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Sample records for empirical likelihood methods

  1. Generalized empirical likelihood methods for analyzing longitudinal data

    KAUST Repository

    Wang, S.

    2010-02-16

    Efficient estimation of parameters is a major objective in analyzing longitudinal data. We propose two generalized empirical likelihood based methods that take into consideration within-subject correlations. A nonparametric version of the Wilks theorem for the limiting distributions of the empirical likelihood ratios is derived. It is shown that one of the proposed methods is locally efficient among a class of within-subject variance-covariance matrices. A simulation study is conducted to investigate the finite sample properties of the proposed methods and compare them with the block empirical likelihood method by You et al. (2006) and the normal approximation with a correctly estimated variance-covariance. The results suggest that the proposed methods are generally more efficient than existing methods which ignore the correlation structure, and better in coverage compared to the normal approximation with correctly specified within-subject correlation. An application illustrating our methods and supporting the simulation study results is also presented.

  2. Empirical likelihood

    CERN Document Server

    Owen, Art B

    2001-01-01

    Empirical likelihood provides inferences whose validity does not depend on specifying a parametric model for the data. Because it uses a likelihood, the method has certain inherent advantages over resampling methods: it uses the data to determine the shape of the confidence regions, and it makes it easy to combined data from multiple sources. It also facilitates incorporating side information, and it simplifies accounting for censored, truncated, or biased sampling.One of the first books published on the subject, Empirical Likelihood offers an in-depth treatment of this method for constructing confidence regions and testing hypotheses. The author applies empirical likelihood to a range of problems, from those as simple as setting a confidence region for a univariate mean under IID sampling, to problems defined through smooth functions of means, regression models, generalized linear models, estimating equations, or kernel smooths, and to sampling with non-identically distributed data. Abundant figures offer vi...

  3. Generalized empirical likelihood methods for analyzing longitudinal data

    KAUST Repository

    Wang, S.; Qian, L.; Carroll, R. J.

    2010-01-01

    Efficient estimation of parameters is a major objective in analyzing longitudinal data. We propose two generalized empirical likelihood based methods that take into consideration within-subject correlations. A nonparametric version of the Wilks

  4. Essays on empirical likelihood in economics

    NARCIS (Netherlands)

    Gao, Z.

    2012-01-01

    This thesis intends to exploit the roots of empirical likelihood and its related methods in mathematical programming and computation. The roots will be connected and the connections will induce new solutions for the problems of estimation, computation, and generalization of empirical likelihood.

  5. Adjusted Empirical Likelihood Method in the Presence of Nuisance Parameters with Application to the Sharpe Ratio

    Directory of Open Access Journals (Sweden)

    Yuejiao Fu

    2018-04-01

    Full Text Available The Sharpe ratio is a widely used risk-adjusted performance measurement in economics and finance. Most of the known statistical inferential methods devoted to the Sharpe ratio are based on the assumption that the data are normally distributed. In this article, without making any distributional assumption on the data, we develop the adjusted empirical likelihood method to obtain inference for a parameter of interest in the presence of nuisance parameters. We show that the log adjusted empirical likelihood ratio statistic is asymptotically distributed as the chi-square distribution. The proposed method is applied to obtain inference for the Sharpe ratio. Simulation results illustrate that the proposed method is comparable to Jobson and Korkie’s method (1981 and outperforms the empirical likelihood method when the data are from a symmetric distribution. In addition, when the data are from a skewed distribution, the proposed method significantly outperforms all other existing methods. A real-data example is analyzed to exemplify the application of the proposed method.

  6. Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood

    Directory of Open Access Journals (Sweden)

    Yunquan Song

    2014-01-01

    Full Text Available Empirical likelihood is a very popular method and has been widely used in the fields of artificial intelligence (AI and data mining as tablets and mobile application and social media dominate the technology landscape. This paper proposes an empirical likelihood shrinkage method to efficiently estimate unknown parameters and select correct moment conditions simultaneously, when the model is defined by moment restrictions in which some are possibly misspecified. We show that our method enjoys oracle-like properties; that is, it consistently selects the correct moment conditions and at the same time its estimator is as efficient as the empirical likelihood estimator obtained by all correct moment conditions. Moreover, unlike the GMM, our proposed method allows us to carry out confidence regions for the parameters included in the model without estimating the covariances of the estimators. For empirical implementation, we provide some data-driven procedures for selecting the tuning parameter of the penalty function. The simulation results show that the method works remarkably well in terms of correct moment selection and the finite sample properties of the estimators. Also, a real-life example is carried out to illustrate the new methodology.

  7. Empirical Likelihood in Nonignorable Covariate-Missing Data Problems.

    Science.gov (United States)

    Xie, Yanmei; Zhang, Biao

    2017-04-20

    Missing covariate data occurs often in regression analysis, which frequently arises in the health and social sciences as well as in survey sampling. We study methods for the analysis of a nonignorable covariate-missing data problem in an assumed conditional mean function when some covariates are completely observed but other covariates are missing for some subjects. We adopt the semiparametric perspective of Bartlett et al. (Improving upon the efficiency of complete case analysis when covariates are MNAR. Biostatistics 2014;15:719-30) on regression analyses with nonignorable missing covariates, in which they have introduced the use of two working models, the working probability model of missingness and the working conditional score model. In this paper, we study an empirical likelihood approach to nonignorable covariate-missing data problems with the objective of effectively utilizing the two working models in the analysis of covariate-missing data. We propose a unified approach to constructing a system of unbiased estimating equations, where there are more equations than unknown parameters of interest. One useful feature of these unbiased estimating equations is that they naturally incorporate the incomplete data into the data analysis, making it possible to seek efficient estimation of the parameter of interest even when the working regression function is not specified to be the optimal regression function. We apply the general methodology of empirical likelihood to optimally combine these unbiased estimating equations. We propose three maximum empirical likelihood estimators of the underlying regression parameters and compare their efficiencies with other existing competitors. We present a simulation study to compare the finite-sample performance of various methods with respect to bias, efficiency, and robustness to model misspecification. The proposed empirical likelihood method is also illustrated by an analysis of a data set from the US National Health and

  8. Generalized Empirical Likelihood-Based Focused Information Criterion and Model Averaging

    Directory of Open Access Journals (Sweden)

    Naoya Sueishi

    2013-07-01

    Full Text Available This paper develops model selection and averaging methods for moment restriction models. We first propose a focused information criterion based on the generalized empirical likelihood estimator. We address the issue of selecting an optimal model, rather than a correct model, for estimating a specific parameter of interest. Then, this study investigates a generalized empirical likelihood-based model averaging estimator that minimizes the asymptotic mean squared error. A simulation study suggests that our averaging estimator can be a useful alternative to existing post-selection estimators.

