+1 SD), ate more chocolate when they had to attend to chocolate and ate less chocolate when they had to attend to non-food stimuli. In contrast, for participants with lower accuracy (-1 SD), the results were exactly reversed. No effects of the experimental attention modification on craving or search time for hidden chocolates were found. We used chocolate as food stimuli so it remains unclear how our findings generalize to other types of food. These findings demonstrate further evidence for a link between attention for food and food intake, and provide an indication about the direction of this relationship. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. Direction of Effects in Multiple Linear Regression Models.

    Science.gov (United States)

    Wiedermann, Wolfgang; von Eye, Alexander

    2015-01-01

    Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.

  12. Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression.

    Science.gov (United States)

    Ding, A Adam; Wu, Hulin

    2014-10-01

    We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method.

  13. Identification of Influential Points in a Linear Regression Model

    Directory of Open Access Journals (Sweden)

    Jan Grosz

    2011-03-01

    Full Text Available The article deals with the detection and identification of influential points in the linear regression model. Three methods of detection of outliers and leverage points are described. These procedures can also be used for one-sample (independentdatasets. This paper briefly describes theoretical aspects of several robust methods as well. Robust statistics is a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. A simulation model of the simple linear regression is presented.

  14. Nonparametric additive regression for repeatedly measured data

    KAUST Repository

    Carroll, R. J.

    2009-05-20

    We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. © 2009 Biometrika Trust.

  15. Sample Size and Robustness of Inferences from Logistic Regression in the Presence of Nonlinearity and Multicollinearity

    OpenAIRE

    Bergtold, Jason S.; Yeager, Elizabeth A.; Featherstone, Allen M.

    2011-01-01

    The logistic regression models has been widely used in the social and natural sciences and results from studies using this model can have significant impact. Thus, confidence in the reliability of inferences drawn from these models is essential. The robustness of such inferences is dependent on sample size. The purpose of this study is to examine the impact of sample size on the mean estimated bias and efficiency of parameter estimation and inference for the logistic regression model. A numbe...

  16. Addressing criticisms of existing predictive bias research: cognitive ability test scores still overpredict African Americans' job performance.

    Science.gov (United States)

    Berry, Christopher M; Zhao, Peng

    2015-01-01

    Predictive bias studies have generally suggested that cognitive ability test scores overpredict job performance of African Americans, meaning these tests are not predictively biased against African Americans. However, at least 2 issues call into question existing over-/underprediction evidence: (a) a bias identified by Aguinis, Culpepper, and Pierce (2010) in the intercept test typically used to assess over-/underprediction and (b) a focus on the level of observed validity instead of operational validity. The present study developed and utilized a method of assessing over-/underprediction that draws on the math of subgroup regression intercept differences, does not rely on the biased intercept test, allows for analysis at the level of operational validity, and can use meta-analytic estimates as input values. Therefore, existing meta-analytic estimates of key parameters, corrected for relevant statistical artifacts, were used to determine whether African American job performance remains overpredicted at the level of operational validity. African American job performance was typically overpredicted by cognitive ability tests across levels of job complexity and across conditions wherein African American and White regression slopes did and did not differ. Because the present study does not rely on the biased intercept test and because appropriate statistical artifact corrections were carried out, the present study's results are not affected by the 2 issues mentioned above. The present study represents strong evidence that cognitive ability tests generally overpredict job performance of African Americans. (c) 2015 APA, all rights reserved.

  17. 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.

  18. Principal component regression for crop yield estimation

    CERN Document Server

    Suryanarayana, T M V

    2016-01-01

    This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of ...

  19. Analysis of tag-position bias in MPSS technology

    Directory of Open Access Journals (Sweden)

    Rattray Magnus

    2006-04-01

    Full Text Available Abstract Background Massively Parallel Signature Sequencing (MPSS technology was recently developed as a high-throughput technology for measuring the concentration of mRNA transcripts in a sample. It has previously been observed that the position of the signature tag in a transcript (distance from 3' end can affect the measurement, but this effect has not been studied in detail. Results We quantify the effect of tag-position bias in Classic and Signature MPSS technology using published data from Arabidopsis, rice and human. We investigate the relationship between measured concentration and tag-position using nonlinear regression methods. The observed relationship is shown to be broadly consistent across different data sets. We find that there exist different and significant biases in both Classic and Signature MPSS data. For Classic MPSS data, genes with tag-position in the middle-range have highest measured abundance on average while genes with tag-position in the high-range, far from the 3' end, show a significant decrease. For Signature MPSS data, high-range tag-position genes tend to have a flatter relationship between tag-position and measured abundance. Thus, our results confirm that the Signature MPSS method fixes a substantial problem with the Classic MPSS method. For both Classic and Signature MPSS data there is a positive correlation between measured abundance and tag-position for low-range tag-position genes. Compared with the effects of mRNA length and number of exons, tag-position bias seems to be more significant in Arabadopsis. The tag-position bias is reflected both in the measured abundance of genes with a significant tag count and in the proportion of unexpressed genes identified. Conclusion Tag-position bias should be taken into consideration when measuring mRNA transcript abundance using MPSS technology, both in Classic and Signature MPSS methods.

  20. Reducing Prejudice With Labels: Shared Group Memberships Attenuate Implicit Bias and Expand Implicit Group Boundaries.

    Science.gov (United States)

    Scroggins, W Anthony; Mackie, Diane M; Allen, Thomas J; Sherman, Jeffrey W

    2016-02-01

    In three experiments, we used a novel Implicit Association Test procedure to investigate the impact of group memberships on implicit bias and implicit group boundaries. Results from Experiment 1 indicated that categorizing targets using a shared category reduced implicit bias by increasing the extent to which positivity was associated with Blacks. Results from Experiment 2 revealed that shared group membership, but not mere positivity of a group membership, was necessary to reduce implicit bias. Quadruple process model analyses indicated that changes in implicit bias caused by shared group membership are due to changes in the way that targets are evaluated, not to changes in the regulation of evaluative bias. Results from Experiment 3 showed that categorizing Black targets into shared group memberships expanded implicit group boundaries. © 2015 by the Society for Personality and Social Psychology, Inc.

  1. Confusing procedures with process when appraising the impact of cognitive bias modification on emotional vulnerability

    NARCIS (Netherlands)

    Grafton, B.; MacLeod, C.; Rudaizky, D.; Holmes, E.A.; Salemink, E.; Fox, E.; Notebaert, L.

    2017-01-01

    If meta-analysis is to provide valuable answers, then it is critical to ensure clarity about the questions being asked. Here, we distinguish two important questions concerning cognitive bias modification research that are not differentiated in the meta-analysis recently published by Cristea et al

  2. Model selection for marginal regression analysis of longitudinal data with missing observations and covariate measurement error.

    Science.gov (United States)

    Shen, Chung-Wei; Chen, Yi-Hau

    2015-10-01

    Missing observations and covariate measurement error commonly arise in longitudinal data. However, existing methods for model selection in marginal regression analysis of longitudinal data fail to address the potential bias resulting from these issues. To tackle this problem, we propose a new model selection criterion, the Generalized Longitudinal Information Criterion, which is based on an approximately unbiased estimator for the expected quadratic error of a considered marginal model accounting for both data missingness and covariate measurement error. The simulation results reveal that the proposed method performs quite well in the presence of missing data and covariate measurement error. On the contrary, the naive procedures without taking care of such complexity in data may perform quite poorly. The proposed method is applied to data from the Taiwan Longitudinal Study on Aging to assess the relationship of depression with health and social status in the elderly, accommodating measurement error in the covariate as well as missing observations. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  3. A Powerful Test for Comparing Multiple Regression Functions.

    Science.gov (United States)

    Maity, Arnab

    2012-09-01

    In this article, we address the important problem of comparison of two or more population regression functions. Recently, Pardo-Fernández, Van Keilegom and González-Manteiga (2007) developed test statistics for simple nonparametric regression models: Y(ij) = θ(j)(Z(ij)) + σ(j)(Z(ij))∊(ij), based on empirical distributions of the errors in each population j = 1, … , J. In this paper, we propose a test for equality of the θ(j)(·) based on the concept of generalized likelihood ratio type statistics. We also generalize our test for other nonparametric regression setups, e.g, nonparametric logistic regression, where the loglikelihood for population j is any general smooth function [Formula: see text]. We describe a resampling procedure to obtain the critical values of the test. In addition, we present a simulation study to evaluate the performance of the proposed test and compare our results to those in Pardo-Fernández et al. (2007).

  4. Learning Supervised Topic Models for Classification and Regression from Crowds

    DEFF Research Database (Denmark)

    Rodrigues, Filipe; Lourenco, Mariana; Ribeiro, Bernardete

    2017-01-01

    problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages...... annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression...

  5. Analysis of quantile regression as alternative to ordinary least squares

    OpenAIRE

    Ibrahim Abdullahi; Abubakar Yahaya

    2015-01-01

    In this article, an alternative to ordinary least squares (OLS) regression based on analytical solution in the Statgraphics software is considered, and this alternative is no other than quantile regression (QR) model. We also present goodness of fit statistic as well as approximate distributions of the associated test statistics for the parameters. Furthermore, we suggest a goodness of fit statistic called the least absolute deviation (LAD) coefficient of determination. The procedure is well ...

  6. Online Coaching of Emotion-Regulation Strategies for Parents: Efficacy of the Online Rational Positive Parenting Program and Attention Bias Modification Procedures.

    Science.gov (United States)

    David, Oana A; Capris, David; Jarda, Alexandra

    2017-01-01

    Parenting programs are currently treatment of choice for behavioral disorders in children and one of their main components is reducing the negativity bias in the child-parent dyad. The Rational Positive Parenting Program (rPPP) is a program with a special focus on parent emotion-regulation functional reappraisal strategies, which has recently received consistent support for reducing child externalizing and internalizing disorders. In the last years, online interventions were proliferated and the Attention Bias Modification (ABM) becoming a promising implicit therapeutic intervention based on attention deployment emotion-regulation strategy, or adjunctive module to usual treatments, with results in multiple domains, varying from pain to self-esteem and emotional disorders (e.g., anxiety). We conducted two studies to investigate (1) the efficacy of the ABM procedures applied to parents and (2) the efficacy of the online version of the rPPP augmented with an ABM module. A total of 42 parents of children aged 2-12 years old participated in the first study, being allocated either to the ABM training or wait-list. Positive results were reported by the parents participating in the ABM group for own distress, satisfaction, positive interactions with the child, and child's strengths. In the second study, 53 parents and their children were allocated either in the rPPP group or in the rPPP + ABM group. Results show that ABM training can boost the effects of the rPPP on the strengths of children reported by the parents after the intervention. Findings are discussed in the light of limited research on using online tools for coaching effective emotion-regulation strategies for parents.

  7. Temperature effects in exchange-biased planar Hall sensors for bioapplications

    DEFF Research Database (Denmark)

    Damsgaard, Christian Danvad; Dalslet, Bjarke Thomas; Freitas, S.C.

    2009-01-01

    The temperature dependence of exchange biased planar Hall effect sensors is investigated between T = −10 and 70 °C. It is shown that a single domain model describes the system well and that the temperature coefficient of the low-field sensitivity at T = 25 °C is 0.32%/°C. A procedure for temperat...

  8. The Application of Six Sigma Techniques in the Evaluation of Enzyme Measurement Procedures in China.

    Science.gov (United States)

    Zhang, Chuanbao; Zhao, Haijian; Wang, Jing; Zeng, Jie; Wang, Zhiguo

    2015-01-01

    Recently, Six Sigma techniques have been adopted by clinical laboratories to evaluate laboratory performance. Measurement procedures in laboratories can be categorized as "excellent", "good", and "improvement needed" based on sigma (σ) metrics of σ ≥ 6, 3 ≤ σ 1.2 indicates that the trueness of the procedure needs to be improved; 0.8 ≤ QGI ≤ 1.2 indicates that both the precision and trueness of the procedure need to be improved. Fresh frozen sera containing seven enzymes (ALT, ALP, AMY, AST, CK, GGT, and LDH) were sent to 78 clinical laboratories in China. The biases for measurement procedures in each laboratory (Bias) were calculated based on the target values assigned by 18 laboratories performing IFCC (International Federation of Clinical Chemistry and Laboratory medicine) recommended reference methods. The imprecision of each measurement procedure was represented by coefficient variations (CV) calculated based on internal quality control (IQC) data. The σ and QGI values were calculated as follows: σ = (TEa-Bias)/CV; QGI = Bias/(1.5 x CV). TEa is allowable total error for each enzyme derived from biological variation. Our study indicated that 7.9% (6/76, ALP) to 31.0% (18/58, AMY) of the participating laboratories were scored as "excellent" (σ ≥ 6), 21.1% (16/76, ALP) to 41.3% (31/75, CK) of the laboratories were scored as "good" (3 ≤ σ Six Sigma techniques still suggested that approximately 31.1% to 71.0% of the laboratories need to improve their enzyme measurement procedures, either in terms of precision, trueness, or both.

  9. A Classroom Demonstration of Potential Biases in the Subjective Interpretation of Projective Tests.

    Science.gov (United States)

    Wiederman, Michael W.

    1999-01-01

    Suggests that instructors teaching psychological assessment can use a demonstration to illustrate potential biases when subjectively interpreting response to projective stimuli. Outlines the classroom procedure, notes styles of learning involved, and presents a summary of student evaluations. (DSK)

  10. Music for a Brighter World: Brightness Judgment Bias by Musical Emotion.

    Science.gov (United States)

    Bhattacharya, Joydeep; Lindsen, Job P

    2016-01-01

    A prevalent conceptual metaphor is the association of the concepts of good and evil with brightness and darkness, respectively. Music cognition, like metaphor, is possibly embodied, yet no study has addressed the question whether musical emotion can modulate brightness judgment in a metaphor consistent fashion. In three separate experiments, participants judged the brightness of a grey square that was presented after a short excerpt of emotional music. The results of Experiment 1 showed that short musical excerpts are effective emotional primes that cross-modally influence brightness judgment of visual stimuli. Grey squares were consistently judged as brighter after listening to music with a positive valence, as compared to music with a negative valence. The results of Experiment 2 revealed that the bias in brightness judgment does not require an active evaluation of the emotional content of the music. By applying a different experimental procedure in Experiment 3, we showed that this brightness judgment bias is indeed a robust effect. Altogether, our findings demonstrate a powerful role of musical emotion in biasing brightness judgment and that this bias is aligned with the metaphor viewpoint.

  11. Stochastic development regression using method of moments

    DEFF Research Database (Denmark)

    Kühnel, Line; Sommer, Stefan Horst

    2017-01-01

    This paper considers the estimation problem arising when inferring parameters in the stochastic development regression model for manifold valued non-linear data. Stochastic development regression captures the relation between manifold-valued response and Euclidean covariate variables using...... the stochastic development construction. It is thereby able to incorporate several covariate variables and random effects. The model is intrinsically defined using the connection of the manifold, and the use of stochastic development avoids linearizing the geometry. We propose to infer parameters using...... the Method of Moments procedure that matches known constraints on moments of the observations conditional on the latent variables. The performance of the model is investigated in a simulation example using data on finite dimensional landmark manifolds....

  12. Understanding the bias between moisture content by oven drying and water content by Karl Fischer titration at moisture equilibrium

    Science.gov (United States)

    Multiple causes of the difference between equilibrium moisture and water content have been found. The errors or biases were traced to the oven drying procedure to determine moisture content. The present paper explains the nature of the biases in oven drying and how it is possible to suppress one ...

  13. Selection bias and the Rubin-Ford effect

    International Nuclear Information System (INIS)

    James, P.A.; Joseph, R.D.; Collins, C.A.

    1991-01-01

    We have re-examined the 'Rubin-Ford effect', and more recent claims of galaxy streaming from the same galaxy sample, to investigate the impact of selection effects on these results. A 'Monte Carlo'-type analysis was applied to simulate the selection procedure used to obtain this sample, and a strong bias was identified, resulting in apparent velocity flows at 600-800 km s -1 . Thus the 'Rubin-Ford effect' and the associated galaxy streaming are spurious effects resulting from the method of sample selection. (author)

  14. An interactive website for analytical method comparison and bias estimation.

    Science.gov (United States)

    Bahar, Burak; Tuncel, Ayse F; Holmes, Earle W; Holmes, Daniel T

    2017-12-01

    Regulatory standards mandate laboratories to perform studies to ensure accuracy and reliability of their test results. Method comparison and bias estimation are important components of these studies. We developed an interactive website for evaluating the relative performance of two analytical methods using R programming language tools. The website can be accessed at https://bahar.shinyapps.io/method_compare/. The site has an easy-to-use interface that allows both copy-pasting and manual entry of data. It also allows selection of a regression model and creation of regression and difference plots. Available regression models include Ordinary Least Squares, Weighted-Ordinary Least Squares, Deming, Weighted-Deming, Passing-Bablok and Passing-Bablok for large datasets. The server processes the data and generates downloadable reports in PDF or HTML format. Our website provides clinical laboratories a practical way to assess the relative performance of two analytical methods. Copyright © 2017 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

  15. truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models

    Directory of Open Access Journals (Sweden)

    Maria Karlsson

    2014-05-01

    Full Text Available Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and ?nite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.

  16. The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees

    DEFF Research Database (Denmark)

    Brunori, Paolo; Hufe, Paul; Mahler, Daniel Gerszon

    2017-01-01

    the risk of arbitrary and ad-hoc model selection. Second, they provide a standardized way of trading off upward and downward biases in inequality of opportunity estimations. Finally, regression trees can be graphically represented; their structure is immediate to read and easy to understand. This will make...... the measurement of inequality of opportunity more easily comprehensible to a large audience. These advantages are illustrated by an empirical application based on the 2011 wave of the European Union Statistics on Income and Living Conditions....

  17. Detecting DIF in Polytomous Items Using MACS, IRT and Ordinal Logistic Regression

    Science.gov (United States)

    Elosua, Paula; Wells, Craig

    2013-01-01

    The purpose of the present study was to compare the Type I error rate and power of two model-based procedures, the mean and covariance structure model (MACS) and the item response theory (IRT), and an observed-score based procedure, ordinal logistic regression, for detecting differential item functioning (DIF) in polytomous items. A simulation…

  18. An Approach to Remove the Systematic Bias from the Storm Surge forecasts in the Venice Lagoon

    Science.gov (United States)

    Canestrelli, A.

    2017-12-01

    In this work a novel approach is proposed for removing the systematic bias from the storm surge forecast computed by a two-dimensional shallow-water model. The model covers both the Adriatic and Mediterranean seas and provides the forecast at the entrance of the Venice Lagoon. The wind drag coefficient at the water-air interface is treated as a calibration parameter, with a different value for each range of wind velocities and wind directions. This sums up to a total of 16-64 parameters to be calibrated, depending on the chosen resolution. The best set of parameters is determined by means of an optimization procedure, which minimizes the RMS error between measured and modeled water level in Venice for the period 2011-2015. It is shown that a bias is present, for which the peaks of wind velocities provided by the weather forecast are largely underestimated, and that the calibration procedure removes this bias. When the calibrated model is used to reproduce events not included in the calibration dataset, the forecast error is strongly reduced, thus confirming the quality of our procedure. The proposed approach it is not site-specific and could be applied to different situations, such as storm surges caused by intense hurricanes.

  19. Prototypes and same-gender bias in perceptions of hiring discrimination.

    Science.gov (United States)

    Carlsson, Rickard; Sinclair, Samantha

    2018-01-01

    The present study investigated the relative importance of two explanations behind perceptions of gender discrimination in hiring: prototypes and same-gender bias. According to the prototype explanation, people perceive an event as discrimination to the extent that it fits their preconceptions of typical discrimination. In contrast, the same-gender bias explanation asserts that people more readily detect discrimination toward members of their own gender. In four experiments (n = 797), women and men made considerably stronger discrimination attributions, and were moderately more discouraged from seeking work, when the victim was female rather than male. Further, a series of regressions analyses showed beliefs in discrimination of women to be moderately correlated with discrimination attributions of female victims, but little added explanatory value of participant gender, stigma consciousness, or feminist identification. The results offer strong support for the prototype explanation.

  20. Methods for estimating disease transmission rates: Evaluating the precision of Poisson regression and two novel methods

    DEFF Research Database (Denmark)

    Kirkeby, Carsten Thure; Hisham Beshara Halasa, Tariq; Gussmann, Maya Katrin

    2017-01-01

    the transmission rate. We use data from the two simulation models and vary the sampling intervals and the size of the population sampled. We devise two new methods to determine transmission rate, and compare these to the frequently used Poisson regression method in both epidemic and endemic situations. For most...... tested scenarios these new methods perform similar or better than Poisson regression, especially in the case of long sampling intervals. We conclude that transmission rate estimates are easily biased, which is important to take into account when using these rates in simulation models....

  1. Parameter Estimation for Improving Association Indicators in Binary Logistic Regression

    Directory of Open Access Journals (Sweden)

    Mahdi Bashiri

    2012-02-01

    Full Text Available The aim of this paper is estimation of Binary logistic regression parameters for maximizing the log-likelihood function with improved association indicators. In this paper the parameter estimation steps have been explained and then measures of association have been introduced and their calculations have been analyzed. Moreover a new related indicators based on membership degree level have been expressed. Indeed association measures demonstrate the number of success responses occurred in front of failure in certain number of Bernoulli independent experiments. In parameter estimation, existing indicators values is not sensitive to the parameter values, whereas the proposed indicators are sensitive to the estimated parameters during the iterative procedure. Therefore, proposing a new association indicator of binary logistic regression with more sensitivity to the estimated parameters in maximizing the log- likelihood in iterative procedure is innovation of this study.

  2. Racial, gender, and socioeconomic status bias in senior medical student clinical decision-making: a national survey.

    Science.gov (United States)

    Williams, Robert L; Romney, Crystal; Kano, Miria; Wright, Randy; Skipper, Betty; Getrich, Christina M; Sussman, Andrew L; Zyzanski, Stephen J

    2015-06-01

    Research suggests stereotyping by clinicians as one contributor to racial and gender-based health disparities. It is necessary to understand the origins of such biases before interventions can be developed to eliminate them. As a first step toward this understanding, we tested for the presence of bias in senior medical students. The purpose of the study was to determine whether bias based on race, gender, or socioeconomic status influenced clinical decision-making among medical students. We surveyed seniors at 84 medical schools, who were required to choose between two clinically equivalent management options for a set of cardiac patient vignettes. We examined variations in student recommendations based on patient race, gender, and socioeconomic status. The study included senior medical students. We investigated the percentage of students selecting cardiac procedural options for vignette patients, analyzed by patient race, gender, and socioeconomic status. Among 4,603 returned surveys, we found no evidence in the overall sample supporting racial or gender bias in student clinical decision-making. Students were slightly more likely to recommend cardiac procedural options for black (43.9 %) vs. white (42 %, p = .03) patients; there was no difference by patient gender. Patient socioeconomic status was the strongest predictor of student recommendations, with patients described as having the highest socioeconomic status most likely to receive procedural care recommendations (50.3 % vs. 43.2 % for those in the lowest socioeconomic status group, p socioeconomic status, geographic variations, and the influence of interactions between patient race and gender on student recommendations.

  3. Journal bias or author bias?

    Science.gov (United States)

    Harris, Ian

    2016-01-01

    I read with interest the comment by Mark Wilson in the Indian Journal of Medical Ethics regarding bias and conflicts of interest in medical journals. Wilson targets one journal (the New England Journal of Medicine: NEJM) and one particular "scandal" to make his point that journals' decisions on publication are biased by commercial conflicts of interest (CoIs). It is interesting that he chooses the NEJM which, by his own admission, had one of the strictest CoI policies and had published widely on this topic. The feeling is that if the NEJM can be guilty, they can all be guilty.

  4. Modeling Governance KB with CATPCA to Overcome Multicollinearity in the Logistic Regression

    Science.gov (United States)

    Khikmah, L.; Wijayanto, H.; Syafitri, U. D.

    2017-04-01

    The problem often encounters in logistic regression modeling are multicollinearity problems. Data that have multicollinearity between explanatory variables with the result in the estimation of parameters to be bias. Besides, the multicollinearity will result in error in the classification. In general, to overcome multicollinearity in regression used stepwise regression. They are also another method to overcome multicollinearity which involves all variable for prediction. That is Principal Component Analysis (PCA). However, classical PCA in only for numeric data. Its data are categorical, one method to solve the problems is Categorical Principal Component Analysis (CATPCA). Data were used in this research were a part of data Demographic and Population Survey Indonesia (IDHS) 2012. This research focuses on the characteristic of women of using the contraceptive methods. Classification results evaluated using Area Under Curve (AUC) values. The higher the AUC value, the better. Based on AUC values, the classification of the contraceptive method using stepwise method (58.66%) is better than the logistic regression model (57.39%) and CATPCA (57.39%). Evaluation of the results of logistic regression using sensitivity, shows the opposite where CATPCA method (99.79%) is better than logistic regression method (92.43%) and stepwise (92.05%). Therefore in this study focuses on major class classification (using a contraceptive method), then the selected model is CATPCA because it can raise the level of the major class model accuracy.

  5. Graphical evaluation of the ridge-type robust regression estimators in mixture experiments.

    Science.gov (United States)

    Erkoc, Ali; Emiroglu, Esra; Akay, Kadri Ulas

    2014-01-01

    In mixture experiments, estimation of the parameters is generally based on ordinary least squares (OLS). However, in the presence of multicollinearity and outliers, OLS can result in very poor estimates. In this case, effects due to the combined outlier-multicollinearity problem can be reduced to certain extent by using alternative approaches. One of these approaches is to use biased-robust regression techniques for the estimation of parameters. In this paper, we evaluate various ridge-type robust estimators in the cases where there are multicollinearity and outliers during the analysis of mixture experiments. Also, for selection of biasing parameter, we use fraction of design space plots for evaluating the effect of the ridge-type robust estimators with respect to the scaled mean squared error of prediction. The suggested graphical approach is illustrated on Hald cement data set.

  6. 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.

  7. Notes on power of normality tests of error terms in regression models

    International Nuclear Information System (INIS)

    Střelec, Luboš

    2015-01-01

    Normality is one of the basic assumptions in applying statistical procedures. For example in linear regression most of the inferential procedures are based on the assumption of normality, i.e. the disturbance vector is assumed to be normally distributed. Failure to assess non-normality of the error terms may lead to incorrect results of usual statistical inference techniques such as t-test or F-test. Thus, error terms should be normally distributed in order to allow us to make exact inferences. As a consequence, normally distributed stochastic errors are necessary in order to make a not misleading inferences which explains a necessity and importance of robust tests of normality. Therefore, the aim of this contribution is to discuss normality testing of error terms in regression models. In this contribution, we introduce the general RT class of robust tests for normality, and present and discuss the trade-off between power and robustness of selected classical and robust normality tests of error terms in regression models

  8. Notes on power of normality tests of error terms in regression models

    Energy Technology Data Exchange (ETDEWEB)

    Střelec, Luboš [Department of Statistics and Operation Analysis, Faculty of Business and Economics, Mendel University in Brno, Zemědělská 1, Brno, 61300 (Czech Republic)

    2015-03-10

    Normality is one of the basic assumptions in applying statistical procedures. For example in linear regression most of the inferential procedures are based on the assumption of normality, i.e. the disturbance vector is assumed to be normally distributed. Failure to assess non-normality of the error terms may lead to incorrect results of usual statistical inference techniques such as t-test or F-test. Thus, error terms should be normally distributed in order to allow us to make exact inferences. As a consequence, normally distributed stochastic errors are necessary in order to make a not misleading inferences which explains a necessity and importance of robust tests of normality. Therefore, the aim of this contribution is to discuss normality testing of error terms in regression models. In this contribution, we introduce the general RT class of robust tests for normality, and present and discuss the trade-off between power and robustness of selected classical and robust normality tests of error terms in regression models.

  9. Beyond assembly bias: exploring secondary halo biases for cluster-size haloes

    Science.gov (United States)

    Mao, Yao-Yuan; Zentner, Andrew R.; Wechsler, Risa H.