  9. Block Empirical Likelihood for Longitudinal Single-Index Varying-Coefficient Model

    Directory of Open Access Journals (Sweden)

    Yunquan Song

    2013-01-01

    Full Text Available In this paper, we consider a single-index varying-coefficient model with application to longitudinal data. In order to accommodate the within-group correlation, we apply the block empirical likelihood procedure to longitudinal single-index varying-coefficient model, and prove a nonparametric version of Wilks’ theorem which can be used to construct the block empirical likelihood confidence region with asymptotically correct coverage probability for the parametric component. In comparison with normal approximations, the proposed method does not require a consistent estimator for the asymptotic covariance matrix, making it easier to conduct inference for the model's parametric component. Simulations demonstrate how the proposed method works.

  10. An alternative empirical likelihood method in missing response problems and causal inference.

    Science.gov (United States)

    Ren, Kaili; Drummond, Christopher A; Brewster, Pamela S; Haller, Steven T; Tian, Jiang; Cooper, Christopher J; Zhang, Biao

    2016-11-30

    Missing responses are common problems in medical, social, and economic studies. When responses are missing at random, a complete case data analysis may result in biases. A popular debias method is inverse probability weighting proposed by Horvitz and Thompson. To improve efficiency, Robins et al. proposed an augmented inverse probability weighting method. The augmented inverse probability weighting estimator has a double-robustness property and achieves the semiparametric efficiency lower bound when the regression model and propensity score model are both correctly specified. In this paper, we introduce an empirical likelihood-based estimator as an alternative to Qin and Zhang (2007). Our proposed estimator is also doubly robust and locally efficient. Simulation results show that the proposed estimator has better performance when the propensity score is correctly modeled. Moreover, the proposed method can be applied in the estimation of average treatment effect in observational causal inferences. Finally, we apply our method to an observational study of smoking, using data from the Cardiovascular Outcomes in Renal Atherosclerotic Lesions clinical trial. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  11. Bayesian interpretation of Generalized empirical likelihood by maximum entropy

    OpenAIRE

    Rochet , Paul

    2011-01-01

    We study a parametric estimation problem related to moment condition models. As an alternative to the generalized empirical likelihood (GEL) and the generalized method of moments (GMM), a Bayesian approach to the problem can be adopted, extending the MEM procedure to parametric moment conditions. We show in particular that a large number of GEL estimators can be interpreted as a maximum entropy solution. Moreover, we provide a more general field of applications by proving the method to be rob...

  12. A Reliability Test of a Complex System Based on Empirical Likelihood

    OpenAIRE

    Zhou, Yan; Fu, Liya; Zhang, Jun; Hui, Yongchang

    2016-01-01

    To analyze the reliability of a complex system described by minimal paths, an empirical likelihood method is proposed to solve the reliability test problem when the subsystem distributions are unknown. Furthermore, we provide a reliability test statistic of the complex system and extract the limit distribution of the test statistic. Therefore, we can obtain the confidence interval for reliability and make statistical inferences. The simulation studies also demonstrate the theorem results.

  13. A Non-standard Empirical Likelihood for Time Series

    DEFF Research Database (Denmark)

    Nordman, Daniel J.; Bunzel, Helle; Lahiri, Soumendra N.

    Standard blockwise empirical likelihood (BEL) for stationary, weakly dependent time series requires specifying a fixed block length as a tuning parameter for setting confidence regions. This aspect can be difficult and impacts coverage accuracy. As an alternative, this paper proposes a new version...... of BEL based on a simple, though non-standard, data-blocking rule which uses a data block of every possible length. Consequently, the method involves no block selection and is also anticipated to exhibit better coverage performance. Its non-standard blocking scheme, however, induces non......-standard asymptotics and requires a significantly different development compared to standard BEL. We establish the large-sample distribution of log-ratio statistics from the new BEL method for calibrating confidence regions for mean or smooth function parameters of time series. This limit law is not the usual chi...

  14. Comparisons of likelihood and machine learning methods of individual classification

    Science.gov (United States)

    Guinand, B.; Topchy, A.; Page, K.S.; Burnham-Curtis, M. K.; Punch, W.F.; Scribner, K.T.

    2002-01-01

    Classification methods used in machine learning (e.g., artificial neural networks, decision trees, and k-nearest neighbor clustering) are rarely used with population genetic data. We compare different nonparametric machine learning techniques with parametric likelihood estimations commonly employed in population genetics for purposes of assigning individuals to their population of origin (“assignment tests”). Classifier accuracy was compared across simulated data sets representing different levels of population differentiation (low and high FST), number of loci surveyed (5 and 10), and allelic diversity (average of three or eight alleles per locus). Empirical data for the lake trout (Salvelinus namaycush) exhibiting levels of population differentiation comparable to those used in simulations were examined to further evaluate and compare classification methods. Classification error rates associated with artificial neural networks and likelihood estimators were lower for simulated data sets compared to k-nearest neighbor and decision tree classifiers over the entire range of parameters considered. Artificial neural networks only marginally outperformed the likelihood method for simulated data (0–2.8% lower error rates). The relative performance of each machine learning classifier improved relative likelihood estimators for empirical data sets, suggesting an ability to “learn” and utilize properties of empirical genotypic arrays intrinsic to each population. Likelihood-based estimation methods provide a more accessible option for reliable assignment of individuals to the population of origin due to the intricacies in development and evaluation of artificial neural networks. In recent years, characterization of highly polymorphic molecular markers such as mini- and microsatellites and development of novel methods of analysis have enabled researchers to extend investigations of ecological and evolutionary processes below the population level to the level of

  15. Maximum Likelihood and Restricted Likelihood Solutions in Multiple-Method Studies.

    Science.gov (United States)

    Rukhin, Andrew L

    2011-01-01

    A formulation of the problem of combining data from several sources is discussed in terms of random effects models. The unknown measurement precision is assumed not to be the same for all methods. We investigate maximum likelihood solutions in this model. By representing the likelihood equations as simultaneous polynomial equations, the exact form of the Groebner basis for their stationary points is derived when there are two methods. A parametrization of these solutions which allows their comparison is suggested. A numerical method for solving likelihood equations is outlined, and an alternative to the maximum likelihood method, the restricted maximum likelihood, is studied. In the situation when methods variances are considered to be known an upper bound on the between-method variance is obtained. The relationship between likelihood equations and moment-type equations is also discussed.

  16. An empirical likelihood ratio test robust to individual heterogeneity for differential expression analysis of RNA-seq.

    Science.gov (United States)

    Xu, Maoqi; Chen, Liang

    2018-01-01

    The individual sample heterogeneity is one of the biggest obstacles in biomarker identification for complex diseases such as cancers. Current statistical models to identify differentially expressed genes between disease and control groups often overlook the substantial human sample heterogeneity. Meanwhile, traditional nonparametric tests lose detailed data information and sacrifice the analysis power, although they are distribution free and robust to heterogeneity. Here, we propose an empirical likelihood ratio test with a mean-variance relationship constraint (ELTSeq) for the differential expression analysis of RNA sequencing (RNA-seq). As a distribution-free nonparametric model, ELTSeq handles individual heterogeneity by estimating an empirical probability for each observation without making any assumption about read-count distribution. It also incorporates a constraint for the read-count overdispersion, which is widely observed in RNA-seq data. ELTSeq demonstrates a significant improvement over existing methods such as edgeR, DESeq, t-tests, Wilcoxon tests and the classic empirical likelihood-ratio test when handling heterogeneous groups. It will significantly advance the transcriptomics studies of cancers and other complex disease. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  17. Evaluating Fast Maximum Likelihood-Based Phylogenetic Programs Using Empirical Phylogenomic Data Sets

    Science.gov (United States)

    Zhou, Xiaofan; Shen, Xing-Xing; Hittinger, Chris Todd

    2018-01-01

    Abstract The sizes of the data matrices assembled to resolve branches of the tree of life have increased dramatically, motivating the development of programs for fast, yet accurate, inference. For example, several different fast programs have been developed in the very popular maximum likelihood framework, including RAxML/ExaML, PhyML, IQ-TREE, and FastTree. Although these programs are widely used, a systematic evaluation and comparison of their performance using empirical genome-scale data matrices has so far been lacking. To address this question, we evaluated these four programs on 19 empirical phylogenomic data sets with hundreds to thousands of genes and up to 200 taxa with respect to likelihood maximization, tree topology, and computational speed. For single-gene tree inference, we found that the more exhaustive and slower strategies (ten searches per alignment) outperformed faster strategies (one tree search per alignment) using RAxML, PhyML, or IQ-TREE. Interestingly, single-gene trees inferred by the three programs yielded comparable coalescent-based species tree estimations. For concatenation-based species tree inference, IQ-TREE consistently achieved the best-observed likelihoods for all data sets, and RAxML/ExaML was a close second. In contrast, PhyML often failed to complete concatenation-based analyses, whereas FastTree was the fastest but generated lower likelihood values and more dissimilar tree topologies in both types of analyses. Finally, data matrix properties, such as the number of taxa and the strength of phylogenetic signal, sometimes substantially influenced the programs’ relative performance. Our results provide real-world gene and species tree phylogenetic inference benchmarks to inform the design and execution of large-scale phylogenomic data analyses. PMID:29177474

  18. Empirical likelihood-based confidence intervals for the sensitivity of a continuous-scale diagnostic test at a fixed level of specificity.

    Science.gov (United States)

    Gengsheng Qin; Davis, Angela E; Jing, Bing-Yi

    2011-06-01

    For a continuous-scale diagnostic test, it is often of interest to find the range of the sensitivity of the test at the cut-off that yields a desired specificity. In this article, we first define a profile empirical likelihood ratio for the sensitivity of a continuous-scale diagnostic test and show that its limiting distribution is a scaled chi-square distribution. We then propose two new empirical likelihood-based confidence intervals for the sensitivity of the test at a fixed level of specificity by using the scaled chi-square distribution. Simulation studies are conducted to compare the finite sample performance of the newly proposed intervals with the existing intervals for the sensitivity in terms of coverage probability. A real example is used to illustrate the application of the recommended methods.

  19. Application of the method of maximum likelihood to the determination of cepheid radii

    International Nuclear Information System (INIS)

    Balona, L.A.

    1977-01-01

    A method is described whereby the radius of any pulsating star can be obtained by applying the Principle of Maximum Likelihood. The relative merits of this method and of the usual Baade-Wesselink method are discussed in an Appendix. The new method is applied to 54 well-observed cepheids which include a number of spectroscopic binaries and two W Vir stars. An empirical period-radius relation is constructed and discussed in terms of two recent period-luminosity-colour calibrations. It is shown that the new method gives radii with an error of no more than 10 per cent. (author)

  20. Empirical training for conditional random fields

    NARCIS (Netherlands)

    Zhu, Zhemin; Hiemstra, Djoerd; Apers, Peter M.G.; Wombacher, Andreas

    2013-01-01

    In this paper (Zhu et al., 2013), we present a practi- cally scalable training method for CRFs called Empir- ical Training (EP). We show that the standard train- ing with unregularized log likelihood can have many maximum likelihood estimations (MLEs). Empirical training has a unique closed form MLE

  1. The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction.

    Directory of Open Access Journals (Sweden)

    Ross S Williamson

    2015-04-01

    Full Text Available Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron's probability of spiking. One popular method, known as maximally informative dimensions (MID, uses an information-theoretic quantity known as "single-spike information" to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex.