    2018-03-01

    Secondary halo bias, commonly known as `assembly bias', is the dependence of halo clustering on a halo property other than mass. This prediction of the Λ Cold Dark Matter cosmology is essential to modelling the galaxy distribution to high precision and interpreting clustering measurements. As the name suggests, different manifestations of secondary halo bias have been thought to originate from halo assembly histories. We show conclusively that this is incorrect for cluster-size haloes. We present an up-to-date summary of secondary halo biases of high-mass haloes due to various halo properties including concentration, spin, several proxies of assembly history, and subhalo properties. While concentration, spin, and the abundance and radial distribution of subhaloes exhibit significant secondary biases, properties that directly quantify halo assembly history do not. In fact, the entire assembly histories of haloes in pairs are nearly identical to those of isolated haloes. In general, a global correlation between two halo properties does not predict whether or not these two properties exhibit similar secondary biases. For example, assembly history and concentration (or subhalo abundance) are correlated for both paired and isolated haloes, but follow slightly different conditional distributions in these two cases. This results in a secondary halo bias due to concentration (or subhalo abundance), despite the lack of assembly bias in the strict sense for cluster-size haloes. Due to this complexity, caution must be exercised in using any one halo property as a proxy to study the secondary bias due to another property.

  10. Infrared Resummation for Biased Tracers in Redshift Space arXiv

    CERN Document Server

    Ivanov, Mikhail M.

    We incorporate the effects of redshift space distortions and non-linear bias in time-sliced perturbation theory (TSPT). This is done via a new method that allows to map cosmological correlation functions from real to redshift space. This mapping preserves a transparent infrared (IR) structure of the theory and provides us with an efficient tool to study non-linear infrared effects altering the pattern of baryon acoustic oscillations (BAO) in redshift space. We give an accurate description of the BAO by means of a systematic resummation of Feynman diagrams guided by well-defined power counting rules. This establishes IR resummation within TSPT as a robust and complete procedure and provides a consistent theoretical model for the BAO feature in the statistics of biased tracers in redshift space.

  11. Heuristics and bias in rectal surgery.

    Science.gov (United States)

    MacDermid, Ewan; Young, Christopher J; Moug, Susan J; Anderson, Robert G; Shepherd, Heather L

    2017-08-01

    Deciding to defunction after anterior resection can be difficult, requiring cognitive tools or heuristics. From our previous work, increasing age and risk-taking propensity were identified as heuristic biases for surgeons in Australia and New Zealand (CSSANZ), and inversely proportional to the likelihood of creating defunctioning stomas. We aimed to assess these factors for colorectal surgeons in the British Isles, and identify other potential biases. The Association of Coloproctology of Great Britain and Ireland (ACPGBI) was invited to complete an online survey. Questions included demographics, risk-taking propensity, sensitivity to professional criticism, self-perception of anastomotic leak rate and propensity for creating defunctioning stomas. Chi-squared testing was used to assess differences between ACPGBI and CSSANZ respondents. Multiple regression analysis identified independent surgeon predictors of stoma formation. One hundred fifty (19.2%) eligible members of the ACPGBI replied. Demographics between ACPGBI and CSSANZ groups were well-matched. Significantly more ACPGBI surgeons admitted to anastomotic leak in the last year (p < 0.001). ACPGBI surgeon age over 50 (p = 0.02), higher risk-taking propensity across several domains (p = 0.044), self-belief in a lower-than-average anastomotic leak rate (p = 0.02) and belief that the average risk of leak after anterior resection is 8% or lower (p = 0.007) were all independent predictors of less frequent stoma formation. Sensitivity to criticism from colleagues was not a predictor of stoma formation. Unrecognised surgeon factors including age, everyday risk-taking, self-belief in surgical ability and lower probability bias of anastomotic leak appear to exert an effect on decision-making in rectal surgery.

  12. The Effectiveness of Cognitive Bias Modification Interventions for Substance Addictions: A Meta-Analysis.

    Directory of Open Access Journals (Sweden)

    Ioana A Cristea

    Full Text Available Cognitive bias modification (CBM interventions, presumably targeting automatic processes, are considered particularly promising for addictions. We conducted a meta-analysis examining randomized controlled trials (RCTs of CBM for substance addiction outcomes.Studies were identified through systematic searches in bibliographical databases. We included RCTs of CBM interventions, alone or in combination with other treatments, for any type of addiction. We examined trial risk of bias, publication bias and possible moderators. Effects sizes were computed for post-test and follow-up, using a random-effects model. We grouped outcome measures and reported results for addiction (all related measures, craving and cognitive bias.We identified 25 trials, 18 for alcohol problems, and 7 for smoking. At post-test, there was no significant effect of CBM for addiction, g = 0.08 (95% CI -0.02 to 0.18 or craving, g = 0.05 (95% CI -0.06 to 0.16, but there was a significant, moderate effect on cognitive bias, g = 0.60 (95% CI 0.39 to 0.79. Results were similar for alcohol and smoking outcomes taken separately. Follow-up addiction outcomes were reported in 7 trials, resulting in a small but significant effect of CBM, g = 0.18 (95% CI 0.03 to 0.32. Results for addiction and craving did not differ by substance type, sample type, delivery setting, bias targeted or number of sessions. Risk of bias was high or uncertain in most trials, for most criteria considered. Meta-regression analyses revealed significant inverse relationships between risk of bias and effect sizes for addiction outcomes and craving. The relationship between cognitive bias and respectively addiction ESs was not significant. There was consistent evidence of publication bias in the form of funnel plot asymmetry.Our results cast serious doubts on the clinical utility of CBM interventions for addiction problems, but sounder methodological trials are necessary before this issue can be settled. We found no

  13. Music for a Brighter World: Brightness Judgment Bias by Musical Emotion.

    Directory of Open Access Journals (Sweden)

    Joydeep Bhattacharya

    Full Text Available A prevalent conceptual metaphor is the association of the concepts of good and evil with brightness and darkness, respectively. Music cognition, like metaphor, is possibly embodied, yet no study has addressed the question whether musical emotion can modulate brightness judgment in a metaphor consistent fashion. In three separate experiments, participants judged the brightness of a grey square that was presented after a short excerpt of emotional music. The results of Experiment 1 showed that short musical excerpts are effective emotional primes that cross-modally influence brightness judgment of visual stimuli. Grey squares were consistently judged as brighter after listening to music with a positive valence, as compared to music with a negative valence. The results of Experiment 2 revealed that the bias in brightness judgment does not require an active evaluation of the emotional content of the music. By applying a different experimental procedure in Experiment 3, we showed that this brightness judgment bias is indeed a robust effect. Altogether, our findings demonstrate a powerful role of musical emotion in biasing brightness judgment and that this bias is aligned with the metaphor viewpoint.

  14. 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

    by a lumped or smoothed m-dimensional approximation γθ*, where γ is an interpolation matrix and θ* is a stochastic vector of parameters. Vector θ* has small enough dimension to allow its estimation with the available data. The consequence of the replacement is that model function f(γθ*) written in terms......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 β that reflects both small and large scales of heterogeneity in the inputs...... small. Model error is accounted for in the weighted nonlinear regression methodology developed to estimate θ* 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...

  15. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

    Science.gov (United States)

    Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg

    2009-11-01

    G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

  16. Evaluating disease management programme effectiveness: an introduction to the regression discontinuity design.

    Science.gov (United States)

    Linden, Ariel; Adams, John L; Roberts, Nancy

    2006-04-01

    Although disease management (DM) has been in existence for over a decade, there is still much uncertainty as to its effectiveness in improving health status and reducing medical cost. The main reason is that most programme evaluations typically follow weak observational study designs that are subject to bias, most notably selection bias and regression to the mean. The regression discontinuity (RD) design may be the best alternative to randomized studies for evaluating DM programme effectiveness. The most crucial element of the RD design is its use of a 'cut-off' score on a pre-test measure to determine assignment to intervention or control. A valuable feature of this technique is that the pre-test measure does not have to be the same as the outcome measure, thus maximizing the programme's ability to use research-based practice guidelines, survey instruments and other tools to identify those individuals in greatest need of the programme intervention. Similarly, the cut-off score can be based on clinical understanding of the disease process, empirically derived, or resource-based. In the RD design, programme effectiveness is determined by a change in the pre-post relationship at the cut-off point. While the RD design is uniquely suitable for DM programme evaluation, its success will depend, in large part, on fundamental changes being made in the way DM programmes identify and assign individuals to the programme intervention.

  17. A simultaneous confidence band for sparse longitudinal regression

    KAUST Repository

    Ma, Shujie; Yang, Lijian; Carroll, Raymond J.

    2012-01-01

    Functional data analysis has received considerable recent attention and a number of successful applications have been reported. In this paper, asymptotically simultaneous confidence bands are obtained for the mean function of the functional regression model, using piecewise constant spline estimation. Simulation experiments corroborate the asymptotic theory. The confidence band procedure is illustrated by analyzing CD4 cell counts of HIV infected patients.

  18. Estimating the Counterfactual Impact of Conservation Programs on Land Cover Outcomes: The Role of Matching and Panel Regression Techniques.

    Science.gov (United States)

    Jones, Kelly W; Lewis, David J

    2015-01-01

    Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented--from protected areas to payments for ecosystem services (PES)--to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing 'matching' to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods--an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1) matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2) fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators--due to the presence of unobservable bias--that lead to differences in conclusions about effectiveness. The Ecuador case illustrates that

  19. Estimating the Counterfactual Impact of Conservation Programs on Land Cover Outcomes: The Role of Matching and Panel Regression Techniques.

    Directory of Open Access Journals (Sweden)

    Kelly W Jones

    Full Text Available Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented--from protected areas to payments for ecosystem services (PES--to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing 'matching' to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods--an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1 matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2 fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators--due to the presence of unobservable bias--that lead to differences in conclusions about effectiveness. The Ecuador case

  20. CPI Bias in Korea

    Directory of Open Access Journals (Sweden)

    Chul Chung

    2007-12-01

    Full Text Available We estimate the CPI bias in Korea by employing the approach of Engel’s Law as suggested by Hamilton (2001. This paper is the first attempt to estimate the bias using Korean panel data, Korean Labor and Income Panel Study(KLIPS. Following Hamilton’s model with non­linear specification correction, our estimation result shows that the cumulative CPI bias over the sample period (2000-2005 was 0.7 percent annually. This CPI bias implies that about 21 percent of the inflation rate during the period can be attributed to the bias. In light of purchasing power parity, we provide an interpretation of the estimated bias.

  1. Decomposing Wage Distributions Using Recentered Influence Function Regressions

    Directory of Open Access Journals (Sweden)

    Sergio P. Firpo

    2018-05-01

    Full Text Available This paper provides a detailed exposition of an extension of the Oaxaca-Blinder decomposition method that can be applied to various distributional measures. The two-stage procedure first divides distributional changes into a wage structure effect and a composition effect using a reweighting method. Second, the two components are further divided into the contribution of each explanatory variable using recentered influence function (RIF regressions. We illustrate the practical aspects of the procedure by analyzing how the polarization of U.S. male wages between the late 1980s and the mid 2010s was affected by factors such as de-unionization, education, occupations, and industry changes.

  2. Studying Gender Bias in Physics Grading: The role of teaching experience and country

    Science.gov (United States)

    Hofer, Sarah I.

    2015-11-01

    The existence of gender-STEM (science, technology, engineering, and mathematics) stereotypes has been repeatedly documented. This article examines physics teachers' gender bias in grading and the influence of teaching experience in Switzerland, Austria, and Germany. In a 2 × 2 between-subjects design, with years of teaching experience included as moderating variable, physics teachers (N = 780) from Switzerland, Austria, and Germany graded a fictive student's answer to a physics test question. While the answer was exactly the same for each teacher, only the student's gender and specialization in languages vs. science were manipulated. Specialization was included to gauge the relative strength of potential gender bias effects. Multiple group regression analyses, with the grade that was awarded as the dependent variable, revealed only partial cross-border generalizability of the effect pattern. While the overall results in fact indicated the existence of a consistent and clear gender bias against girls in the first part of physics teachers' careers that disappeared with increasing teaching experience for Swiss teachers, Austrian teachers, and German female teachers, German male teachers showed no gender bias effects at all. The results are discussed regarding their relevance for educational practice and research.

  3. Confidence bands for inverse regression models

    International Nuclear Information System (INIS)

    Birke, Melanie; Bissantz, Nicolai; Holzmann, Hajo

    2010-01-01

    We construct uniform confidence bands for the regression function in inverse, homoscedastic regression models with convolution-type operators. Here, the convolution is between two non-periodic functions on the whole real line rather than between two periodic functions on a compact interval, since the former situation arguably arises more often in applications. First, following Bickel and Rosenblatt (1973 Ann. Stat. 1 1071–95) we construct asymptotic confidence bands which are based on strong approximations and on a limit theorem for the supremum of a stationary Gaussian process. Further, we propose bootstrap confidence bands based on the residual bootstrap and prove consistency of the bootstrap procedure. A simulation study shows that the bootstrap confidence bands perform reasonably well for moderate sample sizes. Finally, we apply our method to data from a gel electrophoresis experiment with genetically engineered neuronal receptor subunits incubated with rat brain extract

  4. A new approach of optimization procedure for superconducting integrated circuits

    International Nuclear Information System (INIS)

    Saitoh, K.; Soutome, Y.; Tarutani, Y.; Takagi, K.

    1999-01-01

    We have developed and tested a new circuit simulation procedure for superconducting integrated circuits which can be used to optimize circuit parameters. This method reveals a stable operation region in the circuit parameter space in connection with the global bias margin by means of a contour plot of the global bias margin versus the circuit parameters. An optimal set of parameters with margins larger than these of the initial values has been found in the stable region. (author)

  5. Approximate Bias Correction in Econometrics

    OpenAIRE

    James G. MacKinnon; Anthony A. Smith Jr.

    1995-01-01

    This paper discusses ways to reduce the bias of consistent estimators that are biased in finite samples. It is necessary that the bias function, which relates parameter values to bias, should be estimable by computer simulation or by some other method. If so, bias can be reduced or, in some cases that may not be unrealistic, even eliminated. In general, several evaluations of the bias function will be required to do this. Unfortunately, reducing bias may increase the variance, or even the mea...

  6. Logistic regression for risk factor modelling in stuttering research.

    Science.gov (United States)

    Reed, Phil; Wu, Yaqionq

    2013-06-01

    To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.

  7. Robust inference in the negative binomial regression model with an application to falls data.

    Science.gov (United States)

    Aeberhard, William H; Cantoni, Eva; Heritier, Stephane

    2014-12-01

    A popular way to model overdispersed count data, such as the number of falls reported during intervention studies, is by means of the negative binomial (NB) distribution. Classical estimating methods are well-known to be sensitive to model misspecifications, taking the form of patients falling much more than expected in such intervention studies where the NB regression model is used. We extend in this article two approaches for building robust M-estimators of the regression parameters in the class of generalized linear models to the NB distribution. The first approach achieves robustness in the response by applying a bounded function on the Pearson residuals arising in the maximum likelihood estimating equations, while the second approach achieves robustness by bounding the unscaled deviance components. For both approaches, we explore different choices for the bounding functions. Through a unified notation, we show how close these approaches may actually be as long as the bounding functions are chosen and tuned appropriately, and provide the asymptotic distributions of the resulting estimators. Moreover, we introduce a robust weighted maximum likelihood estimator for the overdispersion parameter, specific to the NB distribution. Simulations under various settings show that redescending bounding functions yield estimates with smaller biases under contamination while keeping high efficiency at the assumed model, and this for both approaches. We present an application to a recent randomized controlled trial measuring the effectiveness of an exercise program at reducing the number of falls among people suffering from Parkinsons disease to illustrate the diagnostic use of such robust procedures and their need for reliable inference. © 2014, The International Biometric Society.

  8. Interpreting trial results following use of different intention-to-treat approaches for preventing attrition bias: a meta-epidemiological study protocol

    OpenAIRE

    Dossing, Anna; Tarp, Simon; Furst, Daniel E; Gluud, Christian; Beyene, Joseph; Hansen, Bjarke B; Bliddal, Henning; Christensen, Robin

    2014-01-01

    Introduction When participants drop out of randomised clinical trials, as frequently happens, the intention-to-treat (ITT) principle does not apply, potentially leading to attrition bias. Data lost from patient dropout/lack of follow-up are statistically addressed by imputing, a procedure prone to bias. Deviations from the original definition of ITT are referred to as modified intention-to-treat (mITT). As yet, the impact of the potential bias associated with mITT has not been assessed. Our o...

  9. Estimating the exceedance probability of rain rate by logistic regression

    Science.gov (United States)

    Chiu, Long S.; Kedem, Benjamin

    1990-01-01

    Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.

  10. Attention bias modification in specific fears: Spiders versus snakes.

    Science.gov (United States)

    Luo, Xijia; Ikani, Nessa; Barth, Anja; Rengers, Lea; Becker, Eni; Rinck, Mike

    2015-12-01

    Attention Bias Modification (ABM) is used to manipulate attention biases in anxiety disorders. It has been successful in reducing attention biases and anxious symptoms in social anxiety and generalized anxiety, but not yet in specific fears and phobias. We designed a new version of the dot-probe training task, aiming to train fearful participants' attention away from or towards pictures of threatening stimuli. Moreover, we studied whether the training also affected participants' avoidance behavior and their physical arousal upon being confronted with a real threat object. In Experiment 1, students with fear of spiders were trained. We found that the attention manipulation was successful, but the training failed to affect behavior or arousal. In Experiment 2, the same procedure was used on snake-fearful students. Again, attention was trained in the expected directions. Moreover, participants whose attention had been trained away from snakes showed lower physiological arousal upon being confronted with a real snake. The study involved healthy students with normal distribution of the fear of spider/snake. Future research with clinical sample could help with determining the generalizability of the current findings. The effect of ABM on specific phobia is still in question. The finding in the present study suggested the possibility to alter attentional bias with a dot-probe task with general positive stimuli and this training could even affect the behavior while encountering a real threat. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. Detection of Outliers in Regression Model for Medical Data

    Directory of Open Access Journals (Sweden)

    Stephen Raj S

    2017-07-01

    Full Text Available In regression analysis, an outlier is an observation for which the residual is large in magnitude compared to other observations in the data set. The detection of outliers and influential points is an important step of the regression analysis. Outlier detection methods have been used to detect and remove anomalous values from data. In this paper, we detect the presence of outliers in simple linear regression models for medical data set. Chatterjee and Hadi mentioned that the ordinary residuals are not appropriate for diagnostic purposes; a transformed version of them is preferable. First, we investigate the presence of outliers based on existing procedures of residuals and standardized residuals. Next, we have used the new approach of standardized scores for detecting outliers without the use of predicted values. The performance of the new approach was verified with the real-life data.

  12. Dual Regression

    OpenAIRE

    Spady, Richard; Stouli, Sami

    2012-01-01

    We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...

  13. Regression assumptions in clinical psychology research practice-a systematic review of common misconceptions.

    Science.gov (United States)

    Ernst, Anja F; Albers, Casper J

    2017-01-01

    Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking.

  14. Beyond attentional bias: a perceptual bias in a dot-probe task.

    Science.gov (United States)

    Bocanegra, Bruno R; Huijding, Jorg; Zeelenberg, René

    2012-12-01

    Previous dot-probe studies indicate that threat-related face cues induce a bias in spatial attention. Independently of spatial attention, a recent psychophysical study suggests that a bilateral fearful face cue improves low spatial-frequency perception (LSF) and impairs high spatial-frequency perception (HSF). Here, we combine these separate lines of research within a single dot-probe paradigm. We found that a bilateral fearful face cue, compared with a bilateral neutral face cue, speeded up responses to LSF targets and slowed down responses to HSF targets. This finding is important, as it shows that emotional cues in dot-probe tasks not only bias where information is preferentially processed (i.e., an attentional bias in spatial location), but also bias what type of information is preferentially processed (i.e., a perceptual bias in spatial frequency). PsycINFO Database Record (c) 2012 APA, all rights reserved.

  15. Statistical learning from a regression perspective

    CERN Document Server

    Berk, Richard A

    2016-01-01

    This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be trea...

  16. A study on investors’ personality characteristics and behavioral biases: Conservatism bias and availability bias in the Tehran Stock Exchange

    Directory of Open Access Journals (Sweden)

    Mahmoud Moradi

    2013-04-01

    Full Text Available Most economic and finance theories are based on the assumption that during economic decision making, people would act totally rational and consider all available information. Nevertheless, behavioral finance focuses on studying of the role of psychological factors on economic participants’ behavior. The study shows that in real-world environment, people are influenced by emotional and cognitive errors and may make irrational financial decisions. In many cases, the participants of financial markets are not aware of their talents for error in decision making, so they are dissatisfied with their investments by considering some behavioral biases decisions. These decisions may often yield undesirable outcomes, which could influence economy, significantly. This paper presents a survey on the relationship between personality dimensions with behavioral biases and availability bias among investment managers in the Tehran Stock Exchange using SPSS software, descriptive and inferential statistics. The necessary data are collected through questionnaire and they are analyzed using some statistical tests. The preliminary results indicate that there is a relationship between personality dimensions and behavioral biases like conservatism bias and availability bias among the investors in the Tehran Stock Exchange.

  17. Media bias under direct and indirect government control: when is the bias smaller?

    OpenAIRE

    Abhra Roy

    2015-01-01

    We present an analytical framework to compare media bias under direct and indirect government control. In this context, we show that direct control can lead to a smaller bias and higher welfare than indirect control. We further show that the size of the advertising market affects media bias only under direct control. Media bias, under indirect control, is not affected by the size of the advertising market.

  18. A robust ridge regression approach in the presence of both multicollinearity and outliers in the data

    Science.gov (United States)

    Shariff, Nurul Sima Mohamad; Ferdaos, Nur Aqilah

    2017-08-01

    Multicollinearity often leads to inconsistent and unreliable parameter estimates in regression analysis. This situation will be more severe in the presence of outliers it will cause fatter tails in the error distributions than the normal distributions. The well-known procedure that is robust to multicollinearity problem is the ridge regression method. This method however is expected to be affected by the presence of outliers due to some assumptions imposed in the modeling procedure. Thus, the robust version of existing ridge method with some modification in the inverse matrix and the estimated response value is introduced. The performance of the proposed method is discussed and comparisons are made with several existing estimators namely, Ordinary Least Squares (OLS), ridge regression and robust ridge regression based on GM-estimates. The finding of this study is able to produce reliable parameter estimates in the presence of both multicollinearity and outliers in the data.

  19. Bias in Rating of Rodent Distress during Anesthesia Induction for Anesthesia Compared with Euthanasia.

    Science.gov (United States)

    Baker, Brittany A; Hickman, Debra L

    2018-03-01

    Selection of an appropriate method of euthanasia involves balancing the wellbeing of the animal during the procedure with the intended use of the animal after death and the physical and psychologic safety of the observer or operator. The recommended practices for anesthesia as compared with euthanasia are very disparate, despite the fact that all chemical methods of euthanasia are anesthetic overdoses. To explain this disparity, this study sought to determine whether perception bias is inherent in the discussion of euthanasia compared with anesthesia. In this study, participants viewed videorecordings of the anesthesia of either 4 rats or 4 mice, from induction to loss of consciousness. Half of the participants were told that they were observing anesthesia; the other half understood that they were observing euthanasia. Participants were asked to rate the distress of the animals by scoring escape behaviors, fear behaviors, respiratory distress, and other distress markers. For mice, the participants generally rated the distress as high when they were told that the mouse was being euthanized, as compared with the participants who were told that the mouse was being anesthetized. For rats, the effect was not as strong, and the distress was generally rated higher when participants were told they were watching anesthesia. Because the interpretation of distress showed bias in both species-even though the bias differed regarding the procedure that interpreted as distressing-this study demonstrates that laboratory animal professionals must consider the influence of potential perception bias when developing policies for euthanasia and anesthesia.

  20. Large-scale galaxy bias

    Science.gov (United States)

    Desjacques, Vincent; Jeong, Donghui; Schmidt, Fabian

    2018-02-01

    This review presents a comprehensive overview of galaxy bias, that is, the statistical relation between the distribution of galaxies and matter. We focus on large scales where cosmic density fields are quasi-linear. On these scales, the clustering of galaxies can be described by a perturbative bias expansion, and the complicated physics of galaxy formation is absorbed by a finite set of coefficients of the expansion, called bias parameters. The review begins with a detailed derivation of this very important result, which forms the basis of the rigorous perturbative description of galaxy clustering, under the assumptions of General Relativity and Gaussian, adiabatic initial conditions. Key components of the bias expansion are all leading local gravitational observables, which include the matter density but also tidal fields and their time derivatives. We hence expand the definition of local bias to encompass all these contributions. This derivation is followed by a presentation of the peak-background split in its general form, which elucidates the physical meaning of the bias parameters, and a detailed description of the connection between bias parameters and galaxy statistics. We then review the excursion-set formalism and peak theory which provide predictions for the values of the bias parameters. In the remainder of the review, we consider the generalizations of galaxy bias required in the presence of various types of cosmological physics that go beyond pressureless matter with adiabatic, Gaussian initial conditions: primordial non-Gaussianity, massive neutrinos, baryon-CDM isocurvature perturbations, dark energy, and modified gravity. Finally, we discuss how the description of galaxy bias in the galaxies' rest frame is related to clustering statistics measured from the observed angular positions and redshifts in actual galaxy catalogs.

  1. Magnitude conversion to unified moment magnitude using orthogonal regression relation

    Science.gov (United States)

    Das, Ranjit; Wason, H. R.; Sharma, M. L.

    2012-05-01

    Homogenization of earthquake catalog being a pre-requisite for seismic hazard assessment requires region based magnitude conversion relationships. Linear Standard Regression (SR) relations fail when both the magnitudes have measurement errors. To accomplish homogenization, techniques like Orthogonal Standard Regression (OSR) are thus used. In this paper a technique is proposed for using such OSR for preparation of homogenized earthquake catalog in moment magnitude Mw. For derivation of orthogonal regression relation between mb and Mw, a data set consisting of 171 events with observed body wave magnitudes (mb,obs) and moment magnitude (Mw,obs) values has been taken from ISC and GCMT databases for Northeast India and adjoining region for the period 1978-2006. Firstly, an OSR relation given below has been developed using mb,obs and Mw,obs values corresponding to 150 events from this data set. M=1.3(±0.004)m-1.4(±0.130), where mb,proxy are body wave magnitude values of the points on the OSR line given by the orthogonality criterion, for observed (mb,obs, Mw,obs) points. A linear relation is then developed between these 150 mb,obs values and corresponding mb,proxy values given by the OSR line using orthogonality criterion. The relation obtained is m=0.878(±0.03)m+0.653(±0.15). The accuracy of the above procedure has been checked with the rest of the data i.e., 21 events values. The improvement in the correlation coefficient value between mb,obs and Mw estimated using the proposed procedure compared to the correlation coefficient value between mb,obs and Mw,obs shows the advantage of OSR relationship for homogenization. The OSR procedure developed in this study can be used to homogenize any catalog containing various magnitudes (e.g., ML, mb, MS) with measurement errors, by their conversion to unified moment magnitude Mw. The proposed procedure also remains valid in case the magnitudes have measurement errors of different orders, i.e. the error variance ratio is

  2. Indirect assessment of an interpretation bias in humans: Neurophysiological and behavioral correlates

    Directory of Open Access Journals (Sweden)

    Anita eSchick

    2013-06-01

    Full Text Available Affective state can influence cognition leading to biased information processing, interpretation, attention, and memory. Such bias has been reported to be essential for the onset and maintenance of different psychopathologies, particularly affective disorders. However, empirical evidence has been very heterogeneous and little is known about the neurophysiological mechanisms underlying cognitive bias and its time-course. We therefore investigated the interpretation of ambiguous stimuli as indicators of biased information processing with an ambiguous cue-conditioning paradigm. In an acquisition phase, participants learned to discriminate two tones of different frequency, which acquired emotional and motivational value due to subsequent feedback (monetary gain or avoidance of monetary loss. In the test phase, three additional tones of intermediate frequencies were presented, whose interpretation as positive (approach of reward or negative (avoidance of punishment, indicated by a button press, was used as an indicator of the bias. Twenty healthy volunteers participated in this paradigm while a 64-channel electroencephalogram was recorded. Participants also completed questionnaires assessing individual differences in depression and rumination. Overall, we found a small positive bias, which correlated negatively with reflective pondering, a type of rumination. As expected, reaction times were increased for intermediate tones. ERP amplitudes between 300 – 700 ms post-stimulus differed depending on the interpretation of the intermediate tones. A negative compared to a positive interpretation led to an amplitude increase over frontal electrodes. Our study provides evidence that in humans, as in animal research, the ambiguous cue-conditioning paradigm is a valid procedure for indirectly assessing ambiguous cue interpretation and a potential interpretation bias, which is sensitive to individual differences in affect-related traits.

  3. Statistical methods in regression and calibration analysis of chromosome aberration data

    International Nuclear Information System (INIS)

    Merkle, W.

    1983-01-01

    The method of iteratively reweighted least squares for the regression analysis of Poisson distributed chromosome aberration data is reviewed in the context of other fit procedures used in the cytogenetic literature. As an application of the resulting regression curves methods for calculating confidence intervals on dose from aberration yield are described and compared, and, for the linear quadratic model a confidence interval is given. Emphasis is placed on the rational interpretation and the limitations of various methods from a statistical point of view. (orig./MG)

  4. Experimental modification of interpretation bias about animal fear in young children: effects on cognition, avoidance behavior, anxiety vulnerability, and physiological responding.

    Science.gov (United States)

    Lester, Kathryn J; Field, Andy P; Muris, Peter

    2011-01-01

    This study investigated the effects of experimentally modifying interpretation biases for children's cognitions, avoidance behavior, anxiety vulnerability, and physiological responding. Sixty-seven children (6-11 years) were randomly assigned to receive a positive or negative interpretation bias modification procedure to induce interpretation biases toward or away from threat about ambiguous situations involving Australian marsupials. Children rapidly learned to select outcomes of ambiguous situations, which were congruent with their assigned condition. Furthermore, following positive modification, children's threat biases about novel ambiguous situations significantly decreased, whereas threat biases significantly increased after negative modification. In response to a stress-evoking behavioral avoidance test, positive modification attenuated behavioral avoidance compared to negative modification. However, no significant effects of bias modification on anxiety vulnerability or physiological responses to this stress-evoking Behavioral Avoidance Task were observed.

  5. REAL-TIME ANALYSIS AND SELECTION BIASES IN THE SUPERNOVA LEGACY SURVEY

    International Nuclear Information System (INIS)

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

    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 2007 June), the SNLS discovered more than 3000 transient candidates, 373 of which have been spectroscopically confirmed 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 toward brighter values for z ∼> 0.75. We describe our approach to correct for these selection biases in our three-year SNLS cosmological analysis (SNLS3) and present a breakdown of the systematic uncertainties involved.

  6. Wheat bran reduces concentrations of digestible, metabolizable, and net energy in diets fed to pigs, but energy values in wheat bran determined by the difference procedure are not different from values estimated from a linear regression procedure.

    Science.gov (United States)

    Jaworski, N W; Liu, D W; Li, D F; Stein, H H

    2016-07-01

    An experiment was conducted to determine effects on DE, ME, and NE for growing pigs of adding 15 or 30% wheat bran to a corn-soybean meal diet and to compare values for DE, ME, and NE calculated using the difference procedure with values obtained using linear regression. Eighteen barrows (54.4 ± 4.3 kg initial BW) were individually housed in metabolism crates. The experiment had 3 diets and 6 replicate pigs per diet. The control diet contained corn, soybean meal, and no wheat bran. Two additional diets were formulated by mixing 15 or 30% wheat bran with 85 or 70% of the control diet, respectively. The experimental period lasted 15 d. During the initial 7 d, pigs were adapted to their experimental diets and housed in metabolism crates and fed 573 kcal ME/kg BW per day. On d 8, metabolism crates with the pigs were moved into open-circuit respiration chambers for measurement of O consumption and CO and CH production. The feeding level was the same as in the adaptation period, and feces and urine were collected during this period. On d 13 and 14, pigs were fed 225 kcal ME/kg BW per day, and pigs were then fasted for 24 h to obtain fasting heat production. Results of the experiment indicated that the apparent total tract digestibility of DM, GE, crude fiber, ADF, and NDF linearly decreased ( ≤ 0.05) as wheat bran inclusion increased in the diets. The daily O consumption and CO and CH production by pigs fed increasing concentrations of wheat bran linearly decreased ( ≤ 0.05), resulting in a linear decrease ( ≤ 0.05) in heat production. The DE (3,454, 3,257, and 3,161 kcal/kg for diets containing 0, 15, and 30% wheat bran, respectively for diets containing 0, 15, and 30% wheat bran, respectively), ME (3,400, 3,209, and 3,091 kcal/kg for diets containing 0, 15, and 30% wheat bran, respectively), and NE (1,808, 1,575, and 1,458 kcal/kg for diets containing 0, 15, and 30% wheat bran, respectively) of diets decreased (linear, ≤ 0.05) as wheat bran inclusion increased

  7. Regression periods in infancy: a case study from Catalonia.

    Science.gov (United States)

    Sadurní, Marta; Rostan, Carlos

    2002-05-01

    Based on Rijt-Plooij and Plooij's (1992) research on emergence of regression periods in the first two years of life, the presence of such periods in a group of 18 babies (10 boys and 8 girls, aged between 3 weeks and 14 months) from a Catalonian population was analyzed. The measurements were a questionnaire filled in by the infants' mothers, a semi-structured weekly tape-recorded interview, and observations in their homes. The procedure and the instruments used in the project follow those proposed by Rijt-Plooij and Plooij. Our results confirm the existence of the regression periods in the first year of children's life. Inter-coder agreement for trained coders was 78.2% and within-coder agreement was 90.1%. In the discussion, the possible meaning and relevance of regression periods in order to understand development from a psychobiological and social framework is commented upon.

  8. Ethnic bias and clinical decision-making among New Zealand medical students: an observational study.

    Science.gov (United States)

    Harris, Ricci; Cormack, Donna; Stanley, James; Curtis, Elana; Jones, Rhys; Lacey, Cameron

    2018-01-23

    Health professional racial/ethnic bias may impact on clinical decision-making and contribute to subsequent ethnic health inequities. However, limited research has been undertaken among medical students. This paper presents findings from the Bias and Decision-Making in Medicine (BDMM) study, which sought to examine ethnic bias (Māori (indigenous peoples) compared with New Zealand European) among medical students and associations with clinical decision-making. All final year New Zealand (NZ) medical students in 2014 and 2015 (n = 888) were invited to participate in a cross-sectional online study. Key components included: two chronic disease vignettes (cardiovascular disease (CVD) and depression) with randomized patient ethnicity (Māori or NZ European) and questions on patient management; implicit bias measures (an ethnicity preference Implicit Association Test (IAT) and an ethnicity and compliant patient IAT); and, explicit ethnic bias questions. Associations between ethnic bias and clinical decision-making responses to vignettes were tested using linear regression. Three hundred and two students participated (34% response rate). Implicit and explicit ethnic bias favoring NZ Europeans was apparent among medical students. In the CVD vignette, no significant differences in clinical decision-making by patient ethnicity were observed. There were also no differential associations by patient ethnicity between any measures of ethnic bias (implicit or explicit) and patient management responses in the CVD vignette. In the depression vignette, some differences in the ranking of recommended treatment options were observed by patient ethnicity and explicit preference for NZ Europeans was associated with increased reporting that NZ European patients would benefit from treatment but not Māori (slope difference 0.34, 95% CI 0.08, 0.60; p = 0.011), although this was the only significant finding in these analyses. NZ medical students demonstrated ethnic bias, although

  9. Priming in implicit memory tasks: prior study causes enhanced discriminability, not only bias.

    Science.gov (United States)

    Zeelenberg, René; Wagenmakers, Eric-Jan M; Raaijmakers, Jeroen G W

    2002-03-01

    R. Ratcliff and G. McKoon (1995, 1996, 1997; R. Ratcliff, D. Allbritton, & G. McKoon, 1997) have argued that repetition priming effects are solely due to bias. They showed that prior study of the target resulted in a benefit in a later implicit memory task. However, prior study of a stimulus similar to the target resulted in a cost. The present study, using a 2-alternative forced-choice procedure, investigated the effect of prior study in an unbiased condition: Both alternatives were studied prior to their presentation in an implicit memory task. Contrary to a pure bias interpretation of priming, consistent evidence was obtained in 3 implicit memory tasks (word fragment completion, auditory word identification, and picture identification) that performance was better when both alternatives were studied than when neither alternative was studied. These results show that prior study results in enhanced discriminability, not only bias.

  10. Large-scale galaxy bias

    Science.gov (United States)

    Jeong, Donghui; Desjacques, Vincent; Schmidt, Fabian

    2018-01-01

    Here, we briefly introduce the key results of the recent review (arXiv:1611.09787), whose abstract is as following. This review presents a comprehensive overview of galaxy bias, that is, the statistical relation between the distribution of galaxies and matter. We focus on large scales where cosmic density fields are quasi-linear. On these scales, the clustering of galaxies can be described by a perturbative bias expansion, and the complicated physics of galaxy formation is absorbed by a finite set of coefficients of the expansion, called bias parameters. The review begins with a detailed derivation of this very important result, which forms the basis of the rigorous perturbative description of galaxy clustering, under the assumptions of General Relativity and Gaussian, adiabatic initial conditions. Key components of the bias expansion are all leading local gravitational observables, which include the matter density but also tidal fields and their time derivatives. We hence expand the definition of local bias to encompass all these contributions. This derivation is followed by a presentation of the peak-background split in its general form, which elucidates the physical meaning of the bias parameters, and a detailed description of the connection between bias parameters and galaxy (or halo) statistics. We then review the excursion set formalism and peak theory which provide predictions for the values of the bias parameters. In the remainder of the review, we consider the generalizations of galaxy bias required in the presence of various types of cosmological physics that go beyond pressureless matter with adiabatic, Gaussian initial conditions: primordial non-Gaussianity, massive neutrinos, baryon-CDM isocurvature perturbations, dark energy, and modified gravity. Finally, we discuss how the description of galaxy bias in the galaxies' rest frame is related to clustering statistics measured from the observed angular positions and redshifts in actual galaxy catalogs.

  11. Ensemble stacking mitigates biases in inference of synaptic connectivity.

    Science.gov (United States)

    Chambers, Brendan; Levy, Maayan; Dechery, Joseph B; MacLean, Jason N

    2018-01-01

    A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.

  12. An obesogenic bias in women's spatial memory for high calorie snack food.

    Science.gov (United States)

    Allan, K; Allan, J L

    2013-08-01

    To help maintain a positive energy balance in ancestral human habitats, evolution appears to have designed a functional bias in spatial memory that enhances our ability to remember the location of high-calorie foodstuffs. Here, we investigated whether this functional bias has obesogenic consequences for individuals living in a modern urban environment. Spatial memory, dietary intentions, and perceived desirability, for high-calorie snacks and lower-calorie fruits and vegetables were measured using a computer-based task in 41 women (age: 18-35, body mass index: 18.5-30.0). Using multiple linear regression, we analyzed whether enhanced spatial memory for high-calorie snacks versus fruits and vegetables predicted BMI, controlling for dietary intention strength and perceived food desirability. We observed that enhanced spatial memory for high-calorie snacks (both independently, and relative to that for fruits and vegetables), significantly predicted higher BMI. The evolved function of high-calorie bias in human spatial memory, to promote positive energy balance, would therefore appear to be intact. But our data reveal that this function may contribute to higher, less healthy BMI in individuals in whom the memory bias is most marked. Our findings reveal a novel cognitive marker of vulnerability to weight gain that, once the proximal mechanisms are understood, may offer new possibilities for weight control interventions. Copyright © 2013 Elsevier Ltd. All rights reserved.

  13. Bayesian Inference of a Multivariate Regression Model

    Directory of Open Access Journals (Sweden)

    Marick S. Sinay

    2014-01-01

    Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.

  14. Information environment, behavioral biases, and home bias in analysts’ recommendations

    DEFF Research Database (Denmark)

    Farooq, Omar; Taouss, Mohammed

    2012-01-01

    Can information environment of a firm explain home bias in analysts’ recommendations? Can the extent of agency problems explain optimism difference between foreign and local analysts? This paper answers these questions by documenting the effect of information environment on home bias in analysts’...

  15. Aggression, emotional self-regulation, attentional bias, and cognitive inhibition predict risky driving behavior.

    Science.gov (United States)

    Sani, Susan Raouf Hadadi; Tabibi, Zahra; Fadardi, Javad Salehi; Stavrinos, Despina

    2017-12-01

    The present study explored whether aggression, emotional regulation, cognitive inhibition, and attentional bias towards emotional stimuli were related to risky driving behavior (driving errors, and driving violations). A total of 117 applicants for taxi driver positions (89% male, M age=36.59years, SD=9.39, age range 24-62years) participated in the study. Measures included the Ahwaz Aggression Inventory, the Difficulties in emotion regulation Questionnaire, the emotional Stroop task, the Go/No-go task, and the Driving Behavior Questionnaire. Correlation and regression analyses showed that aggression and emotional regulation predicted risky driving behavior. Difficulties in emotion regulation, the obstinacy and revengeful component of aggression, attentional bias toward emotional stimuli, and cognitive inhibition predicted driving errors. Aggression was the only significant predictive factor for driving violations. In conclusion, aggression and difficulties in regulating emotions may exacerbate risky driving behaviors. Deficits in cognitive inhibition and attentional bias toward negative emotional stimuli can increase driving errors. Predisposition to aggression has strong effect on making one vulnerable to violation of traffic rules and crashes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions

    Science.gov (United States)

    Ernst, Anja F.

    2017-01-01

    Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking. PMID:28533971

  17. Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions

    Directory of Open Access Journals (Sweden)

    Anja F. Ernst

    2017-05-01

    Full Text Available Misconceptions about the assumptions behind the standard linear regression model are widespread and dangerous. These lead to using linear regression when inappropriate, and to employing alternative procedures with less statistical power when unnecessary. Our systematic literature review investigated employment and reporting of assumption checks in twelve clinical psychology journals. Findings indicate that normality of the variables themselves, rather than of the errors, was wrongfully held for a necessary assumption in 4% of papers that use regression. Furthermore, 92% of all papers using linear regression were unclear about their assumption checks, violating APA-recommendations. This paper appeals for a heightened awareness for and increased transparency in the reporting of statistical assumption checking.

  18. Information-Pooling Bias in Collaborative Security Incident Correlation Analysis.

    Science.gov (United States)

    Rajivan, Prashanth; Cooke, Nancy J

    2018-03-01

    Incident correlation is a vital step in the cybersecurity threat detection process. This article presents research on the effect of group-level information-pooling bias on collaborative incident correlation analysis in a synthetic task environment. Past research has shown that uneven information distribution biases people to share information that is known to most team members and prevents them from sharing any unique information available with them. The effect of such biases on security team collaborations are largely unknown. Thirty 3-person teams performed two threat detection missions involving information sharing and correlating security incidents. Incidents were predistributed to each person in the team based on the hidden profile paradigm. Participant teams, randomly assigned to three experimental groups, used different collaboration aids during Mission 2. Communication analysis revealed that participant teams were 3 times more likely to discuss security incidents commonly known to the majority. Unaided team collaboration was inefficient in finding associations between security incidents uniquely available to each member of the team. Visualizations that augment perceptual processing and recognition memory were found to mitigate the bias. The data suggest that (a) security analyst teams, when conducting collaborative correlation analysis, could be inefficient in pooling unique information from their peers; (b) employing off-the-shelf collaboration tools in cybersecurity defense environments is inadequate; and (c) collaborative security visualization tools developed considering the human cognitive limitations of security analysts is necessary. Potential applications of this research include development of team training procedures and collaboration tool development for security analysts.

  19. Procedural Decision-Making Experiences among Informational Technology Professionals at a Midwestern Fortune 500 Company

    Science.gov (United States)

    McKee, Shari Turner

    2013-01-01

    Between 2002 and 2012, information technology (IT) procedural decisions related to technology, fraud, bias, greed, and misleading information increased cost by more than $44 billion. The purpose of this phenomenological study was to explore IT professionals' experiences of IT procedural decisions. The research questions were intended to learn from…

  20. A comparison of three methods of assessing differential item functioning (DIF) in the Hospital Anxiety Depression Scale: ordinal logistic regression, Rasch analysis and the Mantel chi-square procedure.

    Science.gov (United States)

    Cameron, Isobel M; Scott, Neil W; Adler, Mats; Reid, Ian C

    2014-12-01

    It is important for clinical practice and research that measurement scales of well-being and quality of life exhibit only minimal differential item functioning (DIF). DIF occurs where different groups of people endorse items in a scale to different extents after being matched by the intended scale attribute. We investigate the equivalence or otherwise of common methods of assessing DIF. Three methods of measuring age- and sex-related DIF (ordinal logistic regression, Rasch analysis and Mantel χ(2) procedure) were applied to Hospital Anxiety Depression Scale (HADS) data pertaining to a sample of 1,068 patients consulting primary care practitioners. Three items were flagged by all three approaches as having either age- or sex-related DIF with a consistent direction of effect; a further three items identified did not meet stricter criteria for important DIF using at least one method. When applying strict criteria for significant DIF, ordinal logistic regression was slightly less sensitive. Ordinal logistic regression, Rasch analysis and contingency table methods yielded consistent results when identifying DIF in the HADS depression and HADS anxiety scales. Regardless of methods applied, investigators should use a combination of statistical significance, magnitude of the DIF effect and investigator judgement when interpreting the results.

  1. A question of separation: disentangling tracer bias and gravitational non-linearity with counts-in-cells statistics

    Science.gov (United States)

    Uhlemann, C.; Feix, M.; Codis, S.; Pichon, C.; Bernardeau, F.; L'Huillier, B.; Kim, J.; Hong, S. E.; Laigle, C.; Park, C.; Shin, J.; Pogosyan, D.

    2018-02-01

    Starting from a very accurate model for density-in-cells statistics of dark matter based on large deviation theory, a bias model for the tracer density in spheres is formulated. It adopts a mean bias relation based on a quadratic bias model to relate the log-densities of dark matter to those of mass-weighted dark haloes in real and redshift space. The validity of the parametrized bias model is established using a parametrization-independent extraction of the bias function. This average bias model is then combined with the dark matter PDF, neglecting any scatter around it: it nevertheless yields an excellent model for densities-in-cells statistics of mass tracers that is parametrized in terms of the underlying dark matter variance and three bias parameters. The procedure is validated on measurements of both the one- and two-point statistics of subhalo densities in the state-of-the-art Horizon Run 4 simulation showing excellent agreement for measured dark matter variance and bias parameters. Finally, it is demonstrated that this formalism allows for a joint estimation of the non-linear dark matter variance and the bias parameters using solely the statistics of subhaloes. Having verified that galaxy counts in hydrodynamical simulations sampled on a scale of 10 Mpc h-1 closely resemble those of subhaloes, this work provides important steps towards making theoretical predictions for density-in-cells statistics applicable to upcoming galaxy surveys like Euclid or WFIRST.

  2. Flexible competing risks regression modeling and goodness-of-fit

    DEFF Research Database (Denmark)

    Scheike, Thomas; Zhang, Mei-Jie

    2008-01-01

    In this paper we consider different approaches for estimation and assessment of covariate effects for the cumulative incidence curve in the competing risks model. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause...... models that is easy to fit and contains the Fine-Gray model as a special case. One advantage of this approach is that our regression modeling allows for non-proportional hazards. This leads to a new simple goodness-of-fit procedure for the proportional subdistribution hazards assumption that is very easy...... of the flexible regression models to analyze competing risks data when non-proportionality is present in the data....

  3. Gender bias in the evaluation of new age music.

    Science.gov (United States)

    Colley, Ann; North, Adrian; Hargreaves, David J

    2003-04-01

    Eminent composers in Western European art music continue to be predominantly male and eminence in contemporary pop music is similarly male dominated. One contributing factor may be the continuing under-valuation of women's music. Possible anti-female bias in a contemporary genre was investigated using the Goldberg paradigm to elicit judgments of New Age compositions. Since stronger stereotyping effects occur when information provided about individuals is sparse, fictitious male and female composers were presented either by name only or by name with a brief biography. Evidence for anti-female bias was found in the name-only condition and was stronger when liking for the music was controlled. Other findings were the tendency for females to give higher ratings, and the association of gender differences in liking of the music with ratings of quality in the name-only condition. These results are relevant to the design of formal assessment procedures for musical composition.

  4. Detection of Differential Item Functioning with Nonlinear Regression: A Non-IRT Approach Accounting for Guessing

    Science.gov (United States)

    Drabinová, Adéla; Martinková, Patrícia

    2017-01-01

    In this article we present a general approach not relying on item response theory models (non-IRT) to detect differential item functioning (DIF) in dichotomous items with presence of guessing. The proposed nonlinear regression (NLR) procedure for DIF detection is an extension of method based on logistic regression. As a non-IRT approach, NLR can…

  5. Regression: A Bibliography.

    Science.gov (United States)

    Pedrini, D. T.; Pedrini, Bonnie C.

    Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…

  6. Social incentives improve deliberative but not procedural learning in older adults.

    Science.gov (United States)

    Gorlick, Marissa A; Maddox, W Todd

    2015-01-01

    Age-related deficits are seen across tasks where learning depends on asocial feedback processing, however plasticity has been observed in some of the same tasks in social contexts suggesting a novel way to attenuate deficits. Socioemotional selectivity theory suggests this plasticity is due to a deliberative motivational shift toward achieving well-being with age (positivity effect) that reverses when executive processes are limited (negativity effect). The present study examined the interaction of feedback valence (positive, negative) and social salience (emotional face feedback - happy; angry, asocial point feedback - gain; loss) on learning in a deliberative task that challenges executive processes and a procedural task that does not. We predict that angry face feedback will improve learning in a deliberative task when executive function is challenged. We tested two competing hypotheses regarding the interactive effects of deliberative emotional biases on automatic feedback processing: (1) If deliberative emotion regulation and automatic feedback are interactive we expect happy face feedback to improve learning and angry face feedback to impair learning in older adults because cognitive control is available. (2) If deliberative emotion regulation and automatic feedback are not interactive we predict that emotional face feedback will not improve procedural learning regardless of valence. Results demonstrate that older adults show persistent deficits relative to younger adults during procedural category learning suggesting that deliberative emotional biases do not interact with automatic feedback processing. Interestingly, a subgroup of older adults identified as potentially using deliberative strategies tended to learn as well as younger adults with angry relative to happy feedback, matching the pattern observed in the deliberative task. Results suggest that deliberative emotional biases can improve deliberative learning, but have no effect on procedural learning.

  7. An appraisal of convergence failures in the application of logistic regression model in published manuscripts.

    Science.gov (United States)

    Yusuf, O B; Bamgboye, E A; Afolabi, R F; Shodimu, M A

    2014-09-01

    Logistic regression model is widely used in health research for description and predictive purposes. Unfortunately, most researchers are sometimes not aware that the underlying principles of the techniques have failed when the algorithm for maximum likelihood does not converge. Young researchers particularly postgraduate students may not know why separation problem whether quasi or complete occurs, how to identify it and how to fix it. This study was designed to critically evaluate convergence issues in articles that employed logistic regression analysis published in an African Journal of Medicine and medical sciences between 2004 and 2013. Problems of quasi or complete separation were described and were illustrated with the National Demographic and Health Survey dataset. A critical evaluation of articles that employed logistic regression was conducted. A total of 581 articles was reviewed, of which 40 (6.9%) used binary logistic regression. Twenty-four (60.0%) stated the use of logistic regression model in the methodology while none of the articles assessed model fit. Only 3 (12.5%) properly described the procedures. Of the 40 that used the logistic regression model, the problem of convergence occurred in 6 (15.0%) of the articles. Logistic regression tends to be poorly reported in studies published between 2004 and 2013. Our findings showed that the procedure may not be well understood by researchers since very few described the process in their reports and may be totally unaware of the problem of convergence or how to deal with it.

  8. Ethnic differences in attitudes and bias toward older people comparing White and Asian nursing students.

    Science.gov (United States)

    Lee, Young-Shin

    2015-03-01

    To identify attitudes and bias toward aging between Asian and White students and identify factors affecting attitudes toward aging. A cross-sectional sample of 308 students in a nursing program completed the measure of Attitudes Toward Older People and Aging Quiz electronically. There were no differences in positive attitudes and pro-aged bias between Asian and White groups, but Asian students had significantly more negative attitudes and anti-aged bias toward older people than White students. Multiple regression analysis showed ethnicity/race was the strongest variable to explain negative attitudes toward older people. Feeling uneasy about talking to older adults was the most significant factor to explain all attitudinal concepts. Asian students were uneasy about talking with older people and had negative attitudes toward older adults. To become competent in cross-cultural care and communication in nursing, educational strategies to reduce negative attitudes on aging are necessary. © The Author(s) 2014.

  9. Reconstruction of missing daily streamflow data using dynamic regression models

    Science.gov (United States)

    Tencaliec, Patricia; Favre, Anne-Catherine; Prieur, Clémentine; Mathevet, Thibault

    2015-12-01

    River discharge is one of the most important quantities in hydrology. It provides fundamental records for water resources management and climate change monitoring. Even very short data-gaps in this information can cause extremely different analysis outputs. Therefore, reconstructing missing data of incomplete data sets is an important step regarding the performance of the environmental models, engineering, and research applications, thus it presents a great challenge. The objective of this paper is to introduce an effective technique for reconstructing missing daily discharge data when one has access to only daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA structure. Application of the model to eight daily streamflow data for the Durance river watershed showed that the model yields reliable estimates for the missing data in the time series. Simulation studies were also conducted to evaluate the performance of the procedure.

  10. Pharmacogenomics Bias - Systematic distortion of study results by genetic heterogeneity

    Directory of Open Access Journals (Sweden)

    Zietemann, Vera

    2008-04-01

    Full Text Available Background: Decision analyses of drug treatments in chronic diseases require modeling the progression of disease and treatment response beyond the time horizon of clinical or epidemiological studies. In many such models, progression and drug effect have been applied uniformly to all patients; heterogeneity in progression, including pharmacogenomic effects, has been ignored. Objective: We sought to systematically evaluate the existence, direction and relative magnitude of a pharmacogenomics bias (PGX-Bias resulting from failure to adjust for genetic heterogeneity in both treatment response (HT and heterogeneity in progression of disease (HP in decision-analytic studies based on clinical study data. Methods: We performed a systematic literature search in electronic databases for studies regarding the effect of genetic heterogeneity on the validity of study results. Included studies have been summarized in evidence tables. In the case of lacking evidence from published studies we sought to perform our own simulation considering both HT and HP. We constructed two simple Markov models with three basic health states (early-stage disease, late-stage disease, dead, one adjusting and the other not adjusting for genetic heterogeneity. Adjustment was done by creating different disease states for presence (G+ and absence (G- of a dichotomous genetic factor. We compared the life expectancy gains attributable to treatment resulting from both models and defined pharmacogenomics bias as percent deviation of treatment-related life expectancy gains in the unadjusted model from those in the adjusted model. We calculated the bias as a function of underlying model parameters to create generic results. We then applied our model to lipid-lowering therapy with pravastatin in patients with coronary atherosclerosis, incorporating the influence of two TaqIB polymorphism variants (B1 and B2 on progression and drug efficacy as reported in the DNA substudy of the REGRESS

  11. Bond and Equity Home Bias and Foreign Bias: an International Study

    OpenAIRE

    VanPée, Rosanne; De Moor, Lieven

    2012-01-01

    In this paper we explore tentatively and formally the differences between bond and equity home bias and foreign bias based on one large scale dataset including developed and emerging markets for the period 2001 to 2010. We set the stage by tentatively and formally linking the diversion of bond and equity home bias in OECD countries to the increasing public debt issues under the form of government bonds i.e. the supply-driven argument. Unlike Fidora et al. (2007) we do not find that exchange r...

  12. Advanced statistics: linear regression, part I: simple linear regression.

    Science.gov (United States)

    Marill, Keith A

    2004-01-01

    Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

  13. Regression analysis of mixed recurrent-event and panel-count data.

    Science.gov (United States)

    Zhu, Liang; Tong, Xinwei; Sun, Jianguo; Chen, Manhua; Srivastava, Deo Kumar; Leisenring, Wendy; Robison, Leslie L

    2014-07-01

    In event history studies concerning recurrent events, two types of data have been extensively discussed. One is recurrent-event data (Cook and Lawless, 2007. The Analysis of Recurrent Event Data. New York: Springer), and the other is panel-count data (Zhao and others, 2010. Nonparametric inference based on panel-count data. Test 20: , 1-42). In the former case, all study subjects are monitored continuously; thus, complete information is available for the underlying recurrent-event processes of interest. In the latter case, study subjects are monitored periodically; thus, only incomplete information is available for the processes of interest. In reality, however, a third type of data could occur in which some study subjects are monitored continuously, but others are monitored periodically. When this occurs, we have mixed recurrent-event and panel-count data. This paper discusses regression analysis of such mixed data and presents two estimation procedures for the problem. One is a maximum likelihood estimation procedure, and the other is an estimating equation procedure. The asymptotic properties of both resulting estimators of regression parameters are established. Also, the methods are applied to a set of mixed recurrent-event and panel-count data that arose from a Childhood Cancer Survivor Study and motivated this investigation. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  14. A Study of Assimilation Bias in Name-Based Sampling of Migrants

    Directory of Open Access Journals (Sweden)

    Schnell Rainer

    2014-06-01

    Full Text Available The use of personal names for screening is an increasingly popular sampling technique for migrant populations. Although this is often an effective sampling procedure, very little is known about the properties of this method. Based on a large German survey, this article compares characteristics of respondents whose names have been correctly classified as belonging to a migrant population with respondentswho aremigrants and whose names have not been classified as belonging to a migrant population. Although significant differences were found for some variables even with some large effect sizes, the overall bias introduced by name-based sampling (NBS is small as long as procedures with small false-negative rates are employed.