  2. Use of empirical likelihood to calibrate auxiliary information in partly linear monotone regression models.

    Science.gov (United States)

    Chen, Baojiang; Qin, Jing

    2014-05-10

    In statistical analysis, a regression model is needed if one is interested in finding the relationship between a response variable and covariates. When the response depends on the covariate, then it may also depend on the function of this covariate. If one has no knowledge of this functional form but expect for monotonic increasing or decreasing, then the isotonic regression model is preferable. Estimation of parameters for isotonic regression models is based on the pool-adjacent-violators algorithm (PAVA), where the monotonicity constraints are built in. With missing data, people often employ the augmented estimating method to improve estimation efficiency by incorporating auxiliary information through a working regression model. However, under the framework of the isotonic regression model, the PAVA does not work as the monotonicity constraints are violated. In this paper, we develop an empirical likelihood-based method for isotonic regression model to incorporate the auxiliary information. Because the monotonicity constraints still hold, the PAVA can be used for parameter estimation. Simulation studies demonstrate that the proposed method can yield more efficient estimates, and in some situations, the efficiency improvement is substantial. We apply this method to a dementia study. Copyright © 2013 John Wiley & Sons, Ltd.

  3. Phylogenetic analysis using parsimony and likelihood methods.

    Science.gov (United States)

    Yang, Z

    1996-02-01

    The assumptions underlying the maximum-parsimony (MP) method of phylogenetic tree reconstruction were intuitively examined by studying the way the method works. Computer simulations were performed to corroborate the intuitive examination. Parsimony appears to involve very stringent assumptions concerning the process of sequence evolution, such as constancy of substitution rates between nucleotides, constancy of rates across nucleotide sites, and equal branch lengths in the tree. For practical data analysis, the requirement of equal branch lengths means similar substitution rates among lineages (the existence of an approximate molecular clock), relatively long interior branches, and also few species in the data. However, a small amount of evolution is neither a necessary nor a sufficient requirement of the method. The difficulties involved in the application of current statistical estimation theory to tree reconstruction were discussed, and it was suggested that the approach proposed by Felsenstein (1981, J. Mol. Evol. 17: 368-376) for topology estimation, as well as its many variations and extensions, differs fundamentally from the maximum likelihood estimation of a conventional statistical parameter. Evidence was presented showing that the Felsenstein approach does not share the asymptotic efficiency of the maximum likelihood estimator of a statistical parameter. Computer simulations were performed to study the probability that MP recovers the true tree under a hierarchy of models of nucleotide substitution; its performance relative to the likelihood method was especially noted. The results appeared to support the intuitive examination of the assumptions underlying MP. When a simple model of nucleotide substitution was assumed to generate data, the probability that MP recovers the true topology could be as high as, or even higher than, that for the likelihood method. When the assumed model became more complex and realistic, e.g., when substitution rates were

  4. Empirical Correction to the Likelihood Ratio Statistic for Structural Equation Modeling with Many Variables.

    Science.gov (United States)

    Yuan, Ke-Hai; Tian, Yubin; Yanagihara, Hirokazu

    2015-06-01

    Survey data typically contain many variables. Structural equation modeling (SEM) is commonly used in analyzing such data. The most widely used statistic for evaluating the adequacy of a SEM model is T ML, a slight modification to the likelihood ratio statistic. Under normality assumption, T ML approximately follows a chi-square distribution when the number of observations (N) is large and the number of items or variables (p) is small. However, in practice, p can be rather large while N is always limited due to not having enough participants. Even with a relatively large N, empirical results show that T ML rejects the correct model too often when p is not too small. Various corrections to T ML have been proposed, but they are mostly heuristic. Following the principle of the Bartlett correction, this paper proposes an empirical approach to correct T ML so that the mean of the resulting statistic approximately equals the degrees of freedom of the nominal chi-square distribution. Results show that empirically corrected statistics follow the nominal chi-square distribution much more closely than previously proposed corrections to T ML, and they control type I errors reasonably well whenever N ≥ max(50,2p). The formulations of the empirically corrected statistics are further used to predict type I errors of T ML as reported in the literature, and they perform well.

  5. Empirical projection-based basis-component decomposition method

    Science.gov (United States)

    Brendel, Bernhard; Roessl, Ewald; Schlomka, Jens-Peter; Proksa, Roland

    2009-02-01

    Advances in the development of semiconductor based, photon-counting x-ray detectors stimulate research in the domain of energy-resolving pre-clinical and clinical computed tomography (CT). For counting detectors acquiring x-ray attenuation in at least three different energy windows, an extended basis component decomposition can be performed in which in addition to the conventional approach of Alvarez and Macovski a third basis component is introduced, e.g., a gadolinium based CT contrast material. After the decomposition of the measured projection data into the basis component projections, conventional filtered-backprojection reconstruction is performed to obtain the basis-component images. In recent work, this basis component decomposition was obtained by maximizing the likelihood-function of the measurements. This procedure is time consuming and often unstable for excessively noisy data or low intrinsic energy resolution of the detector. Therefore, alternative procedures are of interest. Here, we introduce a generalization of the idea of empirical dual-energy processing published by Stenner et al. to multi-energy, photon-counting CT raw data. Instead of working in the image-domain, we use prior spectral knowledge about the acquisition system (tube spectra, bin sensitivities) to parameterize the line-integrals of the basis component decomposition directly in the projection domain. We compare this empirical approach with the maximum-likelihood (ML) approach considering image noise and image bias (artifacts) and see that only moderate noise increase is to be expected for small bias in the empirical approach. Given the drastic reduction of pre-processing time, the empirical approach is considered a viable alternative to the ML approach.