  15. Inference for multivariate regression model based on multiply imputed synthetic data generated via posterior predictive sampling

    Science.gov (United States)

    Moura, Ricardo; Sinha, Bimal; Coelho, Carlos A.

    2017-06-01

    The recent popularity of the use of synthetic data as a Statistical Disclosure Control technique has enabled the development of several methods of generating and analyzing such data, but almost always relying in asymptotic distributions and in consequence being not adequate for small sample datasets. Thus, a likelihood-based exact inference procedure is derived for the matrix of regression coefficients of the multivariate regression model, for multiply imputed synthetic data generated via Posterior Predictive Sampling. Since it is based in exact distributions this procedure may even be used in small sample datasets. Simulation studies compare the results obtained from the proposed exact inferential procedure with the results obtained from an adaptation of Reiters combination rule to multiply imputed synthetic datasets and an application to the 2000 Current Population Survey is discussed.

  16. A generalized right truncated bivariate Poisson regression model with applications to health data.

    Science.gov (United States)

    Islam, M Ataharul; Chowdhury, Rafiqul I

    2017-01-01

    A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model.

  17. High-Dimensional Intrinsic Interpolation Using Gaussian Process Regression and Diffusion Maps

    International Nuclear Information System (INIS)

    Thimmisetty, Charanraj A.; Ghanem, Roger G.; White, Joshua A.; Chen, Xiao

    2017-01-01

    This article considers the challenging task of estimating geologic properties of interest using a suite of proxy measurements. The current work recast this task as a manifold learning problem. In this process, this article introduces a novel regression procedure for intrinsic variables constrained onto a manifold embedded in an ambient space. The procedure is meant to sharpen high-dimensional interpolation by inferring non-linear correlations from the data being interpolated. The proposed approach augments manifold learning procedures with a Gaussian process regression. It first identifies, using diffusion maps, a low-dimensional manifold embedded in an ambient high-dimensional space associated with the data. It relies on the diffusion distance associated with this construction to define a distance function with which the data model is equipped. This distance metric function is then used to compute the correlation structure of a Gaussian process that describes the statistical dependence of quantities of interest in the high-dimensional ambient space. The proposed method is applicable to arbitrarily high-dimensional data sets. Here, it is applied to subsurface characterization using a suite of well log measurements. The predictions obtained in original, principal component, and diffusion space are compared using both qualitative and quantitative metrics. Considerable improvement in the prediction of the geological structural properties is observed with the proposed method.

  18. Amplification biases: possible differences among deviating gene expressions

    Directory of Open Access Journals (Sweden)

    Piumi Francois

    2008-01-01

    Full Text Available Abstract Background Gene expression profiling has become a tool of choice to study pathological or developmental questions but in most cases the material is scarce and requires sample amplification. Two main procedures have been used: in vitro transcription (IVT and polymerase chain reaction (PCR, the former known as linear and the latter as exponential. Previous reports identified enzymatic pitfalls in PCR and IVT protocols; however the possible differences between the sequences affected by these amplification defaults were only rarely explored. Results Screening a bovine cDNA array dedicated to embryonic stages with embryonic (n = 3 and somatic tissues (n = 2, we proceeded to moderate amplifications starting from 1 μg of total RNA (global PCR or IVT one round. Whatever the tissue, 16% of the probes were involved in deviating gene expressions due to amplification defaults. These distortions were likely due to the molecular features of the affected sequences (position within a gene, GC content, hairpin number but also to the relative abundance of these transcripts within the tissues. These deviating genes mainly encoded housekeeping genes from physiological or cellular processes (70% and constituted 2 subsets which did not overlap (molecular features, signal intensities, gene ID. However, the differential expressions identified between embryonic stages were both reliable (minor intersect with biased expressions and relevant (biologically validated. In addition, the relative expression levels of those genes were biologically similar between amplified and unamplified samples. Conclusion Conversely to the most recent reports which challenged the use of intense amplification procedures on minute amounts of RNA, we chose moderate PCR and IVT amplifications for our gene profiling study. Conclusively, it appeared that systematic biases arose even with moderate amplification procedures, independently of (i the sample used: brain, ovary or embryos, (ii

  19. Science Possible Selves and the Desire to be a Scientist: Mindsets, Gender Bias, and Confidence during Early Adolescence.

    Science.gov (United States)

    Hill, Patricia Wonch; McQuillan, Julia; Talbert, Eli; Spiegel, Amy; Gauthier, G Robin; Diamond, Judy

    2017-06-01

    In the United States, gender gaps in science interest widen during the middle school years. Recent research on adults shows that gender gaps in some academic fields are associated with mindsets about ability and gender-science biases. In a sample of 529 students in a U.S. middle school, we assess how explicit boy-science bias, science confidence, science possible self (belief in being able to become a scientist), and desire to be a scientist vary by gender. Guided by theories and prior research, we use a series of multivariate logistic regression models to examine the relationships between mindsets about ability and these variables. We control for self-reported science grades, social capital, and race/ethnic minority status. Results show that seeing academic ability as innate ("fixed mindsets") is associated with boy-science bias, and that younger girls have less boy-science bias than older girls. Fixed mindsets and boy-science bias are both negatively associated with a science possible self; science confidence is positively associated with a science possible self. In the final model, high science confident and having a science possible self are positively associated with a desire to be a scientist. Facilitating growth mindsets and countering boy-science bias in middle school may be fruitful interventions for widening participation in science careers.

  20. Media bias and advertising: Evidence from a German car magazine

    OpenAIRE

    Dewenter, Ralf; Heimeshoff, Ulrich

    2014-01-01

    This paper investigates the existence of a possible media bias by analyzing the impact of auto- mobile manufacturer’s advertisements on automobile reviews in a leading German car maga- zine. By accounting for both endogeneity and sample selection using a two-step procedure, we find a positive impact of advertising volumes on test scores. The main advantage of our study is the measurement of technical characteristics of cars to explain test scores. Due to this kind of measurement, we avoid ser...

  1. The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study

    Directory of Open Access Journals (Sweden)

    Tania Dehesh

    2015-01-01

    Full Text Available Background. Univariate meta-analysis (UM procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS method as a multivariate meta-analysis approach. Methods. We evaluated the efficiency of four new approaches including zero correlation (ZC, common correlation (CC, estimated correlation (EC, and multivariate multilevel correlation (MMC on the estimation bias, mean square error (MSE, and 95% probability coverage of the confidence interval (CI in the synthesis of Cox proportional hazard models coefficients in a simulation study. Result. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. Conclusion. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.

  2. The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study.

    Science.gov (United States)

    Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi

    2015-01-01

    Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.

  3. Assessment of cognitive bias in decision-making and leadership styles among critical care nurses: a mixed methods study.

    Science.gov (United States)

    Lean Keng, Soon; AlQudah, Hani Nawaf Ibrahim

    2017-02-01

    To raise awareness of critical care nurses' cognitive bias in decision-making, its relationship with leadership styles and its impact on care delivery. The relationship between critical care nurses' decision-making and leadership styles in hospitals has been widely studied, but the influence of cognitive bias on decision-making and leadership styles in critical care environments remains poorly understood, particularly in Jordan. Two-phase mixed methods sequential explanatory design and grounded theory. critical care unit, Prince Hamza Hospital, Jordan. Participant sampling: convenience sampling Phase 1 (quantitative, n = 96), purposive sampling Phase 2 (qualitative, n = 20). Pilot tested quantitative survey of 96 critical care nurses in 2012. Qualitative in-depth interviews, informed by quantitative results, with 20 critical care nurses in 2013. Descriptive and simple linear regression quantitative data analyses. Thematic (constant comparative) qualitative data analysis. Quantitative - correlations found between rationality and cognitive bias, rationality and task-oriented leadership styles, cognitive bias and democratic communication styles and cognitive bias and task-oriented leadership styles. Qualitative - 'being competent', 'organizational structures', 'feeling self-confident' and 'being supported' in the work environment identified as key factors influencing critical care nurses' cognitive bias in decision-making and leadership styles. Two-way impact (strengthening and weakening) of cognitive bias in decision-making and leadership styles on critical care nurses' practice performance. There is a need to heighten critical care nurses' consciousness of cognitive bias in decision-making and leadership styles and its impact and to develop organization-level strategies to increase non-biased decision-making. © 2016 John Wiley & Sons Ltd.

  4. Cognitive Bias in Systems Verification

    Science.gov (United States)

    Larson, Steve

    2012-01-01

    Working definition of cognitive bias: Patterns by which information is sought and interpreted that can lead to systematic errors in decisions. Cognitive bias is used in diverse fields: Economics, Politics, Intelligence, Marketing, to name a few. Attempts to ground cognitive science in physical characteristics of the cognitive apparatus exceed our knowledge. Studies based on correlations; strict cause and effect is difficult to pinpoint. Effects cited in the paper and discussed here have been replicated many times over, and appear sound. Many biases have been described, but it is still unclear whether they are all distinct. There may only be a handful of fundamental biases, which manifest in various ways. Bias can effect system verification in many ways . Overconfidence -> Questionable decisions to deploy. Availability -> Inability to conceive critical tests. Representativeness -> Overinterpretation of results. Positive Test Strategies -> Confirmation bias. Debiasing at individual level very difficult. The potential effect of bias on the verification process can be managed, but not eliminated. Worth considering at key points in the process.

  5. Biases in categorization

    NARCIS (Netherlands)

    Das-Smaal, E.A.

    1990-01-01

    On what grounds can we conclude that an act of categorization is biased? In this chapter, it is contended that in the absence of objective norms of what categories actually are, biases in categorization can only be specified in relation to theoretical understandings of categorization. Therefore, the

  6. Regression Analysis of the Effect of Bias Voltage on Nano- and Macrotribological Properties of Diamond-Like Carbon Films Deposited by a Filtered Cathodic Vacuum Arc Ion-Plating Method

    Directory of Open Access Journals (Sweden)

    Shojiro Miyake

    2014-01-01

    Full Text Available Diamond-like carbon (DLC films are deposited by bend filtered cathodic vacuum arc (FCVA technique with DC and pulsed bias voltage. The effects of varying bias voltage on nanoindentation and nanowear properties were evaluated by atomic force microscopy. DLC films deposited with DC bias voltage of −50 V exhibited the greatest hardness at approximately 50 GPa, a low modulus of dissipation, low elastic modulus to nanoindentation hardness ratio, and high nanowear resistance. Nanoindentation hardness was positively correlated with the Raman peak ratio Id/Ig, whereas wear depth was negatively correlated with this ratio. These nanotribological properties highly depend on the films’ nanostructures. The tribological properties of the FCVA-DLC films were also investigated using a ball-on-disk test. The average friction coefficient of DLC films deposited with DC bias voltage was lower than that of DLC films deposited with pulse bias voltage. The friction coefficient calculated from the ball-on-disk test was correlated with the nanoindentation hardness in dry conditions. However, under boundary lubrication conditions, the friction coefficient and specific wear rate had little correlation with nanoindentation hardness, and wear behavior seemed to be influenced by other factors such as adhesion strength between the film and substrate.

  7. National HIV prevalence estimates for sub-Saharan Africa: controlling selection bias with Heckman-type selection models

    Science.gov (United States)

    Hogan, Daniel R; Salomon, Joshua A; Canning, David; Hammitt, James K; Zaslavsky, Alan M; Bärnighausen, Till

    2012-01-01

    Objectives Population-based HIV testing surveys have become central to deriving estimates of national HIV prevalence in sub-Saharan Africa. However, limited participation in these surveys can lead to selection bias. We control for selection bias in national HIV prevalence estimates using a novel approach, which unlike conventional imputation can account for selection on unobserved factors. Methods For 12 Demographic and Health Surveys conducted from 2001 to 2009 (N=138 300), we predict HIV status among those missing a valid HIV test with Heckman-type selection models, which allow for correlation between infection status and participation in survey HIV testing. We compare these estimates with conventional ones and introduce a simulation procedure that incorporates regression model parameter uncertainty into confidence intervals. Results Selection model point estimates of national HIV prevalence were greater than unadjusted estimates for 10 of 12 surveys for men and 11 of 12 surveys for women, and were also greater than the majority of estimates obtained from conventional imputation, with significantly higher HIV prevalence estimates for men in Cote d'Ivoire 2005, Mali 2006 and Zambia 2007. Accounting for selective non-participation yielded 95% confidence intervals around HIV prevalence estimates that are wider than those obtained with conventional imputation by an average factor of 4.5. Conclusions Our analysis indicates that national HIV prevalence estimates for many countries in sub-Saharan African are more uncertain than previously thought, and may be underestimated in several cases, underscoring the need for increasing participation in HIV surveys. Heckman-type selection models should be included in the set of tools used for routine estimation of HIV prevalence. PMID:23172342

  8. Baboons' response speed is biased by their moods.

    Directory of Open Access Journals (Sweden)

    Yousri Marzouki

    Full Text Available The affect-as-information hypothesis (e.g., Schwarz & Clore, 2003, predicts that the positive or negative valence of our mood differentially affects our processing of the details of the environment. However, this hypothesis has only been tested with mood induction procedures and fairly complex cognitive tasks in humans. Here, six baboons (Papio papio living in a social group had free access to a computerized visual search task on which they were over-trained. Trials that immediately followed a spontaneously expressed emotional behavior were analyzed, ruling out possible biases due to induction procedures. RTs following negatively valenced behaviors are slower than those following neutral and positively valenced behaviors, respectively. Thus, moods affect the performance of nonhuman primates tested in highly automatized tasks, as it does in humans during tasks with much higher cognitive demands. These findings reveal a presumably universal and adaptive mechanism by which moods influence performance in various ecological contexts.

  9. HYPNOSIS FOR ACUTE PROCEDURAL PAIN: A Critical Review

    Science.gov (United States)

    Kendrick, Cassie; Sliwinski, Jim; Yu, Yimin; Johnson, Aimee; Fisher, William; Kekecs, Zoltán; Elkins, Gary

    2015-01-01

    Clinical evidence for the effectiveness of hypnosis in the treatment of acute, procedural pain was critically evaluated based on reports from randomized controlled clinical trials (RCTs). Results from the 29 RCTs meeting inclusion criteria suggest that hypnosis decreases pain compared to standard care and attention control groups and that it is at least as effective as comparable adjunct psychological or behavioral therapies. In addition, applying hypnosis in multiple sessions prior to the day of the procedure produced the highest percentage of significant results. Hypnosis was most effective in minor surgical procedures. However, interpretations are limited by considerable risk of bias. Further studies using minimally effective control conditions and systematic control of intervention dose and timing are required to strengthen conclusions. PMID:26599994

  10. Hypnosis for Acute Procedural Pain: A Critical Review.

    Science.gov (United States)

    Kendrick, Cassie; Sliwinski, Jim; Yu, Yimin; Johnson, Aimee; Fisher, William; Kekecs, Zoltán; Elkins, Gary

    2016-01-01

    Clinical evidence for the effectiveness of hypnosis in the treatment of acute procedural pain was critically evaluated based on reports from randomized controlled clinical trials (RCTs). Results from the 29 RCTs meeting inclusion criteria suggest that hypnosis decreases pain compared to standard care and attention control groups and that it is at least as effective as comparable adjunct psychological or behavioral therapies. In addition, applying hypnosis in multiple sessions prior to the day of the procedure produced the highest percentage of significant results. Hypnosis was most effective in minor surgical procedures. However, interpretations are limited by considerable risk of bias. Further studies using minimally effective control conditions and systematic control of intervention dose and timing are required to strengthen conclusions.

  11. Cognitive bias measurement and social anxiety disorder: Correlating self-report data and attentional bias

    Directory of Open Access Journals (Sweden)

    Alexander Miloff

    2015-09-01

    Full Text Available Social anxiety disorder (SAD and attentional bias are theoretically connected in cognitive behavioral therapeutic models. In fact, there is an emerging field focusing on modifying attentional bias as a stand-alone treatment. However, it is unclear to what degree these attentional biases are present before commencing treatment. The purpose of this study was to measure pre-treatment attentional bias in 153 participants diagnosed with SAD using a home-based Internet version of the dot-probe paradigm. Results showed no significant correlation for attentional bias (towards or away from negative words or faces and the self-rated version of the Liebowitz Social Anxiety Scale (LSAS-SR. However, two positive correlations were found for the secondary measures Generalized Anxiety Disorder 7 (GAD-7 and Patient Health Questionnaire 9 (PHQ-9. These indicated that those with elevated levels of anxiety and depression had a higher bias towards negative faces in neutral–negative and positive–negative valence combinations, respectively. The unreliability of the dot-probe paradigm and home-based Internet delivery are discussed to explain the lack of correlations between LSAS-SR and attentional bias. Changes to the dot-probe task are suggested that could improve reliability.

  12. Estimating integrated variance in the presence of microstructure noise using linear regression

    Science.gov (United States)

    Holý, Vladimír

    2017-07-01

    Using financial high-frequency data for estimation of integrated variance of asset prices is beneficial but with increasing number of observations so-called microstructure noise occurs. This noise can significantly bias the realized variance estimator. We propose a method for estimation of the integrated variance robust to microstructure noise as well as for testing the presence of the noise. Our method utilizes linear regression in which realized variances estimated from different data subsamples act as dependent variable while the number of observations act as explanatory variable. We compare proposed estimator with other methods on simulated data for several microstructure noise structures.

  13. Gaussian process regression for geometry optimization

    Science.gov (United States)

    Denzel, Alexander; Kästner, Johannes

    2018-03-01

    We implemented a geometry optimizer based on Gaussian process regression (GPR) to find minimum structures on potential energy surfaces. We tested both a two times differentiable form of the Matérn kernel and the squared exponential kernel. The Matérn kernel performs much better. We give a detailed description of the optimization procedures. These include overshooting the step resulting from GPR in order to obtain a higher degree of interpolation vs. extrapolation. In a benchmark against the Limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer of the DL-FIND library on 26 test systems, we found the new optimizer to generally reduce the number of required optimization steps.

  14. Ordinary least square regression, orthogonal regression, geometric mean regression and their applications in aerosol science

    International Nuclear Information System (INIS)

    Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei

    2007-01-01

    Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age

  15. Social Incentives Improve Deliberative But Not Procedural Learning in Older Adults

    Directory of Open Access Journals (Sweden)

    Marissa A Gorlick

    2015-04-01

    Full Text Available Age-related deficits are seen across tasks where learning depends on asocial feedback processing, however plasticity has been observed in some of the same tasks in social contexts suggesting a novel way to attenuate deficits. Socioemotional selectivity theory suggests this plasticity is due to a deliberative motivational shift toward achieving well-being with age (positivity effect that reverses when executive processes are limited (negativity effect. The present study examined the interaction of feedback valence (positive, negative and social salience (emotional face feedback – happy; angry, asocial point feedback – gain; loss on learning in a deliberative task that challenges executive processes and a procedural task that does not. We predict that angry face feedback will improve learning in a deliberative task when executive function is challenged. We tested two competing hypotheses regarding the interactive effects of deliberative emotional biases on automatic feedback processing: 1 If deliberative emotion regulation and automatic feedback are interactive we expect happy face feedback to improve learning and angry face feedback to impair learning in older adults because cognitive control is available. 2 If deliberative emotion regulation and automatic feedback are not interactive we predict that emotional face feedback will not improve procedural learning regardless of valence. Results demonstrate that older adults show persistent deficits relative to younger adults during procedural category learning suggesting that deliberative emotional biases do not interact with automatic feedback processing. Interestingly, a subgroup of older adults identified as potentially using deliberative strategies tended to learn as well as younger adults with angry relative to happy feedback, matching the pattern observed in the deliberative task. Results suggest that deliberative emotional biases can improve deliberative learning, but have no effect on

  16. Mood As Cumulative Expectation Mismatch: A Test of Theory Based on Data from Non-verbal Cognitive Bias Tests

    Directory of Open Access Journals (Sweden)

    Camille M. C. Raoult

    2017-12-01

    Full Text Available Affective states are known to influence behavior and cognitive processes. To assess mood (moderately long-term affective states, the cognitive judgment bias test was developed and has been widely used in various animal species. However, little is known about how mood changes, how mood can be experimentally manipulated, and how mood then feeds back into cognitive judgment. A recent theory argues that mood reflects the cumulative impact of differences between obtained outcomes and expectations. Here expectations refer to an established context. Situations in which an established context fails to match an outcome are then perceived as mismatches of expectation and outcome. We take advantage of the large number of studies published on non-verbal cognitive bias tests in recent years (95 studies with a total of 162 independent tests to test whether cumulative mismatch could indeed have led to the observed mood changes. Based on a criteria list, we assessed whether mismatch had occurred with the experimental procedure used to induce mood (mood induction mismatch, or in the context of the non-verbal cognitive bias procedure (testing mismatch. For the mood induction mismatch, we scored the mismatch between the subjects’ potential expectations and the manipulations conducted for inducing mood whereas, for the testing mismatch, we scored mismatches that may have occurred during the actual testing. We then investigated whether these two types of mismatch can predict the actual outcome of the cognitive bias study. The present evaluation shows that mood induction mismatch cannot well predict the success of a cognitive bias test. On the other hand, testing mismatch can modulate or even inverse the expected outcome. We think, cognitive bias studies should more specifically aim at creating expectation mismatch while inducing mood states to test the cumulative mismatch theory more properly. Furthermore, testing mismatch should be avoided as much as possible

  17. Systemic racism moderates effects of provider racial biases on adherence to hypertension treatment for African Americans.

    Science.gov (United States)

    Greer, Tawanda M; Brondolo, Elizabeth; Brown, Porschia

    2014-01-01

    The purpose of the current study was to examine perceived exposure to systemic racism as a moderator of the effects of perceived exposure to provider racial biases on treatment adherence and mistrust of health care for a sample of African American hypertensive patients. We hypothesized that patients who endorsed high levels of systemic racism would exhibit poor adherence to hypertension treatment and increased mistrust in health care in relation to perceptions of exposure to provider racial biases. The sample consisted of 100 African American patients who ranged in age from 24 to 82 years. All were diagnosed with hypertension and were recruited from an outpatient clinic located in the Southeastern region of the United States. Moderated regression analyses were performed to test the study hypotheses. Findings revealed a positive, significant main effect for perceived provider racial biases in predicting mistrust of care. This finding suggested that an increase in mistrust of health care was associated with increased perceptions of provider biases. In predicting treatment adherence, a significant interaction revealed that patients who endorsed low and moderate degrees of exposure to systemic racism displayed poor adherence to treatment in relation to greater perceptions of provider racial biases. The overall findings suggest that patients who perceive themselves as infrequently exposed to systemic racism possess the greatest risk for nonadherence to hypertension treatment in relation to increased perceptions of provider racial biases. Implications of the findings are discussed. 2014 APA, all rights reserved

  18. Threat bias, not negativity bias, underpins differences in political ideology.

    Science.gov (United States)

    Lilienfeld, Scott O; Latzman, Robert D

    2014-06-01

    Although disparities in political ideology are rooted partly in dispositional differences, Hibbing et al.'s analysis paints with an overly broad brush. Research on the personality correlates of liberal-conservative differences points not to global differences in negativity bias, but to differences in threat bias, probably emanating from differences in fearfulness. This distinction bears implications for etiological research and persuasion efforts.

  19. 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...... configurations of an age-structured population dynamics model. This simulation experiment shows that the epsilon-method and the existing bias-correction method perform equally well in data-rich contexts, but the epsilon-method is slightly less biased in data-poor contexts. We then apply the epsilon......-method to a spatial regression model when estimating an index of population abundance, and compare results with an alternative bias-correction algorithm that involves Markov-chain Monte Carlo sampling. This example shows that the epsilon-method leads to a biologically significant difference in estimates of average...

  20. Polychotomization of continuous variables in regression models based on the overall C index

    Directory of Open Access Journals (Sweden)

    Bax Leon

    2006-12-01

    Full Text Available Abstract Background When developing multivariable regression models for diagnosis or prognosis, continuous independent variables can be categorized to make a prediction table instead of a prediction formula. Although many methods have been proposed to dichotomize prognostic variables, to date there has been no integrated method for polychotomization. The latter is necessary when dichotomization results in too much loss of information or when central values refer to normal states and more dispersed values refer to less preferable states, a situation that is not unusual in medical settings (e.g. body temperature, blood pressure. The goal of our study was to develop a theoretical and practical method for polychotomization. Methods We used the overall discrimination index C, introduced by Harrel, as a measure of the predictive ability of an independent regressor variable and derived a method for polychotomization mathematically. Since the naïve application of our method, like some existing methods, gives rise to positive bias, we developed a parametric method that minimizes this bias and assessed its performance by the use of Monte Carlo simulation. Results The overall C is closely related to the area under the ROC curve and the produced di(polychotomized variable's predictive performance is comparable to the original continuous variable. The simulation shows that the parametric method is essentially unbiased for both the estimates of performance and the cutoff points. Application of our method to the predictor variables of a previous study on rhabdomyolysis shows that it can be used to make probability profile tables that are applicable to the diagnosis or prognosis of individual patient status. Conclusion We propose a polychotomization (including dichotomization method for independent continuous variables in regression models based on the overall discrimination index C and clarified its meaning mathematically. To avoid positive bias in

  1. Good practices for quantitative bias analysis.

    Science.gov (United States)

    Lash, Timothy L; Fox, Matthew P; MacLehose, Richard F; Maldonado, George; McCandless, Lawrence C; Greenland, Sander

    2014-12-01

    Quantitative bias analysis serves several objectives in epidemiological research. First, it provides a quantitative estimate of the direction, magnitude and uncertainty arising from systematic errors. Second, the acts of identifying sources of systematic error, writing down models to quantify them, assigning values to the bias parameters and interpreting the results combat the human tendency towards overconfidence in research results, syntheses and critiques and the inferences that rest upon them. Finally, by suggesting aspects that dominate uncertainty in a particular research result or topic area, bias analysis can guide efficient allocation of sparse research resources. The fundamental methods of bias analyses have been known for decades, and there have been calls for more widespread use for nearly as long. There was a time when some believed that bias analyses were rarely undertaken because the methods were not widely known and because automated computing tools were not readily available to implement the methods. These shortcomings have been largely resolved. We must, therefore, contemplate other barriers to implementation. One possibility is that practitioners avoid the analyses because they lack confidence in the practice of bias analysis. The purpose of this paper is therefore to describe what we view as good practices for applying quantitative bias analysis to epidemiological data, directed towards those familiar with the methods. We focus on answering questions often posed to those of us who advocate incorporation of bias analysis methods into teaching and research. These include the following. When is bias analysis practical and productive? How does one select the biases that ought to be addressed? How does one select a method to model biases? How does one assign values to the parameters of a bias model? How does one present and interpret a bias analysis?. We hope that our guide to good practices for conducting and presenting bias analyses will encourage

  2. Polynomial regression analysis and significance test of the regression function

    International Nuclear Information System (INIS)

    Gao Zhengming; Zhao Juan; He Shengping

    2012-01-01

    In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)

  3. Ensemble stacking mitigates biases in inference of synaptic connectivity

    Directory of Open Access Journals (Sweden)

    Brendan Chambers

    2018-03-01

    Full Text Available A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches. Mapping the routing of spikes through local circuitry is crucial for understanding neocortical computation. Under appropriate experimental conditions, these maps can be used to infer likely patterns of synaptic recruitment, linking activity to underlying anatomical connections. Such inferences help to reveal the synaptic implementation of population dynamics and computation. We compare a number of standard functional measures to infer underlying connectivity. We find that regularization impacts measures

  4. Regression analysis of censored data using pseudo-observations

    DEFF Research Database (Denmark)

    Parner, Erik T.; Andersen, Per Kragh

    2010-01-01

    We draw upon a series of articles in which a method based on pseu- dovalues is proposed for direct regression modeling of the survival function, the restricted mean, and the cumulative incidence function in competing risks with right-censored data. The models, once the pseudovalues have been...... computed, can be fit using standard generalized estimating equation software. Here we present Stata procedures for computing these pseudo-observations. An example from a bone marrow transplantation study is used to illustrate the method....

  5. Geographic bias related to geocoding in epidemiologic studies

    Directory of Open Access Journals (Sweden)

    Siadaty Mir

    2005-11-01

    Full Text Available Abstract Background This article describes geographic bias in GIS analyses with unrepresentative data owing to missing geocodes, using as an example a spatial analysis of prostate cancer incidence among whites and African Americans in Virginia, 1990–1999. Statistical tests for clustering were performed and such clusters mapped. The patterns of missing census tract identifiers for the cases were examined by generalized linear regression models. Results The county of residency for all cases was known, and 26,338 (74% of these cases were geocoded successfully to census tracts. Cluster maps showed patterns that appeared markedly different, depending upon whether one used all cases or those geocoded to the census tract. Multivariate regression analysis showed that, in the most rural counties (where the missing data were concentrated, the percent of a county's population over age 64 and with less than a high school education were both independently associated with a higher percent of missing geocodes. Conclusion We found statistically significant pattern differences resulting from spatially non-random differences in geocoding completeness across Virginia. Appropriate interpretation of maps, therefore, requires an understanding of this phenomenon, which we call "cartographic confounding."

  6. Hindsight bias and outcome bias in the social construction of medical negligence: a review.

    Science.gov (United States)

    Hugh, Thomas B; Dekker, Sidney W A

    2009-05-01

    Medical negligence has been the subject of much public debate in recent decades. Although the steep increase in the frequency and size of claims against doctors at the end of the last century appears to have plateaued, in Australia at least, medical indemnity costs and consequences are still a matter of concern for doctors, medical defence organisations and governments in most developed countries. Imprecision in the legal definition of negligence opens the possibility that judgments of this issue at several levels may be subject to hindsight and outcome bias. Hindsight bias relates to the probability of an adverse event perceived by a retrospective observer ("I would have known it was going to happen"), while outcome bias is a largely subconscious cognitive distortion produced by the observer's knowledge of the adverse outcome. This review examines the relevant legal, medical, psychological and sociological literature on the operation of these pervasive and universal biases in the retrospective evaluation of adverse events. A finding of medical negligence is essentially an after-the-event social construction and is invariably affected by hindsight bias and knowledge of the adverse outcome. Such biases obviously pose a threat to the fairness of judgments. A number of debiasing strategies have been suggested but are relatively ineffective because of the universality and strength of these biases and the inherent difficulty of concealing from expert witnesses knowledge of the outcome. Education about the effect of the biases is therefore important for lawyers, medical expert witnesses and the judiciary.

  7. Photovoltaic Bias Generator

    Science.gov (United States)

    2018-02-01

    Department of the Army position unless so designated by other authorized documents. Citation of manufacturer’s or trade names does not constitute an... Interior view of the photovoltaic bias generator showing wrapped-wire side of circuit board...3 Fig. 4 Interior view of the photovoltaic bias generator showing component side of circuit board

  8. 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...

  9. A comparison of the performances of an artificial neural network and a regression model for GFR estimation.

    Science.gov (United States)

    Liu, Xun; Li, Ning-shan; Lv, Lin-sheng; Huang, Jian-hua; Tang, Hua; Chen, Jin-xia; Ma, Hui-juan; Wu, Xiao-ming; Lou, Tan-qi

    2013-12-01

    Accurate estimation of glomerular filtration rate (GFR) is important in clinical practice. Current models derived from regression are limited by the imprecision of GFR estimates. We hypothesized that an artificial neural network (ANN) might improve the precision of GFR estimates. A study of diagnostic test accuracy. 1,230 patients with chronic kidney disease were enrolled, including the development cohort (n=581), internal validation cohort (n=278), and external validation cohort (n=371). Estimated GFR (eGFR) using a new ANN model and a new regression model using age, sex, and standardized serum creatinine level derived in the development and internal validation cohort, and the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) 2009 creatinine equation. Measured GFR (mGFR). GFR was measured using a diethylenetriaminepentaacetic acid renal dynamic imaging method. Serum creatinine was measured with an enzymatic method traceable to isotope-dilution mass spectrometry. In the external validation cohort, mean mGFR was 49±27 (SD) mL/min/1.73 m2 and biases (median difference between mGFR and eGFR) for the CKD-EPI, new regression, and new ANN models were 0.4, 1.5, and -0.5 mL/min/1.73 m2, respectively (P30% from mGFR) were 50.9%, 77.4%, and 78.7%, respectively (Psource of systematic bias in comparisons of new models to CKD-EPI, and both the derivation and validation cohorts consisted of a group of patients who were referred to the same institution. An ANN model using 3 variables did not perform better than a new regression model. Whether ANN can improve GFR estimation using more variables requires further investigation. Copyright © 2013 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

  10. Scaling Flux Tower Observations of Sensible Heat Flux Using Weighted Area-to-Area Regression Kriging

    Directory of Open Access Journals (Sweden)

    Maogui Hu

    2015-07-01

    Full Text Available Sensible heat flux (H plays an important role in characterizations of land surface water and heat balance. There are various types of H measurement methods that depend on observation scale, from local-area-scale eddy covariance (EC to regional-scale large aperture scintillometer (LAS and remote sensing (RS products. However, methods of converting one H scale to another to validate RS products are still open for question. A previous area-to-area regression kriging-based scaling method performed well in converting EC-scale H to LAS-scale H. However, the method does not consider the path-weighting function in the EC- to LAS-scale kriging with the regression residue, which inevitably brought about a bias estimation. In this study, a weighted area-to-area regression kriging (WATA RK model is proposed to convert EC-scale H to LAS-scale H. It involves path-weighting functions of EC and LAS source areas in both regression and area kriging stages. Results show that WATA RK outperforms traditional methods in most cases, improving estimation accuracy. The method is considered to provide an efficient validation of RS H flux products.

  11. A procedure to correct proxy-reported weight in the National Health Interview Survey, 1976–2002

    Directory of Open Access Journals (Sweden)

    Utz Rebecca L

    2009-01-01

    Full Text Available Abstract Background Data from the National Health Interview Survey (NHIS show a larger-than-expected increase in mean BMI between 1996 and 1997. Proxy-reports of height and weight were discontinued as part of the 1997 NHIS redesign, suggesting that the sharp increase between 1996 and 1997 may be artifactual. Methods We merged NHIS data from 1976–2002 into a single database consisting of approximately 1.7 million adults aged 18 and over. The analysis consisted of two parts: First, we estimated the magnitude of BMI differences by reporting status (i.e., self-reported versus proxy-reported height and weight. Second, we developed a procedure to correct biases in BMI introduced by reporting status. Results Our analyses confirmed that proxy-reports of weight tended to be biased downward, with the degree of bias varying by race, sex, and other characteristics. We developed a correction procedure to minimize BMI underestimation associated with proxy-reporting, substantially reducing the larger-than-expected increase found in NHIS data between 1996 and 1997. Conclusion It is imperative that researchers who use reported estimates of height and weight think carefully about flaws in their data and how existing correction procedures might fail to account for them. The development of this particular correction procedure represents an important step toward improving the quality of BMI estimates in a widely used source of epidemiologic data.

  12. Sparse Regression by Projection and Sparse Discriminant Analysis

    KAUST Repository

    Qi, Xin

    2015-04-03

    © 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

  13. Higher cortisol levels at diurnal trough predict greater attentional bias towards threat in healthy young adults.

    Science.gov (United States)

    Hakamata, Yuko; Izawa, Shuhei; Sato, Eisuke; Komi, Shotaro; Murayama, Norio; Moriguchi, Yoshiya; Hanakawa, Takashi; Inoue, Yusuke; Tagaya, Hirokuni

    2013-11-01

    Attentional bias (AB), selective information processing towards threat, can exacerbate anxiety and depression. Despite growing interest, physiological determinants of AB are yet to be understood. We examined whether stress hormone cortisol and its diurnal variation pattern contribute to AB. Eighty-seven healthy young adults underwent assessments for AB, anxious personality traits, depressive symptoms, and attentional function. Salivary cortisol was collected at three time points daily (at awakening, 30 min after awakening, and bedtime) for 2 consecutive days. We performed: (1) multiple regression analysis to examine the relationships between AB and the other measures and (2) analysis of variance (ANOVA) between groups with different cortisol variation patterns for the other measures. Multiple regression analysis revealed that higher cortisol levels at bedtime (pattention and cortisol measurement at three time points daily. We showed that higher cortisol levels at bedtime and blunted cortisol variation are associated with greater AB. Individuals who have higher cortisol levels at diurnal trough might be at risk of clinical anxiety or depression but could also derive more benefits from the attentional-bias-modification program. © 2013 Elsevier B.V. All rights reserved.

  14. Blinding in trials of interventional procedures is possible and worthwhile [version 2; referees: 3 approved

    Directory of Open Access Journals (Sweden)

    Karolina Wartolowska

    2018-01-01

    Full Text Available In this paper, we use evidence from our earlier review of surgical randomised controlled trials with a placebo arm to show that blinding in trials of interventional procedures is feasible. We give examples of ingenious strategies that have been used to simulate the active procedure and to make the placebo control indistinguishable from the active treatment. We discuss why it is important to blind of patients, assessors, and caregivers and what types of bias that may occur in interventional trials. Finally, we describe the benefits of blinding, from the obvious ones such as avoiding bias, as well as less evident benefits such as avoiding patient drop out in the control arm.

  15. Assessment of biases in MODIS surface reflectance due to Lambertian approximation

    Energy Technology Data Exchange (ETDEWEB)

    Cook, Robert B [ORNL; SanthanaVannan, Suresh K [ORNL

    2010-08-01

    Using MODIS data and the AERONET-based Surface Reflectance Validation Network (ASRVN), this work studies errors of MODIS atmospheric correction caused by the Lambertian approximation. On one hand, this approximation greatly simplifies the radiative transfer model, reduces the size of the look-up tables, and makes operational algorithm faster. On the other hand, uncompensated atmospheric scattering caused by Lambertian model systematically biases the results. For example, for a typical bowl-shaped bidirectional reflectance distribution function (BRDF), the derived reflectance is underestimated at high solar or view zenith angles, where BRDF is high, and is overestimated at low zenith angles where BRDF is low. The magnitude of biases grows with the amount of scattering in the atmosphere, i.e., at shorter wavelengths and at higher aerosol concentration. The slope of regression of Lambertian surface reflectance vs. ASRVN bidirectional reflectance factor (BRF) is about 0.85 in the red and 0.6 in the green bands. This error propagates into the MODIS BRDF/albedo algorithm, slightly reducing the magnitude of overall reflectance and anisotropy of BRDF. This results in a small negative bias of spectral surface albedo. An assessment for the GSFC (Greenbelt, USA) validation site shows the albedo reduction by 0.004 in the near infrared, 0.005 in the red, and 0.008 in the green MODIS bands.

  16. Quantile Regression Methods

    DEFF Research Database (Denmark)

    Fitzenberger, Bernd; Wilke, Ralf Andreas

    2015-01-01

    if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...

  17. Weight bias internalization in treatment-seeking overweight adults: Psychometric validation and associations with self-esteem, body image, and mood symptoms.

    Science.gov (United States)

    Durso, Laura E; Latner, Janet D; Ciao, Anna C

    2016-04-01

    Internalized weight bias has been previously associated with impairments in eating behaviors, body image, and psychological functioning. The present study explored the psychological correlates and psychometric properties of the Weight Bias Internalization Scale (WBIS) among overweight adults enrolled in a behavioral weight loss program. Questionnaires assessing internalized weight bias, anti-fat attitudes, self-esteem, body image concern, and mood symptoms were administered to 90 obese or overweight men and women between the ages of 21 and 73. Reliability statistics suggested revisions to the WBIS. The resulting 9-item scale was shown to be positively associated with body image concern, depressive symptoms, and stress, and negatively associated with self-esteem. Multiple linear regression models demonstrated that WBIS scores were significant and independent predictors of body image concern, self-esteem, and depressive symptoms. These results support the use of the revised 9-item WBIS in treatment-seeking samples as a reliable and valid measure of internalized weight bias. Copyright © 2016. Published by Elsevier Ltd.

  18. Estimating the Counterfactual Impact of Conservation Programs on Land Cover Outcomes: The Role of Matching and Panel Regression Techniques

    Science.gov (United States)

    Jones, Kelly W.; Lewis, David J.

    2015-01-01

    Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented—from protected areas to payments for ecosystem services (PES)—to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing ‘matching’ to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods—an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1) matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2) fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators—due to the presence of unobservable bias—that lead to differences in conclusions about effectiveness. The Ecuador case

  19. Computer software for linear and nonlinear regression in organic NMR

    International Nuclear Information System (INIS)

    Canto, Eduardo Leite do; Rittner, Roberto

    1991-01-01

    Calculation involving two variable linear regressions, require specific procedures generally not familiar to chemist. For attending the necessity of fast and efficient handling of NMR data, a self explained and Pc portable software has been developed, which allows user to produce and use diskette recorded tables, containing chemical shift or any other substituent physical-chemical measurements and constants (σ T , σ o R , E s , ...)

  20. Shifting effects in randomised controlled trials of complex interventions: a new kind of performance bias?

    Science.gov (United States)

    Gold, C; Erkkilä, J; Crawford, M J

    2012-11-01

    Randomised controlled trials (RCTs) aim to provide unbiased estimates of treatment effects. However, the process of implementing trial procedures may have an impact on the performance of complex interventions that rely strongly on the intuition and confidence of therapists. We aimed to examine whether shifting effects over the recruitment period can be observed that might indicate such impact. Three RCTs investigating music therapy vs. standard care were included. The intervention was performed by experienced therapists and based on established methods. We examined outcomes of participants graphically, analysed cumulative effects and tested for differences between first vs. later participants. We tested for potential confounding population shifts through multiple regression models. Cumulative differences suggested trends over the recruitment period. Effect sizes tended to be less favourable among the first participants than later participants. In one study, effects even changed direction. Age, gender and baseline severity did not account for these shifting effects. Some trials of complex interventions have shifting effects over the recruitment period that cannot be explained by therapist experience or shifting demographics. Replication and further research should aim to find out which interventions and trial designs are most vulnerable to this new kind of performance bias. © 2012 John Wiley & Sons A/S.

  1. Toward a synthesis of cognitive biases: how noisy information processing can bias human decision making.

    Science.gov (United States)

    Hilbert, Martin

    2012-03-01

    A single coherent framework is proposed to synthesize long-standing research on 8 seemingly unrelated cognitive decision-making biases. During the past 6 decades, hundreds of empirical studies have resulted in a variety of rules of thumb that specify how humans systematically deviate from what is normatively expected from their decisions. Several complementary generative mechanisms have been proposed to explain those cognitive biases. Here it is suggested that (at least) 8 of these empirically detected decision-making biases can be produced by simply assuming noisy deviations in the memory-based information processes that convert objective evidence (observations) into subjective estimates (decisions). An integrative framework is presented to show how similar noise-based mechanisms can lead to conservatism, the Bayesian likelihood bias, illusory correlations, biased self-other placement, subadditivity, exaggerated expectation, the confidence bias, and the hard-easy effect. Analytical tools from information theory are used to explore the nature and limitations that characterize such information processes for binary and multiary decision-making exercises. The ensuing synthesis offers formal mathematical definitions of the biases and their underlying generative mechanism, which permits a consolidated analysis of how they are related. This synthesis contributes to the larger goal of creating a coherent picture that explains the relations among the myriad of seemingly unrelated biases and their potential psychological generative mechanisms. Limitations and research questions are discussed.

  2. The global prevalence and correlates of skin bleaching: a meta-analysis and meta-regression analysis.

    Science.gov (United States)

    Sagoe, Dominic; Pallesen, Ståle; Dlova, Ncoza C; Lartey, Margaret; Ezzedine, Khaled; Dadzie, Ophelia

    2018-06-11

    To estimate and investigate the global lifetime prevalence and correlates of skin bleaching. A meta-analysis and meta-regression analysis was performed based on a systematic and comprehensive literature search conducted in Google Scholar, ISI Web of Science, ProQuest, PsycNET, PubMed, and other relevant websites and reference lists. A total of 68 studies (67,665 participants) providing original data on the lifetime prevalence of skin bleaching were included. Publication bias was corrected using the trim and fill procedure. The pooled (imputed) lifetime prevalence of skin bleaching was 27.7% (95% CI: 19.6-37.5, I 2  = 99.6, P < 0.01). The highest significant prevalences were associated with: males (28.0%), topical corticosteroid use (51.8%), Africa (27.1%), persons aged ≤30 years (55.9%), individuals with only primary school education (31.6%), urban or semiurban residents (74.9%), patients (21.3%), data from 2010-2017 (26.8%), dermatological evaluation and testing-based assessment (24.9%), random sampling methods (29.2%), and moderate quality studies (32.3%). The proportion of females in study samples was significantly related to skin bleaching prevalence. Despite some limitations, our results indicate that the practice of skin bleaching is a serious global public health issue that should be addressed through appropriate public health interventions. © 2018 The International Society of Dermatology.

  3. Regression Model to Predict Global Solar Irradiance in Malaysia

    Directory of Open Access Journals (Sweden)

    Hairuniza Ahmed Kutty

    2015-01-01

    Full Text Available A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE, mean bias error (MBE, and the coefficient of determination (R2 with other models available from literature studies. Seven models based on single parameters (PM1 to PM7 and five multiple-parameter models (PM7 to PM12 are proposed. The new models perform well, with RMSE ranging from 0.429% to 1.774%, R2 ranging from 0.942 to 0.992, and MBE ranging from −0.1571% to 0.6025%. In general, cloud cover significantly affects the estimation of global solar irradiance. However, cloud cover in Malaysia lacks sufficient influence when included into multiple-parameter models although it performs fairly well in single-parameter prediction models.

  4. Geographically weighted regression based methods for merging satellite and gauge precipitation

    Science.gov (United States)

    Chao, Lijun; Zhang, Ke; Li, Zhijia; Zhu, Yuelong; Wang, Jingfeng; Yu, Zhongbo

    2018-03-01

    Real-time precipitation data with high spatiotemporal resolutions are crucial for accurate hydrological forecasting. To improve the spatial resolution and quality of satellite precipitation, a three-step satellite and gauge precipitation merging method was formulated in this study: (1) bilinear interpolation is first applied to downscale coarser satellite precipitation to a finer resolution (PS); (2) the (mixed) geographically weighted regression methods coupled with a weighting function are then used to estimate biases of PS as functions of gauge observations (PO) and PS; and (3) biases of PS are finally corrected to produce a merged precipitation product. Based on the above framework, eight algorithms, a combination of two geographically weighted regression methods and four weighting functions, are developed to merge CMORPH (CPC MORPHing technique) precipitation with station observations on a daily scale in the Ziwuhe Basin of China. The geographical variables (elevation, slope, aspect, surface roughness, and distance to the coastline) and a meteorological variable (wind speed) were used for merging precipitation to avoid the artificial spatial autocorrelation resulting from traditional interpolation methods. The results show that the combination of the MGWR and BI-square function (MGWR-BI) has the best performance (R = 0.863 and RMSE = 7.273 mm/day) among the eight algorithms. The MGWR-BI algorithm was then applied to produce hourly merged precipitation product. Compared to the original CMORPH product (R = 0.208 and RMSE = 1.208 mm/hr), the quality of the merged data is significantly higher (R = 0.724 and RMSE = 0.706 mm/hr). The developed merging method not only improves the spatial resolution and quality of the satellite product but also is easy to implement, which is valuable for hydrological modeling and other applications.

  5. Bias and spread in extreme value theory measurements of probability of error

    Science.gov (United States)

    Smith, J. G.

    1972-01-01

    Extreme value theory is examined to explain the cause of the bias and spread in performance of communications systems characterized by low bit rates and high data reliability requirements, for cases in which underlying noise is Gaussian or perturbed Gaussian. Experimental verification is presented and procedures that minimize these effects are suggested. Even under these conditions, however, extreme value theory test results are not particularly more significant than bit error rate tests.

  6. Evaluating anemometer drift: A statistical approach to correct biases in wind speed measurement

    Science.gov (United States)

    Azorin-Molina, Cesar; Asin, Jesus; McVicar, Tim R.; Minola, Lorenzo; Lopez-Moreno, Juan I.; Vicente-Serrano, Sergio M.; Chen, Deliang

    2018-05-01

    Recent studies on observed wind variability have revealed a decline (termed "stilling") of near-surface wind speed during the last 30-50 years over many mid-latitude terrestrial regions, particularly in the Northern Hemisphere. The well-known impact of cup anemometer drift (i.e., wear on the bearings) on the observed weakening of wind speed has been mentioned as a potential contributor to the declining trend. However, to date, no research has quantified its contribution to stilling based on measurements, which is most likely due to lack of quantification of the ageing effect. In this study, a 3-year field experiment (2014-2016) with 10-minute paired wind speed measurements from one new and one malfunctioned (i.e., old bearings) SEAC SV5 cup anemometer which has been used by the Spanish Meteorological Agency in automatic weather stations since mid-1980s, was developed for assessing for the first time the role of anemometer drift on wind speed measurement. The results showed a statistical significant impact of anemometer drift on wind speed measurements, with the old anemometer measuring lower wind speeds than the new one. Biases show a marked temporal pattern and clear dependency on wind speed, with both weak and strong winds causing significant biases. This pioneering quantification of biases has allowed us to define two regression models that correct up to 37% of the artificial bias in wind speed due to measurement with an old anemometer.

  7. Characterization procedures for double-sided silicon microstrip detectors

    Energy Technology Data Exchange (ETDEWEB)

    Bruner, N.L. [New Mexico Univ., Albuquerque, NM (United States). New Mexico Center for Particle Phys.; Frautschi, M.A. [New Mexico Univ., Albuquerque, NM (United States). New Mexico Center for Particle Phys.; Hoeferkamp, M.R. [New Mexico Univ., Albuquerque, NM (United States). New Mexico Center for Particle Phys.; Seidel, S.C. [New Mexico Univ., Albuquerque, NM (United States). New Mexico Center for Particle Phys.

    1995-08-15

    Since double-sided silicon microstrip detectors are still evolving technologically and are not yet commercially available, they require extensive electrical evaluation by the user to ensure they were manufactured to specifications. In addition, measurements must be performed to determine detector operating conditions. Procedures for measuring the following quantities are described: - Leakage current, - Depletion voltage, - Bias resistance, - Interstrip resistance, - Coupling capacitance, - Coupling capacitor breakdown voltage. (orig.).

  8. Characterization procedures for double-sided silicon microstrip detectors

    International Nuclear Information System (INIS)

    Bruner, N.L.; Frautschi, M.A.; Hoeferkamp, M.R.; Seidel, S.C.

    1995-01-01

    Since double-sided silicon microstrip detectors are still evolving technologically and are not yet commercially available, they require extensive electrical evaluation by the user to ensure they were manufactured to specifications. In addition, measurements must be performed to determine detector operating conditions. Procedures for measuring the following quantities are described: - Leakage current, - Depletion voltage, - Bias resistance, - Interstrip resistance, - Coupling capacitance, - Coupling capacitor breakdown voltage. (orig.)

  9. Mood-congruent attention and memory bias in dysphoria: Exploring the coherence among information-processing biases.

    Science.gov (United States)

    Koster, Ernst H W; De Raedt, Rudi; Leyman, Lemke; De Lissnyder, Evi

    2010-03-01

    Recent studies indicate that depression is characterized by mood-congruent attention bias at later stages of information-processing. Moreover, depression has been associated with enhanced recall of negative information. The present study tested the coherence between attention and memory bias in dysphoria. Stable dysphoric (n = 41) and non-dysphoric (n = 41) undergraduates first performed a spatial cueing task that included negative, positive, and neutral words. Words were presented for 250 ms under conditions that allowed or prevented elaborate processing. Memory for the words presented in the cueing task was tested using incidental free recall. Dysphoric individuals exhibited an attention bias for negative words in the condition that allowed elaborate processing, with the attention bias for negative words predicting free recall of negative words. Results demonstrate the coherence of attention and memory bias in dysphoric individuals and provide suggestions on the influence of attention bias on further processing of negative material. 2009 Elsevier Ltd. All rights reserved.

  10. Capital adjustment cost and bias in income based dynamic panel models with fixed effects

    OpenAIRE

    Yoseph Yilma Getachew; Keshab Bhattarai; Parantap Basu

    2012-01-01

    The fixed effects (FE) estimator of "conditional convergence" in income based dynamic panel models could be biased downward when capital adjustment cost is present. Such a capital adjustment cost means a rising marginal cost of investment which could slow down the convergence. The standard FE regression fails to take into account of this capital adjustment cost and thus it could overestimate the rate of convergence. Using a Ramsey model with long-run adjustment cost of capital, we characteriz...

  11. Estimating Climatological Bias Errors for the Global Precipitation Climatology Project (GPCP)

    Science.gov (United States)

    Adler, Robert; Gu, Guojun; Huffman, George

    2012-01-01

    A procedure is described to estimate bias errors for mean precipitation by using multiple estimates from different algorithms, satellite sources, and merged products. The Global Precipitation Climatology Project (GPCP) monthly product is used as a base precipitation estimate, with other input products included when they are within +/- 50% of the GPCP estimates on a zonal-mean basis (ocean and land separately). The standard deviation s of the included products is then taken to be the estimated systematic, or bias, error. The results allow one to examine monthly climatologies and the annual climatology, producing maps of estimated bias errors, zonal-mean errors, and estimated errors over large areas such as ocean and land for both the tropics and the globe. For ocean areas, where there is the largest question as to absolute magnitude of precipitation, the analysis shows spatial variations in the estimated bias errors, indicating areas where one should have more or less confidence in the mean precipitation estimates. In the tropics, relative bias error estimates (s/m, where m is the mean precipitation) over the eastern Pacific Ocean are as large as 20%, as compared with 10%-15% in the western Pacific part of the ITCZ. An examination of latitudinal differences over ocean clearly shows an increase in estimated bias error at higher latitudes, reaching up to 50%. Over land, the error estimates also locate regions of potential problems in the tropics and larger cold-season errors at high latitudes that are due to snow. An empirical technique to area average the gridded errors (s) is described that allows one to make error estimates for arbitrary areas and for the tropics and the globe (land and ocean separately, and combined). Over the tropics this calculation leads to a relative error estimate for tropical land and ocean combined of 7%, which is considered to be an upper bound because of the lack of sign-of-the-error canceling when integrating over different areas with a

  12. Bias in clinical intervention research

    DEFF Research Database (Denmark)

    Gluud, Lise Lotte

    2006-01-01

    Research on bias in clinical trials may help identify some of the reasons why investigators sometimes reach the wrong conclusions about intervention effects. Several quality components for the assessment of bias control have been suggested, but although they seem intrinsically valid, empirical...... evidence is needed to evaluate their effects on the extent and direction of bias. This narrative review summarizes the findings of methodological studies on the influence of bias in clinical trials. A number of methodological studies suggest that lack of adequate randomization in published trial reports...

  13. Stimulus-Driven Attention, Threat Bias, and Sad Bias in Youth with a History of an Anxiety Disorder or Depression.

    Science.gov (United States)

    Sylvester, Chad M; Hudziak, James J; Gaffrey, Michael S; Barch, Deanna M; Luby, Joan L

    2016-02-01

    Attention biases towards threatening and sad stimuli are associated with pediatric anxiety and depression, respectively. The basic cognitive mechanisms associated with attention biases in youth, however, remain unclear. Here, we tested the hypothesis that threat bias (selective attention for threatening versus neutral stimuli) but not sad bias relies on stimulus-driven attention. We collected measures of stimulus-driven attention, threat bias, sad bias, and current clinical symptoms in youth with a history of an anxiety disorder and/or depression (ANX/DEP; n = 40) as well as healthy controls (HC; n = 33). Stimulus-driven attention was measured with a non-emotional spatial orienting task, while threat bias and sad bias were measured at a short time interval (150 ms) with a spatial orienting task using emotional faces and at a longer time interval (500 ms) using a dot-probe task. In ANX/DEP but not HC, early attention bias towards threat was negatively correlated with later attention bias to threat, suggesting that early threat vigilance was associated with later threat avoidance. Across all subjects, stimulus-driven orienting was not correlated with early threat bias but was negatively correlated with later threat bias, indicating that rapid stimulus-driven orienting is linked to later threat avoidance. No parallel relationships were detected for sad bias. Current symptoms of depression but not anxiety were related to decreased stimulus-driven attention. Together, these results are consistent with the hypothesis that threat bias but not sad bias relies on stimulus-driven attention. These results inform the design of attention bias modification programs that aim to reverse threat biases and reduce symptoms associated with pediatric anxiety and depression.