  6. Efficient Detection of Repeating Sites to Accelerate Phylogenetic Likelihood Calculations.

    Science.gov (United States)

    Kobert, K; Stamatakis, A; Flouri, T

    2017-03-01

    The phylogenetic likelihood function (PLF) is the major computational bottleneck in several applications of evolutionary biology such as phylogenetic inference, species delimitation, model selection, and divergence times estimation. Given the alignment, a tree and the evolutionary model parameters, the likelihood function computes the conditional likelihood vectors for every node of the tree. Vector entries for which all input data are identical result in redundant likelihood operations which, in turn, yield identical conditional values. Such operations can be omitted for improving run-time and, using appropriate data structures, reducing memory usage. We present a fast, novel method for identifying and omitting such redundant operations in phylogenetic likelihood calculations, and assess the performance improvement and memory savings attained by our method. Using empirical and simulated data sets, we show that a prototype implementation of our method yields up to 12-fold speedups and uses up to 78% less memory than one of the fastest and most highly tuned implementations of the PLF currently available. Our method is generic and can seamlessly be integrated into any phylogenetic likelihood implementation. [Algorithms; maximum likelihood; phylogenetic likelihood function; phylogenetics]. © The Author(s) 2016. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

  7. On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood.

    Science.gov (United States)

    Karabatsos, George

    2018-06-01

    This article introduces a Bayesian method for testing the axioms of additive conjoint measurement. The method is based on an importance sampling algorithm that performs likelihood-free, approximate Bayesian inference using a synthetic likelihood to overcome the analytical intractability of this testing problem. This new method improves upon previous methods because it provides an omnibus test of the entire hierarchy of cancellation axioms, beyond double cancellation. It does so while accounting for the posterior uncertainty that is inherent in the empirical orderings that are implied by these axioms, together. The new method is illustrated through a test of the cancellation axioms on a classic survey data set, and through the analysis of simulated data.

  8. The phylogenetic likelihood library.

    Science.gov (United States)

    Flouri, T; Izquierdo-Carrasco, F; Darriba, D; Aberer, A J; Nguyen, L-T; Minh, B Q; Von Haeseler, A; Stamatakis, A

    2015-03-01

    We introduce the Phylogenetic Likelihood Library (PLL), a highly optimized application programming interface for developing likelihood-based phylogenetic inference and postanalysis software. The PLL implements appropriate data structures and functions that allow users to quickly implement common, error-prone, and labor-intensive tasks, such as likelihood calculations, model parameter as well as branch length optimization, and tree space exploration. The highly optimized and parallelized implementation of the phylogenetic likelihood function and a thorough documentation provide a framework for rapid development of scalable parallel phylogenetic software. By example of two likelihood-based phylogenetic codes we show that the PLL improves the sequential performance of current software by a factor of 2-10 while requiring only 1 month of programming time for integration. We show that, when numerical scaling for preventing floating point underflow is enabled, the double precision likelihood calculations in the PLL are up to 1.9 times faster than those in BEAGLE. On an empirical DNA dataset with 2000 taxa the AVX version of PLL is 4 times faster than BEAGLE (scaling enabled and required). The PLL is available at http://www.libpll.org under the GNU General Public License (GPL). © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

  9. Empirical likelihood based detection procedure for change point in mean residual life functions under random censorship.

    Science.gov (United States)

    Chen, Ying-Ju; Ning, Wei; Gupta, Arjun K

    2016-05-01

    The mean residual life (MRL) function is one of the basic parameters of interest in survival analysis that describes the expected remaining time of an individual after a certain age. The study of changes in the MRL function is practical and interesting because it may help us to identify some factors such as age and gender that may influence the remaining lifetimes of patients after receiving a certain surgery. In this paper, we propose a detection procedure based on the empirical likelihood for the changes in MRL functions with right censored data. Two real examples are also given: Veterans' administration lung cancer study and Stanford heart transplant to illustrate the detecting procedure. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  10. Likelihood-based methods for evaluating principal surrogacy in augmented vaccine trials.

    Science.gov (United States)

    Liu, Wei; Zhang, Bo; Zhang, Hui; Zhang, Zhiwei

    2017-04-01

    There is growing interest in assessing immune biomarkers, which are quick to measure and potentially predictive of long-term efficacy, as surrogate endpoints in randomized, placebo-controlled vaccine trials. This can be done under a principal stratification approach, with principal strata defined using a subject's potential immune responses to vaccine and placebo (the latter may be assumed to be zero). In this context, principal surrogacy refers to the extent to which vaccine efficacy varies across principal strata. Because a placebo recipient's potential immune response to vaccine is unobserved in a standard vaccine trial, augmented vaccine trials have been proposed to produce the information needed to evaluate principal surrogacy. This article reviews existing methods based on an estimated likelihood and a pseudo-score (PS) and proposes two new methods based on a semiparametric likelihood (SL) and a pseudo-likelihood (PL), for analyzing augmented vaccine trials. Unlike the PS method, the SL method does not require a model for missingness, which can be advantageous when immune response data are missing by happenstance. The SL method is shown to be asymptotically efficient, and it performs similarly to the PS and PL methods in simulation experiments. The PL method appears to have a computational advantage over the PS and SL methods.

  11. Stability of maximum-likelihood-based clustering methods: exploring the backbone of classifications

    International Nuclear Information System (INIS)

    Mungan, Muhittin; Ramasco, José J

    2010-01-01

    Components of complex systems are often classified according to the way they interact with each other. In graph theory such groups are known as clusters or communities. Many different techniques have been recently proposed to detect them, some of which involve inference methods using either Bayesian or maximum likelihood approaches. In this paper, we study a statistical model designed for detecting clusters based on connection similarity. The basic assumption of the model is that the graph was generated by a certain grouping of the nodes and an expectation maximization algorithm is employed to infer that grouping. We show that the method admits further development to yield a stability analysis of the groupings that quantifies the extent to which each node influences its neighbors' group membership. Our approach naturally allows for the identification of the key elements responsible for the grouping and their resilience to changes in the network. Given the generality of the assumptions underlying the statistical model, such nodes are likely to play special roles in the original system. We illustrate this point by analyzing several empirical networks for which further information about the properties of the nodes is available. The search and identification of stabilizing nodes constitutes thus a novel technique to characterize the relevance of nodes in complex networks

  12. Maximum-likelihood method for numerical inversion of Mellin transform

    International Nuclear Information System (INIS)

    Iqbal, M.