  14. Stimulus-driven attention, threat bias, and sad bias in youth with a history of an anxiety disorder or depression

    Science.gov (United States)

    Sylvester, Chad M.; Hudziak, James J.; Gaffrey, Michael S.; Barch, Deanna M.; Luby, Joan L.

    2015-01-01

    Attention biases towards threatening and sad stimuli are associated with pediatric anxiety and depression, respectively. The basic cognitive mechanisms associated with attention biases in youth, however, remain unclear. Here, we tested the hypothesis that threat bias (selective attention for threatening versus neutral stimuli) but not sad bias relies on stimulus-driven attention. We collected measures of stimulus-driven attention, threat bias, sad bias, and current clinical symptoms in youth with a history of an anxiety disorder and/or depression (ANX/DEP; n=40) as well as healthy controls (HC; n=33). Stimulus-driven attention was measured with a non-emotional spatial orienting task, while threat bias and sad bias were measured at a short time interval (150 ms) with a spatial orienting task using emotional faces and at a longer time interval (500 ms) using a dot-probe task. In ANX/DEP but not HC, early attention bias towards threat was negatively correlated with later attention bias to threat, suggesting that early threat vigilance was associated with later threat avoidance. Across all subjects, stimulus-driven orienting was not correlated with early threat bias but was negatively correlated with later threat bias, indicating that rapid stimulus-driven orienting is linked to later threat avoidance. No parallel relationships were detected for sad bias. Current symptoms of depression but not anxiety were related to decreased stimulus-driven attention. Together, these results are consistent with the hypothesis that threat bias but not sad bias relies on stimulus-driven attention. These results inform the design of attention bias modification programs that aim to reverse threat biases and reduce symptoms associated with pediatric anxiety and depression. PMID:25702927

  15. Case matching and the reduction of selection bias in quasi-experiments: The relative importance of pretest measures of outcome, of unreliable measurement, and of mode of data analysis.

    Science.gov (United States)

    Cook, Thomas D; Steiner, Peter M

    2010-03-01

    In this article, we note the many ontological, epistemological, and methodological similarities between how Campbell and Rubin conceptualize causation. We then explore 3 differences in their written emphases about individual case matching in observational studies. We contend that (a) Campbell places greater emphasis than Rubin on the special role of pretest measures of outcome among matching variables; (b) Campbell is more explicitly concerned with unreliability in the covariates; and (c) for analyzing the outcome, only Rubin emphasizes the advantages of using propensity score over regression methods. To explore how well these 3 factors reduce bias, we reanalyze and review within-study comparisons that contrast experimental and statistically adjusted nonexperimental causal estimates from studies with the same target population and treatment content. In this context, the choice of covariates counts most for reducing selection bias, and the pretest usually plays a special role relative to all the other covariates considered singly. Unreliability in the covariates also influences bias reduction but by less. Furthermore, propensity score and regression methods produce comparable degrees of bias reduction, though these within-study comparisons may not have met the theoretically specified conditions most likely to produce differences due to analytic method.

  16. Regression Phalanxes

    OpenAIRE

    Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.

    2017-01-01

    Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...

  17. Fair lineups are better than biased lineups and showups, but not because they increase underlying discriminability.

    Science.gov (United States)

    Smith, Andrew M; Wells, Gary L; Lindsay, R C L; Penrod, Steven D

    2017-04-01

    Receiver Operating Characteristic (ROC) analysis has recently come in vogue for assessing the underlying discriminability and the applied utility of lineup procedures. Two primary assumptions underlie recommendations that ROC analysis be used to assess the applied utility of lineup procedures: (a) ROC analysis of lineups measures underlying discriminability, and (b) the procedure that produces superior underlying discriminability produces superior applied utility. These same assumptions underlie a recently derived diagnostic-feature detection theory, a theory of discriminability, intended to explain recent patterns observed in ROC comparisons of lineups. We demonstrate, however, that these assumptions are incorrect when ROC analysis is applied to lineups. We also demonstrate that a structural phenomenon of lineups, differential filler siphoning, and not the psychological phenomenon of diagnostic-feature detection, explains why lineups are superior to showups and why fair lineups are superior to biased lineups. In the process of our proofs, we show that computational simulations have assumed, unrealistically, that all witnesses share exactly the same decision criteria. When criterial variance is included in computational models, differential filler siphoning emerges. The result proves dissociation between ROC curves and underlying discriminability: Higher ROC curves for lineups than for showups and for fair than for biased lineups despite no increase in underlying discriminability. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  18. Sampling procedure in a willow plantation for estimation of moisture content

    DEFF Research Database (Denmark)

    Nielsen, Henrik Kofoed; Lærke, Poul Erik; Liu, Na

    2015-01-01

    Heating value and fuel quality of wood is closely connected to moisture content. In this work the variation of moisture content (MC) of short rotation coppice (SRC) willow shoots is described for five clones during one harvesting season. Subsequently an appropriate sampling procedure minimising...... labour costs and sampling uncertainty is proposed, where the MC of a single stem section with the length of 10–50 cm corresponds to the mean shoot moisture content (MSMC) with a bias of maximum 11 g kg−1. This bias can be reduced by selecting the stem section according to the particular clone...

  19. Benefits of being biased!

    Indian Academy of Sciences (India)

    Administrator

    Journal of Genetics, Vol. 83, No. 2, August 2004. Keywords. codon bias; alcohol dehydrogenase; Darwinian ... RESEARCH COMMENTARY. Benefits of being biased! SUTIRTH DEY*. Evolutionary Biology Laboratory, Evolutionary & Organismal Biology Unit,. Jawaharlal Nehru Centre for Advanced Scientific Research,.

  20. Model performance analysis and model validation in logistic regression

    Directory of Open Access Journals (Sweden)

    Rosa Arboretti Giancristofaro

    2007-10-01

    Full Text Available In this paper a new model validation procedure for a logistic regression model is presented. At first, we illustrate a brief review of different techniques of model validation. Next, we define a number of properties required for a model to be considered "good", and a number of quantitative performance measures. Lastly, we describe a methodology for the assessment of the performance of a given model by using an example taken from a management study.

  1. Racial bias in implicit danger associations generalizes to older male targets.

    Directory of Open Access Journals (Sweden)

    Gustav J W Lundberg

    Full Text Available Across two experiments, we examined whether implicit stereotypes linking younger (~28-year-old Black versus White men with violence and criminality extend to older (~68-year-old Black versus White men. In Experiment 1, participants completed a sequential priming task wherein they categorized objects as guns or tools after seeing briefly-presented facial images of men who varied in age (younger versus older and race (Black versus White. In Experiment 2, we used different face primes of younger and older Black and White men, and participants categorized words as 'threatening' or 'safe.' Results consistently revealed robust racial biases in object and word identification: Dangerous objects and words were identified more easily (faster response times, lower error rates, and non-dangerous objects and words were identified less easily, after seeing Black face primes than after seeing White face primes. Process dissociation procedure analyses, which aim to isolate the unique contributions of automatic and controlled processes to task performance, further indicated that these effects were driven entirely by racial biases in automatic processing. In neither experiment did prime age moderate racial bias, suggesting that the implicit danger associations commonly evoked by younger Black versus White men appear to generalize to older Black versus White men.

  2. Causal role for inverse reasoning on obsessive-compulsive symptoms: Preliminary evidence from a cognitive bias modification for interpretation bias study.

    Science.gov (United States)

    Wong, Shiu F; Grisham, Jessica R

    2017-12-01

    The inference-based approach (IBA) is a cognitive account of the genesis and maintenance of obsessive-compulsive disorder (OCD). According to the IBA, individuals with OCD are prone to using inverse reasoning, in which hypothetical causes form the basis of conclusions about reality. Several studies have provided preliminary support for an association between features of the IBA and OCD symptoms. However, there are currently no studies that have investigated the proposed causal relationship of inverse reasoning in OCD. In a non-clinical sample (N = 187), we used an interpretive cognitive bias procedure to train a bias towards using inverse reasoning (n = 64), healthy sensory-based reasoning (n = 65), or a control condition (n = 58). Participants were randomly allocated to these training conditions. This manipulation allowed us to assess whether, consistent with the IBA, inverse reasoning training increased compulsive-like behaviours and self-reported OCD symptoms. Results indicated that compared to a control condition, participants trained in inverse reasoning reported more OCD symptoms and were more avoidant of potentially contaminated objects. Moreover, change in inverse reasoning bias was a small but significant mediator of the relationship between training condition and behavioural avoidance. Conversely, training in a healthy (non-inverse) reasoning style did not have any effect on symptoms or behaviour relative to the control condition. As this study was conducted in a non-clinical sample, we were unable to generalise our findings to a clinical population. Findings generally support the IBA model by providing preliminary evidence of a causal role for inverse reasoning in OCD. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Biases in GNSS-Data Processing

    Science.gov (United States)

    Schaer, S. C.; Dach, R.; Lutz, S.; Meindl, M.; Beutler, G.

    2010-12-01

    Within the Global Positioning System (GPS) traditionally different types of pseudo-range measurements (P-code, C/A-code) are available on the first frequency that are tracked by the receivers with different technologies. For that reason, P1-C1 and P1-P2 Differential Code Biases (DCB) need to be considered in a GPS data processing with a mix of different receiver types. Since the Block IIR-M series of GPS satellites also provide C/A-code on the second frequency, P2-C2 DCB need to be added to the list of biases for maintenance. Potential quarter-cycle biases between different phase observables (specifically L2P and L2C) are another issue. When combining GNSS (currently GPS and GLONASS), careful consideration of inter-system biases (ISB) is indispensable, in particular when an adequate combination of individual GLONASS clock correction results from different sources (using, e.g., different software packages) is intended. Facing the GPS and GLONASS modernization programs and the upcoming GNSS, like the European Galileo and the Chinese Compass, an increasing number of types of biases is expected. The Center for Orbit Determination in Europe (CODE) is monitoring these GPS and GLONASS related biases for a long time based on RINEX files of the tracking network of the International GNSS Service (IGS) and in the frame of the data processing as one of the global analysis centers of the IGS. Within the presentation we give an overview on the stability of the biases based on the monitoring. Biases derived from different sources are compared. Finally, we give an outlook on the potential handling of such biases with the big variety of signals and systems expected in the future.

  4. Bias aware Kalman filters

    DEFF Research Database (Denmark)

    Drecourt, J.-P.; Madsen, H.; Rosbjerg, Dan

    2006-01-01

    This paper reviews two different approaches that have been proposed to tackle the problems of model bias with the Kalman filter: the use of a colored noise model and the implementation of a separate bias filter. Both filters are implemented with and without feedback of the bias into the model state....... The colored noise filter formulation is extended to correct both time correlated and uncorrelated model error components. A more stable version of the separate filter without feedback is presented. The filters are implemented in an ensemble framework using Latin hypercube sampling. The techniques...... are illustrated on a simple one-dimensional groundwater problem. The results show that the presented filters outperform the standard Kalman filter and that the implementations with bias feedback work in more general conditions than the implementations without feedback. 2005 Elsevier Ltd. All rights reserved....

  5. Absence of bias against smokers in access to coronary revascularization after cardiac catheterization

    OpenAIRE

    Cornuz, Jacques; Faris, Peter D.; Galbraith, P. Diane; Knudtson, Merril L.; Ghali, William A.

    2017-01-01

    Objective. Many consider smoking to be a personal choice for which individuals should be held accountable. We assessed whether there is any evidence of bias against smokers in cardiac care decision-making by determining whether smokers were as likely as non-smokers to undergo revascularization procedures after cardiac catheterization. Design. Prospective cohort study. Subjects and setting. All patients undergoing cardiac catheterization in Alberta, Canada. Main measures. Patients were categor...

  6. A rank-based approach for correcting systematic biases in spatial disaggregation of coarse-scale climate simulations

    Science.gov (United States)

    Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

    2017-07-01

    Use of General Circulation Model (GCM) precipitation and evapotranspiration sequences for hydrologic modelling can result in unrealistic simulations due to the coarse scales at which GCMs operate and the systematic biases they contain. The Bias Correction Spatial Disaggregation (BCSD) method is a popular statistical downscaling and bias correction method developed to address this issue. The advantage of BCSD is its ability to reduce biases in the distribution of precipitation totals at the GCM scale and then introduce more realistic variability at finer scales than simpler spatial interpolation schemes. Although BCSD corrects biases at the GCM scale before disaggregation; at finer spatial scales biases are re-introduced by the assumptions made in the spatial disaggregation process. Our study focuses on this limitation of BCSD and proposes a rank-based approach that aims to reduce the spatial disaggregation bias especially for both low and high precipitation extremes. BCSD requires the specification of a multiplicative bias correction anomaly field that represents the ratio of the fine scale precipitation to the disaggregated precipitation. It is shown that there is significant temporal variation in the anomalies, which is masked when a mean anomaly field is used. This can be improved by modelling the anomalies in rank-space. Results from the application of the rank-BCSD procedure improve the match between the distributions of observed and downscaled precipitation at the fine scale compared to the original BCSD approach. Further improvements in the distribution are identified when a scaling correction to preserve mass in the disaggregation process is implemented. An assessment of the approach using a single GCM over Australia shows clear advantages especially in the simulation of particularly low and high downscaled precipitation amounts.

  7. Two-step variable selection in quantile regression models

    Directory of Open Access Journals (Sweden)

    FAN Yali

    2015-06-01

    Full Text Available We propose a two-step variable selection procedure for high dimensional quantile regressions, in which the dimension of the covariates, pn is much larger than the sample size n. In the first step, we perform ℓ1 penalty, and we demonstrate that the first step penalized estimator with the LASSO penalty can reduce the model from an ultra-high dimensional to a model whose size has the same order as that of the true model, and the selected model can cover the true model. The second step excludes the remained irrelevant covariates by applying the adaptive LASSO penalty to the reduced model obtained from the first step. Under some regularity conditions, we show that our procedure enjoys the model selection consistency. We conduct a simulation study and a real data analysis to evaluate the finite sample performance of the proposed approach.

  8. Calibration of semi-stochastic procedure for simulating high-frequency ground motions

    Science.gov (United States)

    Seyhan, Emel; Stewart, Jonathan P.; Graves, Robert

    2013-01-01

    Broadband ground motion simulation procedures typically utilize physics-based modeling at low frequencies, coupled with semi-stochastic procedures at high frequencies. The high-frequency procedure considered here combines deterministic Fourier amplitude spectra (dependent on source, path, and site models) with random phase. Previous work showed that high-frequency intensity measures from this simulation methodology attenuate faster with distance and have lower intra-event dispersion than in empirical equations. We address these issues by increasing crustal damping (Q) to reduce distance attenuation bias and by introducing random site-to-site variations to Fourier amplitudes using a lognormal standard deviation ranging from 0.45 for Mw  100 km).

  9. Administrative bias in South Africa

    Directory of Open Access Journals (Sweden)

    E S Nwauche

    2005-01-01

    Full Text Available This article reviews the interpretation of section 6(2(aii of the Promotion of Administrative Justice Act which makes an administrator “biased or reasonably suspected of bias” a ground of judicial review. In this regard, the paper reviews the determination of administrative bias in South Africa especially highlighting the concept of institutional bias. The paper notes that inspite of the formulation of the bias ground of review the test for administrative bias is the reasonable apprehension test laid down in the case of President of South Africa v South African Rugby Football Union(2 which on close examination is not the same thing. Accordingly the paper urges an alternative interpretation that is based on the reasonable suspicion test enunciated in BTR Industries South Africa (Pty Ltd v Metal and Allied Workers Union and R v Roberts. Within this context, the paper constructs a model for interpreting the bias ground of review that combines the reasonable suspicion test as interpreted in BTR Industries and R v Roberts, the possibility of the waiver of administrative bias, the curative mechanism of administrative appeal as well as some level of judicial review exemplified by the jurisprudence of article 6(1 of the European Convention of Human Rights, especially in the light of the contemplation of the South African Magistrate Court as a jurisdictional route of judicial review.

  10. Modelling of binary logistic regression for obesity among secondary students in a rural area of Kedah

    Science.gov (United States)

    Kamaruddin, Ainur Amira; Ali, Zalila; Noor, Norlida Mohd.; Baharum, Adam; Ahmad, Wan Muhamad Amir W.

    2014-07-01

    Logistic regression analysis examines the influence of various factors on a dichotomous outcome by estimating the probability of the event's occurrence. Logistic regression, also called a logit model, is a statistical procedure used to model dichotomous outcomes. In the logit model the log odds of the dichotomous outcome is modeled as a linear combination of the predictor variables. The log odds ratio in logistic regression provides a description of the probabilistic relationship of the variables and the outcome. In conducting logistic regression, selection procedures are used in selecting important predictor variables, diagnostics are used to check that assumptions are valid which include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers and a test statistic is calculated to determine the aptness of the model. This study used the binary logistic regression model to investigate overweight and obesity among rural secondary school students on the basis of their demographics profile, medical history, diet and lifestyle. The results indicate that overweight and obesity of students are influenced by obesity in family and the interaction between a student's ethnicity and routine meals intake. The odds of a student being overweight and obese are higher for a student having a family history of obesity and for a non-Malay student who frequently takes routine meals as compared to a Malay student.

  11. Advanced statistics: linear regression, part II: multiple linear regression.

    Science.gov (United States)

    Marill, Keith A

    2004-01-01

    The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

  12. Attention, interpretation, and memory biases in subclinical depression: a proof-of-principle test of the combined cognitive biases hypothesis.

    Science.gov (United States)

    Everaert, Jonas; Duyck, Wouter; Koster, Ernst H W

    2014-04-01

    Emotional biases in attention, interpretation, and memory are viewed as important cognitive processes underlying symptoms of depression. To date, there is a limited understanding of the interplay among these processing biases. This study tested the dependence of memory on depression-related biases in attention and interpretation. Subclinically depressed and nondepressed participants completed a computerized version of the scrambled sentences test (measuring interpretation bias) while their eye movements were recorded (measuring attention bias). This task was followed by an incidental free recall test of previously constructed interpretations (measuring memory bias). Path analysis revealed a good fit for the model in which selective orienting of attention was associated with interpretation bias, which in turn was associated with a congruent bias in memory. Also, a good fit was observed for a path model in which biases in the maintenance of attention and interpretation were associated with memory bias. Both path models attained a superior fit compared with path models without the theorized functional relations among processing biases. These findings enhance understanding of how mechanisms of attention and interpretation regulate what is remembered. As such, they offer support for the combined cognitive biases hypothesis or the notion that emotionally biased cognitive processes are not isolated mechanisms but instead influence each other. Implications for theoretical models and emotion regulation across the spectrum of depressive symptoms are discussed.

  13. Nonlinear bias analysis and correction of microwave temperature sounder observations for FY-3C meteorological satellite

    Science.gov (United States)

    Hu, Taiyang; Lv, Rongchuan; Jin, Xu; Li, Hao; Chen, Wenxin

    2018-01-01

    The nonlinear bias analysis and correction of receiving channels in Chinese FY-3C meteorological satellite Microwave Temperature Sounder (MWTS) is a key technology of data assimilation for satellite radiance data. The thermal-vacuum chamber calibration data acquired from the MWTS can be analyzed to evaluate the instrument performance, including radiometric temperature sensitivity, channel nonlinearity and calibration accuracy. Especially, the nonlinearity parameters due to imperfect square-law detectors will be calculated from calibration data and further used to correct the nonlinear bias contributions of microwave receiving channels. Based upon the operational principles and thermalvacuum chamber calibration procedures of MWTS, this paper mainly focuses on the nonlinear bias analysis and correction methods for improving the calibration accuracy of the important instrument onboard FY-3C meteorological satellite, from the perspective of theoretical and experimental studies. Furthermore, a series of original results are presented to demonstrate the feasibility and significance of the methods.

  14. Percentile-Based ETCCDI Temperature Extremes Indices for CMIP5 Model Output: New Results through Semiparametric Quantile Regression Approach

    Science.gov (United States)

    Li, L.; Yang, C.

    2017-12-01

    Climate extremes often manifest as rare events in terms of surface air temperature and precipitation with an annual reoccurrence period. In order to represent the manifold characteristics of climate extremes for monitoring and analysis, the Expert Team on Climate Change Detection and Indices (ETCCDI) had worked out a set of 27 core indices based on daily temperature and precipitation data, describing extreme weather and climate events on an annual basis. The CLIMDEX project (http://www.climdex.org) had produced public domain datasets of such indices for data from a variety of sources, including output from global climate models (GCM) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Among the 27 ETCCDI indices, there are six percentile-based temperature extremes indices that may fall into two groups: exceedance rates (ER) (TN10p, TN90p, TX10p and TX90p) and durations (CSDI and WSDI). Percentiles must be estimated prior to the calculation of the indices, and could more or less be biased by the adopted algorithm. Such biases will in turn be propagated to the final results of indices. The CLIMDEX used an empirical quantile estimator combined with a bootstrap resampling procedure to reduce the inhomogeneity in the annual series of the ER indices. However, there are still some problems remained in the CLIMDEX datasets, namely the overestimated climate variability due to unaccounted autocorrelation in the daily temperature data, seasonally varying biases and inconsistency between algorithms applied to the ER indices and to the duration indices. We now present new results of the six indices through a semiparametric quantile regression approach for the CMIP5 model output. By using the base-period data as a whole and taking seasonality and autocorrelation into account, this approach successfully addressed the aforementioned issues and came out with consistent results. The new datasets cover the historical and three projected (RCP2.6, RCP4.5 and RCP

  15. Boosted beta regression.

    Directory of Open Access Journals (Sweden)

    Matthias Schmid

    Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.

  16. Preferences, country bias, and international trade

    NARCIS (Netherlands)

    S. Roy (Santanu); J.M.A. Viaene (Jean-Marie)

    1998-01-01

    textabstractAnalyzes international trade where consumer preferences exhibit country bias. Why country biases arise; How trade can occur in the presence of country bias; Implication for the pattern of trade and specialization.

  17. Biases in casino betting

    Directory of Open Access Journals (Sweden)

    James Sundali

    2006-07-01

    Full Text Available We examine two departures of individual perceptions of randomness from probability theory: the hot hand and the gambler's fallacy, and their respective opposites. This paper's first contribution is to use data from the field (individuals playing roulette in a casino to demonstrate the existence and impact of these biases that have been previously documented in the lab. Decisions in the field are consistent with biased beliefs, although we observe significant individual heterogeneity in the population. A second contribution is to separately identify these biases within a given individual, then to examine their within-person correlation. We find a positive and significant correlation across individuals between hot hand and gambler's fallacy biases, suggesting a common (root cause of the two related errors. We speculate as to the source of this correlation (locus of control, and suggest future research which could test this speculation.

  18. Regression to Causality : Regression-style presentation influences causal attribution

    DEFF Research Database (Denmark)

    Bordacconi, Mats Joe; Larsen, Martin Vinæs

    2014-01-01

    of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...

  19. An inclusive taxonomy of behavioral biases

    Directory of Open Access Journals (Sweden)

    David Peón

    2017-07-01

    Full Text Available This paper overviews the theoretical and empirical research on behavioral biases and their influence in the literature. To provide a systematic exposition, we present a unified framework that takes the reader through an original taxonomy, based on the reviews of relevant authors in the field. In particular, we establish three broad categories that may be distinguished: heuristics and biases; choices, values and frames; and social factors. We then describe the main biases within each category, and revise the main theoretical and empirical developments, linking each bias with other biases and anomalies that are related to them, according to the literature.

  20. Reference levels in PTCA as a function of procedure complexity

    International Nuclear Information System (INIS)

    Peterzol, A.; Quai, E.; Padovani, R.; Bernardi, G.; Kotre, C. J.; Dowling, A.

    2005-01-01

    The multicentre assessment of a procedure complexity index (CI) for the introduction of reference levels (RLs) in percutaneous transluminal coronary angio-plasties (PTCA) is presented here. PTCAs were investigated based on methodology proposed by Bernardi et al. Multiple linear stepwise regression analysis, including clinical, anatomical and technical factors, was performed to obtain fluoroscopy time predictors. Based on these regression coefficients, a scoring system was defined and CI obtained. CI was used to classify dose values into three groups: low, medium and high complexity procedures, since there was good correlation (r = 0.41; P 2 , and 12, 20 and 27 min for fluoroscopy time, for the three CI groups. (authors)

  1. Information bias and lifetime mortality risks of radiation-induced cancer: Low LET radiation

    International Nuclear Information System (INIS)

    Peterson, L.E.; Schull, W.J.; Davis, B.R.; Buffler, P.A.

    1994-04-01

    Additive and multiplicative models of relative risk were used to measure the effect of cancer misclassification and DS86 random errors on lifetime risk projections in the Life Span Study (LSS) of Hiroshima and Nagasaki atomic bomb survivors. The true number of cancer deaths in each stratum of the cancer mortality cross-classification was estimated using sufficient statistics from the EM algorithm. Average survivor doses in the strata were corrected for DS86 random error (σ=0.45) by use of reduction factors. Poisson regression was used to model the corrected and uncorrected mortality rates with risks in RERF Report 11 (Part 2) and the BEIR-V Report. Bias due to DS86 random error typically ranged from -15% to -30% for both sexes, and all sites and models. The total bias, including diagnostic misclassification, of excess risk of nonleukemia for exposure to 1 Sv from age 18 to 65 under the non-constant relative project model was -37.1% for males and -23.3% for females. Total excess risks of leukemia under the relative projection model were biased -27.1% for males and -43.4% for females. Thus, nonleukemia risks for 1 Sv from ages 18 to 65 (DRREF=2) increased from 1.91%/Sv to 2.68%/Sv among males and from 3.23%/Sv to 4.92%/Sv among females. Leukemia excess risk increased from 0.87%/Sv to 1.10/Sv among males and from 0.73%/Sv to 1.04/Sv among females. Bias was dependent on the gender, site, correction method, exposure profile and projection model considered. Future studies that use LSS data for US nuclear workers may be downwardly biased if lifetime risk projections are not adjusted for random and systematic errors

  2. Information bias and lifetime mortality risks of radiation-induced cancer: Low LET radiation

    Energy Technology Data Exchange (ETDEWEB)

    Peterson, L.E.; Schull, W.J.; Davis, B.R. [Texas Univ., Houston, TX (United States). Health Science Center; Buffler, P.A. [California Univ., Berkeley, CA (United States). School of Public Health

    1994-04-01

    Additive and multiplicative models of relative risk were used to measure the effect of cancer misclassification and DS86 random errors on lifetime risk projections in the Life Span Study (LSS) of Hiroshima and Nagasaki atomic bomb survivors. The true number of cancer deaths in each stratum of the cancer mortality cross-classification was estimated using sufficient statistics from the EM algorithm. Average survivor doses in the strata were corrected for DS86 random error ({sigma}=0.45) by use of reduction factors. Poisson regression was used to model the corrected and uncorrected mortality rates with risks in RERF Report 11 (Part 2) and the BEIR-V Report. Bias due to DS86 random error typically ranged from {minus}15% to {minus}30% for both sexes, and all sites and models. The total bias, including diagnostic misclassification, of excess risk of nonleukemia for exposure to 1 Sv from age 18 to 65 under the non-constant relative project model was {minus}37.1% for males and {minus}23.3% for females. Total excess risks of leukemia under the relative projection model were biased {minus}27.1% for males and {minus}43.4% for females. Thus, nonleukemia risks for 1 Sv from ages 18 to 65 (DRREF=2) increased from 1.91%/Sv to 2.68%/Sv among males and from 3.23%/Sv to 4.92%/Sv among females. Leukemia excess risk increased from 0.87%/Sv to 1.10/Sv among males and from 0.73%/Sv to 1.04/Sv among females. Bias was dependent on the gender, site, correction method, exposure profile and projection model considered. Future studies that use LSS data for US nuclear workers may be downwardly biased if lifetime risk projections are not adjusted for random and systematic errors.

  3. Probing Biased Signaling in Chemokine Receptors

    DEFF Research Database (Denmark)

    Amarandi, Roxana Maria; Hjortø, Gertrud Malene; Rosenkilde, Mette Marie

    2016-01-01

    The chemokine system mediates leukocyte migration during homeostatic and inflammatory processes. Traditionally, it is described as redundant and promiscuous, with a single chemokine ligand binding to different receptors and a single receptor having several ligands. Signaling of chemokine receptors...... of others has been termed signaling bias and can accordingly be grouped into ligand bias, receptor bias, and tissue bias. Bias has so far been broadly overlooked in the process of drug development. The low number of currently approved drugs targeting the chemokine system, as well as the broad range...... of failed clinical trials, reflects the need for a better understanding of the chemokine system. Thus, understanding the character, direction, and consequence of biased signaling in the chemokine system may aid the development of new therapeutics. This review describes experiments to assess G protein...

  4. On Weighted Support Vector Regression

    DEFF Research Database (Denmark)

    Han, Xixuan; Clemmensen, Line Katrine Harder

    2014-01-01

    We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...... shrinks the coefficient of each observation in the estimated functions; thus, it is widely used for minimizing influence of outliers. We propose to additionally add weights to the slack variables in the constraints (CF‐weights) and call the combination of weights the doubly weighted SVR. We illustrate...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...

  5. Self-reporting bias in Chinook salmon sport fisheries in Idaho: implications for roving creel surveys

    Science.gov (United States)

    McCormick, Joshua L.; Quist, Michael C.; Schill, Daniel J.

    2013-01-01

    Self-reporting bias in sport fisheries of Chinook Salmon Oncorhynchus tshawytscha in Idaho was quantified by comparing observed and angler-reported data. A total of 164 observed anglers fished for 541 h and caught 74 Chinook Salmon. Fifty-eight fish were harvested and 16 were released. Anglers reported fishing for 604 h, an overestimate of 63 h. Anglers reported catching 66 fish; four less harvested and four less released fish were reported than observed. A Monte Carlo simulation revealed that when angler-reported data were used, total catch was underestimated by 14–15 fish (19–20%) using the ratio-of-means estimator to calculate mean catch rate. Negative bias was reduced to six fish (8%) when the means-of-ratio estimator was used. Multiple linear regression models to predict reporting bias in time fished had poor predictive value. However, actual time fished and a categorical covariate indicating whether the angler fished continuously during their fishing trip were two variables that were present in all of the top a priori models evaluated. Underreporting of catch and overreporting of time fished by anglers present challenges when managing Chinook Salmon sport fisheries. However, confidence intervals were near target levels and using more liberal definitions of angling when estimating effort in creel surveys may decrease sensitivity to bias in angler-reported data.

  6. Thermophysical Property Estimation by Transient Experiments: The Effect of a Biased Initial Temperature Distribution

    Directory of Open Access Journals (Sweden)

    Federico Scarpa

    2015-01-01

    Full Text Available The identification of thermophysical properties of materials in dynamic experiments can be conveniently performed by the inverse solution of the associated heat conduction problem (IHCP. The inverse technique demands the knowledge of the initial temperature distribution within the material. As only a limited number of temperature sensors (or no sensor at all are arranged inside the test specimen, the knowledge of the initial temperature distribution is affected by some uncertainty. This uncertainty, together with other possible sources of bias in the experimental procedure, will propagate in the estimation process and the accuracy of the reconstructed thermophysical property values could deteriorate. In this work the effect on the estimated thermophysical properties due to errors in the initial temperature distribution is investigated along with a practical method to quantify this effect. Furthermore, a technique for compensating this kind of bias is proposed. The method consists in including the initial temperature distribution among the unknown functions to be estimated. In this way the effect of the initial bias is removed and the accuracy of the identified thermophysical property values is highly improved.

  7. Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis.

    Science.gov (United States)

    Pataky, Todd C; Vanrenterghem, Jos; Robinson, Mark A

    2015-05-01

    Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on 0D statistical procedures, and the non-parametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between 0D and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D non-parametric CIs were qualitatively identical to RFT CIs, and all were very different from 0D CIs. Second, 1D parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, design-dependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one explicitly identifies 0D variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one׳s hypothesis explicitly or implicitly pertains to whole 1D trajectories. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. Bias-field equalizer for bubble memories

    Science.gov (United States)

    Keefe, G. E.

    1977-01-01

    Magnetoresistive Perm-alloy sensor monitors bias field required to maintain bubble memory. Sensor provides error signal that, in turn, corrects magnitude of bias field. Error signal from sensor can be used to control magnitude of bias field in either auxiliary set of bias-field coils around permanent magnet field, or current in small coils used to remagnetize permanent magnet by infrequent, short, high-current pulse or short sequence of pulses.

  9. “Virtual IED sensor” at an rf-biased electrode in low-pressure plasma

    Energy Technology Data Exchange (ETDEWEB)

    Bogdanova, M. A.; Zyryanov, S. M. [Skobeltsyn Institute of Nuclear Physics, Moscow State University, SINP MSU, Moscow (Russian Federation); Faculty of Physics, Moscow State University, MSU, Moscow (Russian Federation); Lopaev, D. V.; Rakhimov, A. T. [Skobeltsyn Institute of Nuclear Physics, Moscow State University, SINP MSU, Moscow (Russian Federation)

    2016-07-15

    Energy distribution and the flux of the ions coming on a surface are considered as the key-parameters in anisotropic plasma etching. Since direct ion energy distribution (IED) measurements at the treated surface during plasma processing are often hardly possible, there is an opportunity for virtual ones. This work is devoted to the possibility of such indirect IED and ion flux measurements at an rf-biased electrode in low-pressure rf plasma by using a “virtual IED sensor” which represents “in-situ” IED calculations on the absolute scale in accordance with a plasma sheath model containing a set of measurable external parameters. The “virtual IED sensor” should also involve some external calibration procedure. Applicability and accuracy of the “virtual IED sensor” are validated for a dual-frequency reactive ion etching (RIE) inductively coupled plasma (ICP) reactor with a capacitively coupled rf-biased electrode. The validation is carried out for heavy (Ar) and light (H{sub 2}) gases under different discharge conditions (different ICP powers, rf-bias frequencies, and voltages). An EQP mass-spectrometer and an rf-compensated Langmuir probe (LP) are used to characterize plasma, while an rf-compensated retarded field energy analyzer (RFEA) is applied to measure IED and ion flux at the rf-biased electrode. Besides, the pulsed selfbias method is used as an external calibration procedure for ion flux estimating at the rf-biased electrode. It is shown that pulsed selfbias method allows calibrating the IED absolute scale quite accurately. It is also shown that the “virtual IED sensor” based on the simplest collisionless sheath model allows reproducing well enough the experimental IEDs at the pressures when the sheath thickness s is less than the ion mean free path λ{sub i} (s < λ{sub i}). At higher pressure (when s > λ{sub i}), the difference between calculated and experimental IEDs due to ion collisions in the sheath is observed in the low

  10. Wide brick tunnel randomization - an unequal allocation procedure that limits the imbalance in treatment totals.

    Science.gov (United States)

    Kuznetsova, Olga M; Tymofyeyev, Yevgen

    2014-04-30

    In open-label studies, partial predictability of permuted block randomization provides potential for selection bias. To lessen the selection bias in two-arm studies with equal allocation, a number of allocation procedures that limit the imbalance in treatment totals at a pre-specified level but do not require the exact balance at the ends of the blocks were developed. In studies with unequal allocation, however, the task of designing a randomization procedure that sets a pre-specified limit on imbalance in group totals is not resolved. Existing allocation procedures either do not preserve the allocation ratio at every allocation or do not include all allocation sequences that comply with the pre-specified imbalance threshold. Kuznetsova and Tymofyeyev described the brick tunnel randomization for studies with unequal allocation that preserves the allocation ratio at every step and, in the two-arm case, includes all sequences that satisfy the smallest possible imbalance threshold. This article introduces wide brick tunnel randomization for studies with unequal allocation that allows all allocation sequences with imbalance not exceeding any pre-specified threshold while preserving the allocation ratio at every step. In open-label studies, allowing a larger imbalance in treatment totals lowers selection bias because of the predictability of treatment assignments. The applications of the technique in two-arm and multi-arm open-label studies with unequal allocation are described. Copyright © 2013 John Wiley & Sons, Ltd.

  11. Introduction to Unconscious Bias

    Science.gov (United States)

    Schmelz, Joan T.

    2010-05-01

    We all have biases, and we are (for the most part) unaware of them. In general, men and women BOTH unconsciously devalue the contributions of women. This can have a detrimental effect on grant proposals, job applications, and performance reviews. Sociology is way ahead of astronomy in these studies. When evaluating identical application packages, male and female University psychology professors preferred 2:1 to hire "Brian” over "Karen” as an assistant professor. When evaluating a more experienced record (at the point of promotion to tenure), reservations were expressed four times more often when the name was female. This unconscious bias has a repeated negative effect on Karen's career. This talk will introduce the concept of unconscious bias and also give recommendations on how to address it using an example for a faculty search committee. The process of eliminating unconscious bias begins with awareness, then moves to policy and practice, and ends with accountability.

  12. Correction of confounding bias in non-randomized studies by appropriate weighting.

    Science.gov (United States)

    Schmoor, Claudia; Gall, Christine; Stampf, Susanne; Graf, Erika

    2011-03-01

    In non-randomized studies, the assessment of a causal effect of treatment or exposure on outcome is hampered by possible confounding. Applying multiple regression models including the effects of treatment and covariates on outcome is the well-known classical approach to adjust for confounding. In recent years other approaches have been promoted. One of them is based on the propensity score and considers the effect of possible confounders on treatment as a relevant criterion for adjustment. Another proposal is based on using an instrumental variable. Here inference relies on a factor, the instrument, which affects treatment but is thought to be otherwise unrelated to outcome, so that it mimics randomization. Each of these approaches can basically be interpreted as a simple reweighting scheme, designed to address confounding. The procedures will be compared with respect to their fundamental properties, namely, which bias they aim to eliminate, which effect they aim to estimate, and which parameter is modelled. We will expand our overview of methods for analysis of non-randomized studies to methods for analysis of randomized controlled trials and show that analyses of both study types may target different effects and different parameters. The considerations will be illustrated using a breast cancer study with a so-called Comprehensive Cohort Study design, including a randomized controlled trial and a non-randomized study in the same patient population as sub-cohorts. This design offers ideal opportunities to discuss and illustrate the properties of the different approaches. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. Comparison of cranial sex determination by discriminant analysis and logistic regression.

    Science.gov (United States)

    Amores-Ampuero, Anabel; Alemán, Inmaculada

    2016-04-05

    Various methods have been proposed for estimating dimorphism. The objective of this study was to compare sex determination results from cranial measurements using discriminant analysis or logistic regression. The study sample comprised 130 individuals (70 males) of known sex, age, and cause of death from San José cemetery in Granada (Spain). Measurements of 19 neurocranial dimensions and 11 splanchnocranial dimensions were subjected to discriminant analysis and logistic regression, and the percentages of correct classification were compared between the sex functions obtained with each method. The discriminant capacity of the selected variables was evaluated with a cross-validation procedure. The percentage accuracy with discriminant analysis was 78.2% for the neurocranium (82.4% in females and 74.6% in males) and 73.7% for the splanchnocranium (79.6% in females and 68.8% in males). These percentages were higher with logistic regression analysis: 85.7% for the neurocranium (in both sexes) and 94.1% for the splanchnocranium (100% in females and 91.7% in males).

  14. Detecting overdispersion in count data: A zero-inflated Poisson regression analysis

    Science.gov (United States)

    Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Nor, Maria Elena; Mohamed, Maryati; Ismail, Norradihah

    2017-09-01

    This study focusing on analysing count data of butterflies communities in Jasin, Melaka. In analysing count dependent variable, the Poisson regression model has been known as a benchmark model for regression analysis. Continuing from the previous literature that used Poisson regression analysis, this study comprising the used of zero-inflated Poisson (ZIP) regression analysis to gain acute precision on analysing the count data of butterfly communities in Jasin, Melaka. On the other hands, Poisson regression should be abandoned in the favour of count data models, which are capable of taking into account the extra zeros explicitly. By far, one of the most popular models include ZIP regression model. The data of butterfly communities which had been called as the number of subjects in this study had been taken in Jasin, Melaka and consisted of 131 number of subjects visits Jasin, Melaka. Since the researchers are considering the number of subjects, this data set consists of five families of butterfly and represent the five variables involve in the analysis which are the types of subjects. Besides, the analysis of ZIP used the SAS procedure of overdispersion in analysing zeros value and the main purpose of continuing the previous study is to compare which models would be better than when exists zero values for the observation of the count data. The analysis used AIC, BIC and Voung test of 5% level significance in order to achieve the objectives. The finding indicates that there is a presence of over-dispersion in analysing zero value. The ZIP regression model is better than Poisson regression model when zero values exist.

  15. Research bias in judgement bias studies : a systematic review of valuation judgement literature

    NARCIS (Netherlands)

    Vincent Gruis; Pim Klamer; Cok Bakker

    2017-01-01

    Valuation judgement bias has been a research topic for several years due to its proclaimed effect on valuation accuracy. However, little is known on the emphasis of literature on judgement bias, with regard to, for instance, research methodologies, research context and robustness of research

  16. Research bias in judgement bias studies : A systematic review of valuation judgement literature

    NARCIS (Netherlands)

    Klamer, Pim; Bakker, C.; Gruis, Vincent

    2017-01-01

    Valuation judgement bias has been a research topic for several years due to its proclaimed effect on valuation accuracy. However, little is known on the emphasis of literature on judgement bias, with regard to, for instance, research methodologies, research context and robustness of research

  17. A New Navigation Satellite Clock Bias Prediction Method Based on Modified Clock-bias Quadratic Polynomial Model

    Science.gov (United States)

    Wang, Y. P.; Lu, Z. P.; Sun, D. S.; Wang, N.

    2016-01-01

    In order to better express the characteristics of satellite clock bias (SCB) and improve SCB prediction precision, this paper proposed a new SCB prediction model which can take physical characteristics of space-borne atomic clock, the cyclic variation, and random part of SCB into consideration. First, the new model employs a quadratic polynomial model with periodic items to fit and extract the trend term and cyclic term of SCB; then based on the characteristics of fitting residuals, a time series ARIMA ~(Auto-Regressive Integrated Moving Average) model is used to model the residuals; eventually, the results from the two models are combined to obtain final SCB prediction values. At last, this paper uses precise SCB data from IGS (International GNSS Service) to conduct prediction tests, and the results show that the proposed model is effective and has better prediction performance compared with the quadratic polynomial model, grey model, and ARIMA model. In addition, the new method can also overcome the insufficiency of the ARIMA model in model recognition and order determination.

  18. A procedure for estimating the dose modifying effect of chemotherapy on radiation response

    International Nuclear Information System (INIS)

    Hao, Y.; Keane, T.

    1994-01-01

    A procedure based on a logistic regression model was used to estimate the dose-modifying effect of chemotherapy on the response of normal tissues to radiation. The DEF in the proposed procedure is expressed as a function of logistic regression coefficients, response levels and values of covariates in the model. The proposed procedure is advantageous as it allows consideration of both the response levels and the values of covariates in calculating the DEF. A plot of the DEF against the response or a covariate describes how the DEF varies with the response levels or the covariate values. Confidence intervals of the DEF were obtained based on the normal approximation of the distribution of the estimated DEF and on a non-parametric Bootstrap method. An example is given to illustrate the proposed procedure. (Author)

  19. Is there a positive bias in false recognition? Evidence from confabulating amnesia patients.

    Science.gov (United States)

    Alkathiri, Nura H; Morris, Robin G; Kopelman, Michael D

    2015-10-01

    Although there is some evidence for a positive emotional bias in the content of confabulations in brain damaged patients, findings have been inconsistent. The present study used the semantic-associates procedure to induce false recall and false recognition in order to examine whether a positive bias would be found in confabulating amnesic patients, relative to non-confabulating amnesic patients and healthy controls. Lists of positive, negative and neutral words were presented in order to induce false recall or false recognition of non-presented (but semantically associated) words. The latter were termed 'critical intrusions'. Thirteen confabulating amnesic patients, 13 non-confabulating amnesic patients and 13 healthy controls were investigated. Confabulating patients falsely recognised a higher proportion of positive (but unrelated) words, compared with non-confabulating patients and healthy controls. No differences were found for recall memory. Signal detection analysis, however, indicated that the positive bias for false recognition memory might reflect weaker memory in the confabulating amnesic group. This suggested that amnesia patients with weaker memory are more likely to confabulate and the content of these confabulations are more likely to be positive. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Management of Industrial Performance Indicators: Regression Analysis and Simulation

    Directory of Open Access Journals (Sweden)

    Walter Roberto Hernandez Vergara

    2017-11-01

    Full Text Available Stochastic methods can be used in problem solving and explanation of natural phenomena through the application of statistical procedures. The article aims to associate the regression analysis and systems simulation, in order to facilitate the practical understanding of data analysis. The algorithms were developed in Microsoft Office Excel software, using statistical techniques such as regression theory, ANOVA and Cholesky Factorization, which made it possible to create models of single and multiple systems with up to five independent variables. For the analysis of these models, the Monte Carlo simulation and analysis of industrial performance indicators were used, resulting in numerical indices that aim to improve the goals’ management for compliance indicators, by identifying systems’ instability, correlation and anomalies. The analytical models presented in the survey indicated satisfactory results with numerous possibilities for industrial and academic applications, as well as the potential for deployment in new analytical techniques.

  1. Some effects of random dose measurement errors on analysis of atomic bomb survivor data

    International Nuclear Information System (INIS)

    Gilbert, E.S.

    1985-01-01

    The effects of random dose measurement errors on analyses of atomic bomb survivor data are described and quantified for several procedures. It is found that the ways in which measurement error is most likely to mislead are through downward bias in the estimated regression coefficients and through distortion of the shape of the dose-response curve. The magnitude of the bias with simple linear regression is evaluated for several dose treatments including the use of grouped and ungrouped data, analyses with and without truncation at 600 rad, and analyses which exclude doses exceeding 200 rad. Limited calculations have also been made for maximum likelihood estimation based on Poisson regression. 16 refs., 6 tabs

  2. Attribution bias and social anxiety in schizophrenia

    Directory of Open Access Journals (Sweden)

    Amelie M. Achim

    2016-06-01

    Full Text Available Studies on attribution biases in schizophrenia have produced mixed results, whereas such biases have been more consistently reported in people with anxiety disorders. Anxiety comorbidities are frequent in schizophrenia, in particular social anxiety disorder, which could influence their patterns of attribution biases. The objective of the present study was thus to determine if individuals with schizophrenia and a comorbid social anxiety disorder (SZ+ show distinct attribution biases as compared with individuals with schizophrenia without social anxiety (SZ− and healthy controls. Attribution biases were assessed with the Internal, Personal, and Situational Attributions Questionnaire in 41 individual with schizophrenia and 41 healthy controls. Results revealed the lack of the normal externalizing bias in SZ+, whereas SZ− did not significantly differ from healthy controls on this dimension. The personalizing bias was not influenced by social anxiety but was in contrast linked with delusions, with a greater personalizing bias in individuals with current delusions. Future studies on attribution biases in schizophrenia should carefully document symptom presentation, including social anxiety.

  3. Assessing probe-specific dye and slide biases in two-color microarray data

    Directory of Open Access Journals (Sweden)

    Goldberg Zelanna

    2008-07-01

    Full Text Available Abstract Background A primary reason for using two-color microarrays is that the use of two samples labeled with different dyes on the same slide, that bind to probes on the same spot, is supposed to adjust for many factors that introduce noise and errors into the analysis. Most users assume that any differences between the dyes can be adjusted out by standard methods of normalization, so that measures such as log ratios on the same slide are reliable measures of comparative expression. However, even after the normalization, there are still probe specific dye and slide variation among the data. We define a method to quantify the amount of the dye-by-probe and slide-by-probe interaction. This serves as a diagnostic, both visual and numeric, of the existence of probe-specific dye bias. We show how this improved the performance of two-color array analysis for arrays for genomic analysis of biological samples ranging from rice to human tissue. Results We develop a procedure for quantifying the extent of probe-specific dye and slide bias in two-color microarrays. The primary output is a graphical diagnostic of the extent of the bias which called ECDF (Empirical Cumulative Distribution Function, though numerical results are also obtained. Conclusion We show that the dye and slide biases were high for human and rice genomic arrays in two gene expression facilities, even after the standard intensity-based normalization, and describe how this diagnostic allowed the problems causing the probe-specific bias to be addressed, and resulted in important improvements in performance. The R package LMGene which contains the method described in this paper has been available to download from Bioconductor.

  4. Effect of vicarious fear learning on children's heart rate responses and attentional bias for novel animals.

    Science.gov (United States)

    Reynolds, Gemma; Field, Andy P; Askew, Chris

    2014-10-01

    Research with children has shown that vicarious learning can result in changes to 2 of Lang's (1968) 3 anxiety response systems: subjective report and behavioral avoidance. The current study extended this research by exploring the effect of vicarious learning on physiological responses (Lang's final response system) and attentional bias. The study used Askew and Field's (2007) vicarious learning procedure and demonstrated fear-related increases in children's cognitive, behavioral, and physiological responses. Cognitive and behavioral changes were retested 1 week and 1 month later, and remained elevated. In addition, a visual search task demonstrated that fear-related vicarious learning creates an attentional bias for novel animals, which is moderated by increases in fear beliefs during learning. The findings demonstrate that vicarious learning leads to lasting changes in all 3 of Lang's anxiety response systems and is sufficient to create attentional bias to threat in children. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  5. Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches

    Science.gov (United States)

    Duarte, Adam; Adams, Michael J.; Peterson, James T.

    2018-01-01

    Monitoring animal populations is central to wildlife and fisheries management, and the use of N-mixture models toward these efforts has markedly increased in recent years. Nevertheless, relatively little work has evaluated estimator performance when basic assumptions are violated. Moreover, diagnostics to identify when bias in parameter estimates from N-mixture models is likely is largely unexplored. We simulated count data sets using 837 combinations of detection probability, number of sample units, number of survey occasions, and type and extent of heterogeneity in abundance or detectability. We fit Poisson N-mixture models to these data, quantified the bias associated with each combination, and evaluated if the parametric bootstrap goodness-of-fit (GOF) test can be used to indicate bias in parameter estimates. We also explored if assumption violations can be diagnosed prior to fitting N-mixture models. In doing so, we propose a new model diagnostic, which we term the quasi-coefficient of variation (QCV). N-mixture models performed well when assumptions were met and detection probabilities were moderate (i.e., ≥0.3), and the performance of the estimator improved with increasing survey occasions and sample units. However, the magnitude of bias in estimated mean abundance with even slight amounts of unmodeled heterogeneity was substantial. The parametric bootstrap GOF test did not perform well as a diagnostic for bias in parameter estimates when detectability and sample sizes were low. The results indicate the QCV is useful to diagnose potential bias and that potential bias associated with unidirectional trends in abundance or detectability can be diagnosed using Poisson regression. This study represents the most thorough assessment to date of assumption violations and diagnostics when fitting N-mixture models using the most commonly implemented error distribution. Unbiased estimates of population state variables are needed to properly inform management decision

  6. Comparison of two-concentration with multi-concentration linear regressions: Retrospective data analysis of multiple regulated LC-MS bioanalytical projects.

    Science.gov (United States)

    Musuku, Adrien; Tan, Aimin; Awaiye, Kayode; Trabelsi, Fethi

    2013-09-01

    Linear calibration is usually performed using eight to ten calibration concentration levels in regulated LC-MS bioanalysis because a minimum of six are specified in regulatory guidelines. However, we have previously reported that two-concentration linear calibration is as reliable as or even better than using multiple concentrations. The purpose of this research is to compare two-concentration with multiple-concentration linear calibration through retrospective data analysis of multiple bioanalytical projects that were conducted in an independent regulated bioanalytical laboratory. A total of 12 bioanalytical projects were randomly selected: two validations and two studies for each of the three most commonly used types of sample extraction methods (protein precipitation, liquid-liquid extraction, solid-phase extraction). When the existing data were retrospectively linearly regressed using only the lowest and the highest concentration levels, no extra batch failure/QC rejection was observed and the differences in accuracy and precision between the original multi-concentration regression and the new two-concentration linear regression are negligible. Specifically, the differences in overall mean apparent bias (square root of mean individual bias squares) are within the ranges of -0.3% to 0.7% and 0.1-0.7% for the validations and studies, respectively. The differences in mean QC concentrations are within the ranges of -0.6% to 1.8% and -0.8% to 2.5% for the validations and studies, respectively. The differences in %CV are within the ranges of -0.7% to 0.9% and -0.3% to 0.6% for the validations and studies, respectively. The average differences in study sample concentrations are within the range of -0.8% to 2.3%. With two-concentration linear regression, an average of 13% of time and cost could have been saved for each batch together with 53% of saving in the lead-in for each project (the preparation of working standard solutions, spiking, and aliquoting). Furthermore

  7. Biased Brownian dynamics for rate constant calculation.

    OpenAIRE

    Zou, G; Skeel, R D; Subramaniam, S

    2000-01-01

    An enhanced sampling method-biased Brownian dynamics-is developed for the calculation of diffusion-limited biomolecular association reaction rates with high energy or entropy barriers. Biased Brownian dynamics introduces a biasing force in addition to the electrostatic force between the reactants, and it associates a probability weight with each trajectory. A simulation loses weight when movement is along the biasing force and gains weight when movement is against the biasing force. The sampl...

  8. Effect of Vicarious Fear Learning on Children’s Heart Rate Responses and Attentional Bias for Novel Animals

    Science.gov (United States)

    2014-01-01

    Research with children has shown that vicarious learning can result in changes to 2 of Lang’s (1968) 3 anxiety response systems: subjective report and behavioral avoidance. The current study extended this research by exploring the effect of vicarious learning on physiological responses (Lang’s final response system) and attentional bias. The study used Askew and Field’s (2007) vicarious learning procedure and demonstrated fear-related increases in children’s cognitive, behavioral, and physiological responses. Cognitive and behavioral changes were retested 1 week and 1 month later, and remained elevated. In addition, a visual search task demonstrated that fear-related vicarious learning creates an attentional bias for novel animals, which is moderated by increases in fear beliefs during learning. The findings demonstrate that vicarious learning leads to lasting changes in all 3 of Lang’s anxiety response systems and is sufficient to create attentional bias to threat in children. PMID:25151521

  9. The coalitional value theory of antigay bias

    NARCIS (Netherlands)

    Winegard, Bo; Reynolds, Tania; Baumeister, Roy F.; Plant, E. Ashby

    2016-01-01

    Research indicates that antigay bias follows a specific pattern (and probably has throughout written history, at least in the West): (a) men evince more antigay bias than women; (b) men who belong to traditionally male coalitions evince more antigay bias than those who do not; (c) antigay bias is

  10. Symmetry as Bias: Rediscovering Special Relativity

    Science.gov (United States)

    Lowry, Michael R.

    1992-01-01

    This paper describes a rational reconstruction of Einstein's discovery of special relativity, validated through an implementation: the Erlanger program. Einstein's discovery of special relativity revolutionized both the content of physics and the research strategy used by theoretical physicists. This research strategy entails a mutual bootstrapping process between a hypothesis space for biases, defined through different postulated symmetries of the universe, and a hypothesis space for physical theories. The invariance principle mutually constrains these two spaces. The invariance principle enables detecting when an evolving physical theory becomes inconsistent with its bias, and also when the biases for theories describing different phenomena are inconsistent. Structural properties of the invariance principle facilitate generating a new bias when an inconsistency is detected. After a new bias is generated. this principle facilitates reformulating the old, inconsistent theory by treating the latter as a limiting approximation. The structural properties of the invariance principle can be suitably generalized to other types of biases to enable primal-dual learning.

  11. Regression analysis of informative current status data with the additive hazards model.

    Science.gov (United States)

    Zhao, Shishun; Hu, Tao; Ma, Ling; Wang, Peijie; Sun, Jianguo

    2015-04-01

    This paper discusses regression analysis of current status failure time data arising from the additive hazards model in the presence of informative censoring. Many methods have been developed for regression analysis of current status data under various regression models if the censoring is noninformative, and also there exists a large literature on parametric analysis of informative current status data in the context of tumorgenicity experiments. In this paper, a semiparametric maximum likelihood estimation procedure is presented and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring time. Furthermore, I-splines are used to approximate the nonparametric functions involved and the asymptotic consistency and normality of the proposed estimators are established. A simulation study is conducted and indicates that the proposed approach works well for practical situations. An illustrative example is also provided.