    1997-01-01

    A method is described for inverting the Mellin transform which uses an expansion in Laguerre polynomials and converts the Mellin transform to Laplace transform, then the maximum-likelihood regularization method is used to recover the original function of the Mellin transform. The performance of the method is illustrated by the inversion of the test functions available in the literature (J. Inst. Math. Appl., 20 (1977) 73; Math. Comput., 53 (1989) 589). Effectiveness of the method is shown by results obtained through demonstration by means of tables and diagrams

  13. Methods for Calculating Empires in Quasicrystals

    Directory of Open Access Journals (Sweden)

    Fang Fang

    2017-10-01

    Full Text Available This paper reviews the empire problem for quasiperiodic tilings and the existing methods for generating the empires of the vertex configurations in quasicrystals, while introducing a new and more efficient method based on the cut-and-project technique. Using Penrose tiling as an example, this method finds the forced tiles with the restrictions in the high dimensional lattice (the mother lattice that can be cut-and-projected into the lower dimensional quasicrystal. We compare our method to the two existing methods, namely one method that uses the algorithm of the Fibonacci chain to force the Ammann bars in order to find the forced tiles of an empire and the method that follows the work of N.G. de Bruijn on constructing a Penrose tiling as the dual to a pentagrid. This new method is not only conceptually simple and clear, but it also allows us to calculate the empires of the vertex configurations in a defected quasicrystal by reversing the configuration of the quasicrystal to its higher dimensional lattice, where we then apply the restrictions. These advantages may provide a key guiding principle for phason dynamics and an important tool for self error-correction in quasicrystal growth.

  14. Likelihood ratio data to report the validation of a forensic fingerprint evaluation method

    NARCIS (Netherlands)

    Ramos, Daniel; Haraksim, Rudolf; Meuwly, Didier

    2017-01-01

    Data to which the authors refer to throughout this article are likelihood ratios (LR) computed from the comparison of 5–12 minutiae fingermarks with fingerprints. These LRs data are used for the validation of a likelihood ratio (LR) method in forensic evidence evaluation. These data present a

  15. Efficient simulation and likelihood methods for non-neutral multi-allele models.

    Science.gov (United States)

    Joyce, Paul; Genz, Alan; Buzbas, Erkan Ozge

    2012-06-01

    Throughout the 1980s, Simon Tavaré made numerous significant contributions to population genetics theory. As genetic data, in particular DNA sequence, became more readily available, a need to connect population-genetic models to data became the central issue. The seminal work of Griffiths and Tavaré (1994a , 1994b , 1994c) was among the first to develop a likelihood method to estimate the population-genetic parameters using full DNA sequences. Now, we are in the genomics era where methods need to scale-up to handle massive data sets, and Tavaré has led the way to new approaches. However, performing statistical inference under non-neutral models has proved elusive. In tribute to Simon Tavaré, we present an article in spirit of his work that provides a computationally tractable method for simulating and analyzing data under a class of non-neutral population-genetic models. Computational methods for approximating likelihood functions and generating samples under a class of allele-frequency based non-neutral parent-independent mutation models were proposed by Donnelly, Nordborg, and Joyce (DNJ) (Donnelly et al., 2001). DNJ (2001) simulated samples of allele frequencies from non-neutral models using neutral models as auxiliary distribution in a rejection algorithm. However, patterns of allele frequencies produced by neutral models are dissimilar to patterns of allele frequencies produced by non-neutral models, making the rejection method inefficient. For example, in some cases the methods in DNJ (2001) require 10(9) rejections before a sample from the non-neutral model is accepted. Our method simulates samples directly from the distribution of non-neutral models, making simulation methods a practical tool to study the behavior of the likelihood and to perform inference on the strength of selection.

  16. Estimation Methods for Non-Homogeneous Regression - Minimum CRPS vs Maximum Likelihood

    Science.gov (United States)

    Gebetsberger, Manuel; Messner, Jakob W.; Mayr, Georg J.; Zeileis, Achim

    2017-04-01

    Non-homogeneous regression models are widely used to statistically post-process numerical weather prediction models. Such regression models correct for errors in mean and variance and are capable to forecast a full probability distribution. In order to estimate the corresponding regression coefficients, CRPS minimization is performed in many meteorological post-processing studies since the last decade. In contrast to maximum likelihood estimation, CRPS minimization is claimed to yield more calibrated forecasts. Theoretically, both scoring rules used as an optimization score should be able to locate a similar and unknown optimum. Discrepancies might result from a wrong distributional assumption of the observed quantity. To address this theoretical concept, this study compares maximum likelihood and minimum CRPS estimation for different distributional assumptions. First, a synthetic case study shows that, for an appropriate distributional assumption, both estimation methods yield to similar regression coefficients. The log-likelihood estimator is slightly more efficient. A real world case study for surface temperature forecasts at different sites in Europe confirms these results but shows that surface temperature does not always follow the classical assumption of a Gaussian distribution. KEYWORDS: ensemble post-processing, maximum likelihood estimation, CRPS minimization, probabilistic temperature forecasting, distributional regression models

  17. Empirical evaluation methods in computer vision

    CERN Document Server

    Christensen, Henrik I

    2002-01-01

    This book provides comprehensive coverage of methods for the empirical evaluation of computer vision techniques. The practical use of computer vision requires empirical evaluation to ensure that the overall system has a guaranteed performance. The book contains articles that cover the design of experiments for evaluation, range image segmentation, the evaluation of face recognition and diffusion methods, image matching using correlation methods, and the performance of medical image processing algorithms. Sample Chapter(s). Foreword (228 KB). Chapter 1: Introduction (505 KB). Contents: Automate

  18. Average Likelihood Methods of Classification of Code Division Multiple Access (CDMA)

    Science.gov (United States)

    2016-05-01

    subject to code matrices that follows the structure given by (113). [⃗ yR y⃗I ] = √ Es 2L [ GR1 −GI1 GI2 GR2 ] [ QR −QI QI QR ] [⃗ bR b⃗I ] + [⃗ nR n⃗I... QR ] [⃗ b+ b⃗− ] + [⃗ n+ n⃗− ] (115) The average likelihood for type 4 CDMA (116) is a special case of type 1 CDMA with twice the code length and...AVERAGE LIKELIHOOD METHODS OF CLASSIFICATION OF CODE DIVISION MULTIPLE ACCESS (CDMA) MAY 2016 FINAL TECHNICAL REPORT APPROVED FOR PUBLIC RELEASE

  19. Maximum-likelihood methods for array processing based on time-frequency distributions

    Science.gov (United States)

    Zhang, Yimin; Mu, Weifeng; Amin, Moeness G.

    1999-11-01

    This paper proposes a novel time-frequency maximum likelihood (t-f ML) method for direction-of-arrival (DOA) estimation for non- stationary signals, and compares this method with conventional maximum likelihood DOA estimation techniques. Time-frequency distributions localize the signal power in the time-frequency domain, and as such enhance the effective SNR, leading to improved DOA estimation. The localization of signals with different t-f signatures permits the division of the time-frequency domain into smaller regions, each contains fewer signals than those incident on the array. The reduction of the number of signals within different time-frequency regions not only reduces the required number of sensors, but also decreases the computational load in multi- dimensional optimizations. Compared to the recently proposed time- frequency MUSIC (t-f MUSIC), the proposed t-f ML method can be applied in coherent environments, without the need to perform any type of preprocessing that is subject to both array geometry and array aperture.

  20. A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation

    NARCIS (Netherlands)

    Meuwly, Didier; Ramos, Daniel; Haraksim, Rudolf

    2017-01-01

    This Guideline proposes a protocol for the validation of forensic evaluation methods at the source level, using the Likelihood Ratio framework as defined within the Bayes’ inference model. In the context of the inference of identity of source, the Likelihood Ratio is used to evaluate the strength of

  1. Failed refutations: further comments on parsimony and likelihood methods and their relationship to Popper's degree of corroboration.

    Science.gov (United States)

    de Queiroz, Kevin; Poe, Steven

    2003-06-01

    Kluge's (2001, Syst. Biol. 50:322-330) continued arguments that phylogenetic methods based on the statistical principle of likelihood are incompatible with the philosophy of science described by Karl Popper are based on false premises related to Kluge's misrepresentations of Popper's philosophy. Contrary to Kluge's conjectures, likelihood methods are not inherently verificationist; they do not treat every instance of a hypothesis as confirmation of that hypothesis. The historical nature of phylogeny does not preclude phylogenetic hypotheses from being evaluated using the probability of evidence. The low absolute probabilities of hypotheses are irrelevant to the correct interpretation of Popper's concept termed degree of corroboration, which is defined entirely in terms of relative probabilities. Popper did not advocate minimizing background knowledge; in any case, the background knowledge of both parsimony and likelihood methods consists of the general assumption of descent with modification and additional assumptions that are deterministic, concerning which tree is considered most highly corroborated. Although parsimony methods do not assume (in the sense of entailing) that homoplasy is rare, they do assume (in the sense of requiring to obtain a correct phylogenetic inference) certain things about patterns of homoplasy. Both parsimony and likelihood methods assume (in the sense of implying by the manner in which they operate) various things about evolutionary processes, although violation of those assumptions does not always cause the methods to yield incorrect phylogenetic inferences. Test severity is increased by sampling additional relevant characters rather than by character reanalysis, although either interpretation is compatible with the use of phylogenetic likelihood methods. Neither parsimony nor likelihood methods assess test severity (critical evidence) when used to identify a most highly corroborated tree(s) based on a single method or model and a

  2. Maximum-likelihood model averaging to profile clustering of site types across discrete linear sequences.

    Directory of Open Access Journals (Sweden)

    Zhang Zhang

    2009-06-01

    Full Text Available A major analytical challenge in computational biology is the detection and description of clusters of specified site types, such as polymorphic or substituted sites within DNA or protein sequences. Progress has been stymied by a lack of suitable methods to detect clusters and to estimate the extent of clustering in discrete linear sequences, particularly when there is no a priori specification of cluster size or cluster count. Here we derive and demonstrate a maximum likelihood method of hierarchical clustering. Our method incorporates a tripartite divide-and-conquer strategy that models sequence heterogeneity, delineates clusters, and yields a profile of the level of clustering associated with each site. The clustering model may be evaluated via model selection using the Akaike Information Criterion, the corrected Akaike Information Criterion, and the Bayesian Information Criterion. Furthermore, model averaging using weighted model likelihoods may be applied to incorporate model uncertainty into the profile of heterogeneity across sites. We evaluated our method by examining its performance on a number of simulated datasets as well as on empirical polymorphism data from diverse natural alleles of the Drosophila alcohol dehydrogenase gene. Our method yielded greater power for the detection of clustered sites across a breadth of parameter ranges, and achieved better accuracy and precision of estimation of clusters, than did the existing empirical cumulative distribution function statistics.

  3. Regularization parameter selection methods for ill-posed Poisson maximum likelihood estimation

    International Nuclear Information System (INIS)

    Bardsley, Johnathan M; Goldes, John

    2009-01-01

    In image processing applications, image intensity is often measured via the counting of incident photons emitted by the object of interest. In such cases, image data noise is accurately modeled by a Poisson distribution. This motivates the use of Poisson maximum likelihood estimation for image reconstruction. However, when the underlying model equation is ill-posed, regularization is needed. Regularized Poisson likelihood estimation has been studied extensively by the authors, though a problem of high importance remains: the choice of the regularization parameter. We will present three statistically motivated methods for choosing the regularization parameter, and numerical examples will be presented to illustrate their effectiveness

  4. Updated logistic regression equations for the calculation of post-fire debris-flow likelihood in the western United States

    Science.gov (United States)

    Staley, Dennis M.; Negri, Jacquelyn A.; Kean, Jason W.; Laber, Jayme L.; Tillery, Anne C.; Youberg, Ann M.

    2016-06-30

    Wildfire can significantly alter the hydrologic response of a watershed to the extent that even modest rainstorms can generate dangerous flash floods and debris flows. To reduce public exposure to hazard, the U.S. Geological Survey produces post-fire debris-flow hazard assessments for select fires in the western United States. We use publicly available geospatial data describing basin morphology, burn severity, soil properties, and rainfall characteristics to estimate the statistical likelihood that debris flows will occur in response to a storm of a given rainfall intensity. Using an empirical database and refined geospatial analysis methods, we defined new equations for the prediction of debris-flow likelihood using logistic regression methods. We showed that the new logistic regression model outperformed previous models used to predict debris-flow likelihood.

  5. Extended likelihood inference in reliability

    International Nuclear Information System (INIS)

    Martz, H.F. Jr.; Beckman, R.J.; Waller, R.A.