  12. Bias correction for selecting the minimal-error classifier from many machine learning models.

    Science.gov (United States)

    Ding, Ying; Tang, Shaowu; Liao, Serena G; Jia, Jia; Oesterreich, Steffi; Lin, Yan; Tseng, George C

    2014-11-15

    Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts. In this article, we illustrated the probabilistic framework of the problem and explored the statistical and asymptotic properties. We proposed a new bias correction method based on learning curve fitting by inverse power law (IPL) and compared it with three existing methods: nested cross-validation, weighted mean correction and Tibshirani-Tibshirani procedure. All methods were compared in simulation datasets, five moderate size real datasets and two large breast cancer datasets. The result showed that IPL outperforms the other methods in bias correction with smaller variance, and it has an additional advantage to extrapolate error estimates for larger sample sizes, a practical feature to recommend whether more samples should be recruited to improve the classifier and accuracy. An R package 'MLbias' and all source files are publicly available. tsenglab.biostat.pitt.edu/software.htm. ctseng@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  13. The relationship between weight stigma and eating behavior is explained by weight bias internalization and psychological distress.

    Science.gov (United States)

    O'Brien, Kerry S; Latner, Janet D; Puhl, Rebecca M; Vartanian, Lenny R; Giles, Claudia; Griva, Konstadina; Carter, Adrian

    2016-07-01

    Weight stigma is associated with a range of negative outcomes, including disordered eating, but the psychological mechanisms underlying these associations are not well understood. The present study tested whether the association between weight stigma experiences and disordered eating behaviors (emotional eating, uncontrolled eating, and loss-of-control eating) are mediated by weight bias internalization and psychological distress. Six-hundred and thirty-four undergraduate university students completed an online survey assessing weight stigma, weight bias internalization, psychological distress, disordered eating, along with demographic characteristics (i.e., age, gender, weight status). Statistical analyses found that weight stigma was significantly associated with all measures of disordered eating, and with weight bias internalization and psychological distress. In regression and mediation analyses accounting for age, gender and weight status, weight bias internalization and psychological distress mediated the relationship between weight stigma and disordered eating behavior. Thus, weight bias internalization and psychological distress appear to be important factors underpinning the relationship between weight stigma and disordered eating behaviors, and could be targets for interventions, such as, psychological acceptance and mindfulness therapy, which have been shown to reduce the impact of weight stigma. The evidence for the health consequences resulting from weight stigma is becoming clear. It is important that health and social policy makers are informed of this literature and encouraged develop anti-weight stigma policies for school, work, and medical settings. Copyright © 2016 Elsevier Ltd. All rights reserved.

  14. Anti-Bias Education: Reflections

    Science.gov (United States)

    Derman-Sparks, Louise

    2011-01-01

    It is 30 years since NAEYC published "Anti-Bias Curriculum Tools for Empowering Young Children" (Derman-Sparks & ABC Task Force, 1989). Since then, anti-bias education concepts have become part of the early childhood education (ECE) narrative in the United States and many other countries. It has brought a fresh way of thinking about…

  15. Comparing spatial regression to random forests for large ...

    Science.gov (United States)

    Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. Our primary goal is predicting MMI at over 1.1 million perennial stream reaches across the USA. For spatial regression modeling, we develop two new methods to accommodate large data: (1) a procedure that estimates optimal Box-Cox transformations to linearize covariate relationships; and (2) a computationally efficient covariate selection routine that takes into account spatial autocorrelation. We show that our new methods lead to cross-validated performance similar to random forests, but that there is an advantage for spatial regression when quantifying the uncertainty of the predictions. Simulations are used to clarify advantages for each method. This research investigates different approaches for modeling and mapping national stream condition. We use MMI data from the EPA's National Rivers and Streams Assessment and predictors from StreamCat (Hill et al., 2015). Previous studies have focused on modeling the MMI condition classes (i.e., good, fair, and po

  16. Sampler bias -- Phase 1

    International Nuclear Information System (INIS)

    Blanchard, R.J.

    1995-01-01

    This documents Phase 1 determinations on sampler induced bias for four sampler types used in tank characterization. Each sampler, grab sampler or bottle-on-a-string, auger sampler, sludge sampler and universal sampler, is briefly discussed and their physical limits noted. Phase 2 of this document will define additional testing and analysis to further define Sampler Bias

  17. The PIT-trap-A "model-free" bootstrap procedure for inference about regression models with discrete, multivariate responses.

    Science.gov (United States)

    Warton, David I; Thibaut, Loïc; Wang, Yi Alice

    2017-01-01

    Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of "model-free bootstrap", adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods.

  18. Negativity Bias in Dangerous Drivers.

    Directory of Open Access Journals (Sweden)

    Jing Chai

    Full Text Available The behavioral and cognitive characteristics of dangerous drivers differ significantly from those of safe drivers. However, differences in emotional information processing have seldom been investigated. Previous studies have revealed that drivers with higher anger/anxiety trait scores are more likely to be involved in crashes and that individuals with higher anger traits exhibit stronger negativity biases when processing emotions compared with control groups. However, researchers have not explored the relationship between emotional information processing and driving behavior. In this study, we examined the emotional information processing differences between dangerous drivers and safe drivers. Thirty-eight non-professional drivers were divided into two groups according to the penalty points that they had accrued for traffic violations: 15 drivers with 6 or more points were included in the dangerous driver group, and 23 drivers with 3 or fewer points were included in the safe driver group. The emotional Stroop task was used to measure negativity biases, and both behavioral and electroencephalograph data were recorded. The behavioral results revealed stronger negativity biases in the dangerous drivers than in the safe drivers. The bias score was correlated with self-reported dangerous driving behavior. Drivers with strong negativity biases reported having been involved in mores crashes compared with the less-biased drivers. The event-related potentials (ERPs revealed that the dangerous drivers exhibited reduced P3 components when responding to negative stimuli, suggesting decreased inhibitory control of information that is task-irrelevant but emotionally salient. The influence of negativity bias provides one possible explanation of the effects of individual differences on dangerous driving behavior and traffic crashes.

  19. Does attention bias modification improve attentional control? A double-blind randomized experiment with individuals with social anxiety disorder.

    Science.gov (United States)

    Heeren, Alexandre; Mogoaşe, Cristina; McNally, Richard J; Schmitz, Anne; Philippot, Pierre

    2015-01-01

    People with anxiety disorders often exhibit an attentional bias for threat. Attention bias modification (ABM) procedure may reduce this bias, thereby diminishing anxiety symptoms. In ABM, participants respond to probes that reliably follow non-threatening stimuli (e.g., neutral faces) such that their attention is directed away from concurrently presented threatening stimuli (e.g., disgust faces). Early studies showed that ABM reduced anxiety more than control procedures lacking any contingency between valenced stimuli and probes. However, recent work suggests that no-contingency training and training toward threat cues can be as effective as ABM in reducing anxiety, implying that any training may increase executive control over attention, thereby helping people inhibit their anxious thoughts. Extending this work, we randomly assigned participants with DSM-IV diagnosed social anxiety disorder to either training toward non-threat (ABM), training toward threat, or no-contingency condition, and we used the attention network task (ANT) to assess all three components of attention. After two training sessions, subjects in all three conditions exhibited indistinguishably significant declines from baseline to post-training in self-report and behavioral measures of anxiety on an impromptu speech task. Moreover, all groups exhibited similarly significant improvements on the alerting and executive (but not orienting) components of attention. Implications for ABM research are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. The Implicit Relational Assessment Procedure (IRAP) as a Measure of Spider Fear

    Science.gov (United States)

    Nicholson, Emma; Barnes-Holmes, Dermot

    2012-01-01

    A greater understanding of implicit cognition can provide important information regarding the etiology and maintenance of psychological disorders. The current study sought to determine the utility of the Implicit Relational Assessment Procedure (IRAP) as a measure of implicit aversive bias toward spiders in two groups of known variation, high fear…

  1. Religious Attitudes and Home Bias

    OpenAIRE

    C. Reggiani; G. Rossini

    2008-01-01

    Home bias affects trade in goods, services and financial assets. It is mostly generated by "natural" trade barriers. Among these dividers we may list many behavioral and sociological factors, such as status quo biases and a few kind of ‘embeddedness’. Unfortunately these factors are difficult to measure. An important part of ‘embeddedness’ may be related to religious attitudes. Is there any relation between economic home bias and religious attitudes at the individual tier? Our aim is to provi...

  2. Placebo effect studies are susceptible to response bias and to other types of biases

    DEFF Research Database (Denmark)

    Hróbjartsson, Asbjørn; Kaptchuk, Ted J; Miller, Franklin G

    2011-01-01

    Investigations of the effect of placebo are often challenging to conduct and interpret. The history of placebo shows that assessment of its clinical significance has a real potential to be biased. We analyze and discuss typical types of bias in studies on placebo....

  3. A Comparison of attentional biases and memory biases in social phobia and major depression

    NARCIS (Netherlands)

    Rinck, M.; Becker, E.S.

    2005-01-01

    Cognitive processes play an important role in the etiology and maintenance of anxiety and depression. Current theories differ, however, in their predictions regarding the occurrence of attentional biases and memory biases in depression and anxiety. To allow for a systematic comparison of disorders

  4. Are most samples of animals systematically biased? Consistent individual trait differences bias samples despite random sampling.

    Science.gov (United States)

    Biro, Peter A

    2013-02-01

    Sampling animals from the wild for study is something nearly every biologist has done, but despite our best efforts to obtain random samples of animals, 'hidden' trait biases may still exist. For example, consistent behavioral traits can affect trappability/catchability, independent of obvious factors such as size and gender, and these traits are often correlated with other repeatable physiological and/or life history traits. If so, systematic sampling bias may exist for any of these traits. The extent to which this is a problem, of course, depends on the magnitude of bias, which is presently unknown because the underlying trait distributions in populations are usually unknown, or unknowable. Indeed, our present knowledge about sampling bias comes from samples (not complete population censuses), which can possess bias to begin with. I had the unique opportunity to create naturalized populations of fish by seeding each of four small fishless lakes with equal densities of slow-, intermediate-, and fast-growing fish. Using sampling methods that are not size-selective, I observed that fast-growing fish were up to two-times more likely to be sampled than slower-growing fish. This indicates substantial and systematic bias with respect to an important life history trait (growth rate). If correlations between behavioral, physiological and life-history traits are as widespread as the literature suggests, then many animal samples may be systematically biased with respect to these traits (e.g., when collecting animals for laboratory use), and affect our inferences about population structure and abundance. I conclude with a discussion on ways to minimize sampling bias for particular physiological/behavioral/life-history types within animal populations.

  5. Learning Supervised Topic Models for Classification and Regression from Crowds.

    Science.gov (United States)

    Rodrigues, Filipe; Lourenco, Mariana; Ribeiro, Bernardete; Pereira, Francisco C

    2017-12-01

    The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages of the proposed model over state-of-the-art approaches.

  6. PEST reduces bias in forced choice psychophysics.

    Science.gov (United States)

    Taylor, M M; Forbes, S M; Creelman, C D

    1983-11-01

    Observers performed several different detection tasks using both the PEST adaptive psychophysical procedure and a fixed-level (method of constant stimuli) psychophysical procedure. In two experiments, PEST runs targeted at P (C) = 0.80 were immediately followed by fixed-level detection runs presented at the difficulty level resulting from the PEST run. The fixed-level runs yielded P (C) about 0.75. During the fixed-level runs, the probability of a correct response was greater when the preceding response was correct than when it was wrong. Observers, even highly trained ones, perform in a nonstationary manner. The sequential dependency data can be used to determine a lower bound for the observer's "true" capability when performing optimally; this lower bound is close to the PEST target, and well above the forced choice P (C). The observer's "true" capability is the measure used by most theories of detection performance. A further experiment compared psychometric functions obtained from a set of PEST runs using different targets with those obtained from blocks of fixed-level trials at different levels. PEST results were more stable across observers, performance at all but the highest signal levels was better with PEST, and the PEST psychometric functions had shallower slopes. We hypothesize that PEST permits the observer to keep track of what he is trying to detect, whereas in the fixed-level method performance is disrupted by memory failure. Some recently suggested "more virulent" versions of PEST may be subject to biases similar to those of the fixed-level procedures.(ABSTRACT TRUNCATED AT 250 WORDS)

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

    International Nuclear Information System (INIS)

    Terwilliger, Thomas C.; Grosse-Kunstleve, Ralf W.; Afonine, Pavel V.; Moriarty, Nigel W.; Adams, Paul D.; Read, Randy J.; Zwart, Peter H.; Hung, Li-Wei

    2008-01-01

    An OMIT procedure is presented that has the benefits of iterative model building density modification and refinement yet is essentially unbiased by the atomic model that is built. 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

  8. Bias due to differential participation in case-control studies and review of available approaches for adjustment.

    Science.gov (United States)

    Aigner, Annette; Grittner, Ulrike; Becher, Heiko

    2018-01-01

    Low response rates in epidemiologic research potentially lead to the recruitment of a non-representative sample of controls in case-control studies. Problems in the unbiased estimation of odds ratios arise when characteristics causing the probability of participation are associated with exposure and outcome. This is a specific setting of selection bias and a realistic hazard in many case-control studies. This paper formally describes the problem and shows its potential extent, reviews existing approaches for bias adjustment applicable under certain conditions, compares and applies them. We focus on two scenarios: a characteristic C causing differential participation of controls is linked to the outcome through its association with risk factor E (scenario I), and C is additionally a genuine risk factor itself (scenario II). We further assume external data sources are available which provide an unbiased estimate of C in the underlying population. Given these scenarios, we (i) review available approaches and their performance in the setting of bias due to differential participation; (ii) describe two existing approaches to correct for the bias in both scenarios in more detail; (iii) present the magnitude of the resulting bias by simulation if the selection of a non-representative sample is ignored; and (iv) demonstrate the approaches' application via data from a case-control study on stroke. The bias of the effect measure for variable E in scenario I and C in scenario II can be large and should therefore be adjusted for in any analysis. It is positively associated with the difference in response rates between groups of the characteristic causing differential participation, and inversely associated with the total response rate in the controls. Adjustment in a standard logistic regression framework is possible in both scenarios if the population distribution of the characteristic causing differential participation is known or can be approximated well.

  9. A Simple and Convenient Method of Multiple Linear Regression to Calculate Iodine Molecular Constants

    Science.gov (United States)

    Cooper, Paul D.

    2010-01-01

    A new procedure using a student-friendly least-squares multiple linear-regression technique utilizing a function within Microsoft Excel is described that enables students to calculate molecular constants from the vibronic spectrum of iodine. This method is advantageous pedagogically as it calculates molecular constants for ground and excited…

  10. Concomitant hysteroscopic endometrial ablation and Essure procedure: feasibility, efficacy and satisfaction.

    Science.gov (United States)

    Levy-Zauberman, Y; Legendre, G; Nazac, A; Faivre, E; Deffieux, X; Fernandez, H

    2014-07-01

    Hysteroscopic endometrial destruction procedures for abnormal uterine bleeding are an alternative to hysterectomy. Such procedures are not contraceptive and are performed on fertile patients, requiring long-term contraception. This is the first study evaluating long-term results of a combined procedure associating endometrial destruction and concomitant hysteroscopic tubal sterilization by Essure(®) micro-inserts. Our goal is to evaluate efficacy of endometrial destruction as well as hysteroscopic sterilization and satisfaction after a combined procedure in the case of abnormal uterine bleeding in non-menopausal patients. This is a retrospective study (Canadian task force II-2) that includes 131 patients operated with combined endometrial destruction and hysteroscopic tubal sterilization between 2002 and 2011 at our university hospital. The patients were contacted to answer a questionnaire. Statistical analysis was performed with SAS© version 9.2. (SAS Institute Inc., Cary, NC). Ninety-three patients out of 131 could be reached. The mean follow-up was of 37.8 months (min=8, max=87, SD=6.2). Thirty-eight patients (29%) were lost to follow-up. Essure(®) micro-inserts introduction success rate (evaluated on 131 patients) was 95.8%, and their position was appropriate in 81.1% of the 106 patients with position control. Efficacy of the procedure on the haemorrhagic symptoms (evaluated on 93 patients) was 80.6%. Twelve patients (12.9%) underwent a hysterectomy, 7 of which (58.3%) were a direct consequence of treatment failure. No pregnancies were reported. Satisfaction rate was of 90.3%. Inadequate position rates of the micro-inserts after 3 months seem somewhat above literature findings, though no pregnancy has been reported. However, recurrent bleeding symptoms and hysterectomy rates are consistent with those observed after an endometrial destruction procedure alone. Limitations are the limited number of patients, the bias inherent to retrospective studies (lost of

  11. Domain wall engineering through exchange bias

    International Nuclear Information System (INIS)

    Albisetti, E.; Petti, D.

    2016-01-01

    The control of the structure and position of magnetic domain walls is at the basis of the development of different magnetic devices and architectures. Several nanofabrication techniques have been proposed to geometrically confine and shape domain wall structures; however, a fine tuning of the position and micromagnetic configuration is hardly achieved, especially in continuous films. This work shows that, by controlling the unidirectional anisotropy of a continuous ferromagnetic film through exchange bias, domain walls whose spin arrangement is generally not favored by dipolar and exchange interactions can be created. Micromagnetic simulations reveal that the domain wall width, position and profile can be tuned by establishing an abrupt change in the direction and magnitude of the exchange bias field set in the system. - Highlights: • Micromagnetic simulations study domain walls in exchange biased thin films. • Novel domain wall configurations can be stabilized via exchange bias. • Domain walls nucleate at the boundary of regions with different exchange bias. • Domain wall width and spin profile are controlled by tuning the exchange bias.

  12. How we categorize objects is related to how we remember them: The shape bias as a memory bias.

    Science.gov (United States)

    Vlach, Haley A

    2016-12-01

    The "shape bias" describes the phenomenon that, after a certain point in development, children and adults generalize object categories based on shape to a greater degree than other perceptual features. The focus of research on the shape bias has been to examine the types of information that learners attend to in one moment in time. The current work takes a different approach by examining whether learners' categorical biases are related to their retention of information across time. In three experiments, children's (N=72) and adults' (N=240) memory performance for features of objects was examined in relation to their categorical biases. The results of these experiments demonstrated that the number of shape matches chosen during the shape bias task significantly predicted shape memory. Moreover, children and adults with a shape bias were more likely to remember the shape of objects than the color and size of objects. Taken together, this work suggests that the development of a shape bias may engender better memory for shape information. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. Is bias in the eye of the beholder? A vignette study to assess recognition of cognitive biases in clinical case workups.

    Science.gov (United States)

    Zwaan, Laura; Monteiro, Sandra; Sherbino, Jonathan; Ilgen, Jonathan; Howey, Betty; Norman, Geoffrey

    2017-02-01

    Many authors have implicated cognitive biases as a primary cause of diagnostic error. If this is so, then physicians already familiar with common cognitive biases should consistently identify biases present in a clinical workup. The aim of this paper is to determine whether physicians agree on the presence or absence of particular biases in a clinical case workup and how case outcome knowledge affects bias identification. We conducted a web survey of 37 physicians. Each participant read eight cases and listed which biases were present from a list provided. In half the cases the outcome implied a correct diagnosis; in the other half, it implied an incorrect diagnosis. We compared the number of biases identified when the outcome implied a correct or incorrect primary diagnosis. Additionally, the agreement among participants about presence or absence of specific biases was assessed. When the case outcome implied a correct diagnosis, an average of 1.75 cognitive biases were reported; when incorrect, 3.45 biases (F=71.3, p<0.00001). Individual biases were reported from 73% to 125% more often when an incorrect diagnosis was implied. There was no agreement on presence or absence of individual biases, with κ ranging from 0.000 to 0.044. Individual physicians are unable to agree on the presence or absence of individual cognitive biases. Their judgements are heavily influenced by hindsight bias; when the outcome implies a diagnostic error, twice as many biases are identified. The results present challenges for current error reduction strategies based on identification of cognitive biases. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  14. Unconscious race and social class bias among acute care surgical clinicians and clinical treatment decisions.

    Science.gov (United States)

    Haider, Adil H; Schneider, Eric B; Sriram, N; Dossick, Deborah S; Scott, Valerie K; Swoboda, Sandra M; Losonczy, Lia; Haut, Elliott R; Efron, David T; Pronovost, Peter J; Lipsett, Pamela A; Cornwell, Edward E; MacKenzie, Ellen J; Cooper, Lisa A; Freischlag, Julie A

    2015-05-01

    Significant health inequities persist among minority and socially disadvantaged patients. Better understanding of how unconscious biases affect clinical decision making may help to illuminate clinicians' roles in propagating disparities. To determine whether clinicians' unconscious race and/or social class biases correlate with patient management decisions. We conducted a web-based survey among 230 physicians from surgery and related specialties at an academic, level I trauma center from December 1, 2011, through January 31, 2012. We administered clinical vignettes, each with 3 management questions. Eight vignettes assessed the relationship between unconscious bias and clinical decision making. We performed ordered logistic regression analysis on the Implicit Association Test (IAT) scores and used multivariable analysis to determine whether implicit bias was associated with the vignette responses. Differential response times (D scores) on the IAT as a surrogate for unconscious bias. Patient management vignettes varied by patient race or social class. Resulting D scores were calculated for each management decision. In total, 215 clinicians were included and consisted of 74 attending surgeons, 32 fellows, 86 residents, 19 interns, and 4 physicians with an undetermined level of education. Specialties included surgery (32.1%), anesthesia (18.1%), emergency medicine (18.1%), orthopedics (7.9%), otolaryngology (7.0%), neurosurgery (7.0%), critical care (6.0%), and urology (2.8%); 1.9% did not report a departmental affiliation. Implicit race and social class biases were present in most respondents. Among all clinicians, mean IAT D scores for race and social class were 0.42 (95% CI, 0.37-0.48) and 0.71 (95% CI, 0.65-0.78), respectively. Race and class scores were similar across departments (general surgery, orthopedics, urology, etc), race, or age. Women demonstrated less bias concerning race (mean IAT D score, 0.39 [95% CI, 0.29-0.49]) and social class (mean IAT D score

  15. Reduction of determinate errors in mass bias-corrected isotope ratios measured using a multi-collector plasma mass spectrometer

    International Nuclear Information System (INIS)

    Doherty, W.

    2015-01-01

    A nebulizer-centric instrument response function model of the plasma mass spectrometer was combined with a signal drift model, and the result was used to identify the causes of the non-spectroscopic determinate errors remaining in mass bias-corrected Pb isotope ratios (Tl as internal standard) measured using a multi-collector plasma mass spectrometer. Model calculations, confirmed by measurement, show that the detectable time-dependent errors are a result of the combined effect of signal drift and differences in the coordinates of the Pb and Tl response function maxima (horizontal offset effect). If there are no horizontal offsets, then the mass bias-corrected isotope ratios are approximately constant in time. In the absence of signal drift, the response surface curvature and horizontal offset effects are responsible for proportional errors in the mass bias-corrected isotope ratios. The proportional errors will be different for different analyte isotope ratios and different at every instrument operating point. Consequently, mass bias coefficients calculated using different isotope ratios are not necessarily equal. The error analysis based on the combined model provides strong justification for recommending a three step correction procedure (mass bias correction, drift correction and a proportional error correction, in that order) for isotope ratio measurements using a multi-collector plasma mass spectrometer

  16. Sensitivity of Hydrologic Response to Climate Model Debiasing Procedures

    Science.gov (United States)

    Channell, K.; Gronewold, A.; Rood, R. B.; Xiao, C.; Lofgren, B. M.; Hunter, T.

    2017-12-01

    Climate change is already having a profound impact on the global hydrologic cycle. In the Laurentian Great Lakes, changes in long-term evaporation and precipitation can lead to rapid water level fluctuations in the lakes, as evidenced by unprecedented change in water levels seen in the last two decades. These fluctuations often have an adverse impact on the region's human, environmental, and economic well-being, making accurate long-term water level projections invaluable to regional water resources management planning. Here we use hydrological components from a downscaled climate model (GFDL-CM3/WRF), to obtain future water supplies for the Great Lakes. We then apply a suite of bias correction procedures before propagating these water supplies through a routing model to produce lake water levels. Results using conventional bias correction methods suggest that water levels will decline by several feet in the coming century. However, methods that reflect the seasonal water cycle and explicitly debias individual hydrological components (overlake precipitation, overlake evaporation, runoff) imply that future water levels may be closer to their historical average. This discrepancy between debiased results indicates that water level forecasts are highly influenced by the bias correction method, a source of sensitivity that is commonly overlooked. Debiasing, however, does not remedy misrepresentation of the underlying physical processes in the climate model that produce these biases and contribute uncertainty to the hydrological projections. This uncertainty coupled with the differences in water level forecasts from varying bias correction methods are important for water management and long term planning in the Great Lakes region.

  17. Assessing nonresponse bias at follow-up in a large prospective cohort of relatively young and mobile military service members

    Directory of Open Access Journals (Sweden)

    Hooper Tomoko

    2010-10-01

    Full Text Available Abstract Background Nonresponse bias in a longitudinal study could affect the magnitude and direction of measures of association. We identified sociodemographic, behavioral, military, and health-related predictors of response to the first follow-up questionnaire in a large military cohort and assessed the extent to which nonresponse biased measures of association. Methods Data are from the baseline and first follow-up survey of the Millennium Cohort Study. Seventy-six thousand, seven hundred and seventy-five eligible individuals completed the baseline survey and were presumed alive at the time of follow-up; of these, 54,960 (71.6% completed the first follow-up survey. Logistic regression models were used to calculate inverse probability weights using propensity scores. Results Characteristics associated with a greater probability of response included female gender, older age, higher education level, officer rank, active-duty status, and a self-reported history of military exposures. Ever smokers, those with a history of chronic alcohol consumption or a major depressive disorder, and those separated from the military at follow-up had a lower probability of response. Nonresponse to the follow-up questionnaire did not result in appreciable bias; bias was greatest in subgroups with small numbers. Conclusions These findings suggest that prospective analyses from this cohort are not substantially biased by non-response at the first follow-up assessment.

  18. Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

    Science.gov (United States)

    Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Ling, Wing-Kuen; Zhao, Huimin; Marshall, Stephen

    2017-12-01

    Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.

  19. A Linear Regression Model for Global Solar Radiation on Horizontal Surfaces at Warri, Nigeria

    Directory of Open Access Journals (Sweden)

    Michael S. Okundamiya

    2013-10-01

    Full Text Available The growing anxiety on the negative effects of fossil fuels on the environment and the global emission reduction targets call for a more extensive use of renewable energy alternatives. Efficient solar energy utilization is an essential solution to the high atmospheric pollution caused by fossil fuel combustion. Global solar radiation (GSR data, which are useful for the design and evaluation of solar energy conversion system, are not measured at the forty-five meteorological stations in Nigeria. The dearth of the measured solar radiation data calls for accurate estimation. This study proposed a temperature-based linear regression, for predicting the monthly average daily GSR on horizontal surfaces, at Warri (latitude 5.020N and longitude 7.880E an oil city located in the south-south geopolitical zone, in Nigeria. The proposed model is analyzed based on five statistical indicators (coefficient of correlation, coefficient of determination, mean bias error, root mean square error, and t-statistic, and compared with the existing sunshine-based model for the same study. The results indicate that the proposed temperature-based linear regression model could replace the existing sunshine-based model for generating global solar radiation data. Keywords: air temperature; empirical model; global solar radiation; regression analysis; renewable energy; Warri

  20. Regression tools for CO2 inversions: application of a shrinkage estimator to process attribution

    International Nuclear Information System (INIS)

    Shaby, Benjamin A.; Field, Christopher B.

    2006-01-01

    In this study we perform an atmospheric inversion based on a shrinkage estimator. This method is used to estimate surface fluxes of CO 2 , first partitioned according to constituent geographic regions, and then according to constituent processes that are responsible for the total flux. Our approach differs from previous approaches in two important ways. The first is that the technique of linear Bayesian inversion is recast as a regression problem. Seen as such, standard regression tools are employed to analyse and reduce errors in the resultant estimates. A shrinkage estimator, which combines standard ridge regression with the linear 'Bayesian inversion' model, is introduced. This method introduces additional bias into the model with the aim of reducing variance such that errors are decreased overall. Compared with standard linear Bayesian inversion, the ridge technique seems to reduce both flux estimation errors and prediction errors. The second divergence from previous studies is that instead of dividing the world into geographically distinct regions and estimating the CO 2 flux in each region, the flux space is divided conceptually into processes that contribute to the total global flux. Formulating the problem in this manner adds to the interpretability of the resultant estimates and attempts to shed light on the problem of attributing sources and sinks to their underlying mechanisms