    1978-10-01

    Extended likelihood methods of inference are developed in which subjective information in the form of a prior distribution is combined with sampling results by means of an extended likelihood function. The extended likelihood function is standardized for use in obtaining extended likelihood intervals. Extended likelihood intervals are derived for the mean of a normal distribution with known variance, the failure-rate of an exponential distribution, and the parameter of a binomial distribution. Extended second-order likelihood methods are developed and used to solve several prediction problems associated with the exponential and binomial distributions. In particular, such quantities as the next failure-time, the number of failures in a given time period, and the time required to observe a given number of failures are predicted for the exponential model with a gamma prior distribution on the failure-rate. In addition, six types of life testing experiments are considered. For the binomial model with a beta prior distribution on the probability of nonsurvival, methods are obtained for predicting the number of nonsurvivors in a given sample size and for predicting the required sample size for observing a specified number of nonsurvivors. Examples illustrate each of the methods developed. Finally, comparisons are made with Bayesian intervals in those cases where these are known to exist

  6. Likelihood devices in spatial statistics

    NARCIS (Netherlands)

    Zwet, E.W. van

    1999-01-01

    One of the main themes of this thesis is the application to spatial data of modern semi- and nonparametric methods. Another, closely related theme is maximum likelihood estimation from spatial data. Maximum likelihood estimation is not common practice in spatial statistics. The method of moments

  7. FPGA Acceleration of the phylogenetic likelihood function for Bayesian MCMC inference methods

    Directory of Open Access Journals (Sweden)

    Bakos Jason D

    2010-04-01

    Full Text Available Abstract Background Likelihood (ML-based phylogenetic inference has become a popular method for estimating the evolutionary relationships among species based on genomic sequence data. This method is used in applications such as RAxML, GARLI, MrBayes, PAML, and PAUP. The Phylogenetic Likelihood Function (PLF is an important kernel computation for this method. The PLF consists of a loop with no conditional behavior or dependencies between iterations. As such it contains a high potential for exploiting parallelism using micro-architectural techniques. In this paper, we describe a technique for mapping the PLF and supporting logic onto a Field Programmable Gate Array (FPGA-based co-processor. By leveraging the FPGA's on-chip DSP modules and the high-bandwidth local memory attached to the FPGA, the resultant co-processor can accelerate ML-based methods and outperform state-of-the-art multi-core processors. Results We use the MrBayes 3 tool as a framework for designing our co-processor. For large datasets, we estimate that our accelerated MrBayes, if run on a current-generation FPGA, achieves a 10× speedup relative to software running on a state-of-the-art server-class microprocessor. The FPGA-based implementation achieves its performance by deeply pipelining the likelihood computations, performing multiple floating-point operations in parallel, and through a natural log approximation that is chosen specifically to leverage a deeply pipelined custom architecture. Conclusions Heterogeneous computing, which combines general-purpose processors with special-purpose co-processors such as FPGAs and GPUs, is a promising approach for high-performance phylogeny inference as shown by the growing body of literature in this field. FPGAs in particular are well-suited for this task because of their low power consumption as compared to many-core processors and Graphics Processor Units (GPUs 1.

  8. Composite likelihood estimation of demographic parameters

    Directory of Open Access Journals (Sweden)

    Garrigan Daniel

    2009-11-01

    Full Text Available Abstract Background Most existing likelihood-based methods for fitting historical demographic models to DNA sequence polymorphism data to do not scale feasibly up to the level of whole-genome data sets. Computational economies can be achieved by incorporating two forms of pseudo-likelihood: composite and approximate likelihood methods. Composite likelihood enables scaling up to large data sets because it takes the product of marginal likelihoods as an estimator of the likelihood of the complete data set. This approach is especially useful when a large number of genomic regions constitutes the data set. Additionally, approximate likelihood methods can reduce the dimensionality of the data by summarizing the information in the original data by either a sufficient statistic, or a set of statistics. Both composite and approximate likelihood methods hold promise for analyzing large data sets or for use in situations where the underlying demographic model is complex and has many parameters. This paper considers a simple demographic model of allopatric divergence between two populations, in which one of the population is hypothesized to have experienced a founder event, or population bottleneck. A large resequencing data set from human populations is summarized by the joint frequency spectrum, which is a matrix of the genomic frequency spectrum of derived base frequencies in two populations. A Bayesian Metropolis-coupled Markov chain Monte Carlo (MCMCMC method for parameter estimation is developed that uses both composite and likelihood methods and is applied to the three different pairwise combinations of the human population resequence data. The accuracy of the method is also tested on data sets sampled from a simulated population model with known parameters. Results The Bayesian MCMCMC method also estimates the ratio of effective population size for the X chromosome versus that of the autosomes. The method is shown to estimate, with reasonable

  9. Cox regression with missing covariate data using a modified partial likelihood method

    DEFF Research Database (Denmark)

    Martinussen, Torben; Holst, Klaus K.; Scheike, Thomas H.

    2016-01-01

    Missing covariate values is a common problem in survival analysis. In this paper we propose a novel method for the Cox regression model that is close to maximum likelihood but avoids the use of the EM-algorithm. It exploits that the observed hazard function is multiplicative in the baseline hazard...

  10. Generalized Likelihood Uncertainty Estimation (GLUE) Using Multi-Optimization Algorithm as Sampling Method

    Science.gov (United States)

    Wang, Z.

    2015-12-01

    For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.

  11. Preliminary application of maximum likelihood method in HL-2A Thomson scattering system

    International Nuclear Information System (INIS)

    Yao Ke; Huang Yuan; Feng Zhen; Liu Chunhua; Li Enping; Nie Lin

    2010-01-01

    Maximum likelihood method to process the data of HL-2A Thomson scattering system is presented. Using mathematical statistics, this method maximizes the possibility of the likeness between the theoretical data and the observed data, so that we could get more accurate result. It has been proved to be applicable in comparison with that of the ratios method, and some of the drawbacks in ratios method do not exist in this new one. (authors)

  12. Application of Generalized Student’s T-Distribution In Modeling The Distribution of Empirical Return Rates on Selected Stock Exchange Indexes

    Directory of Open Access Journals (Sweden)

    Purczyńskiz Jan

    2014-07-01

    Full Text Available This paper examines the application of the so called generalized Student’s t-distribution in modeling the distribution of empirical return rates on selected Warsaw stock exchange indexes. It deals with distribution parameters by means of the method of logarithmic moments, the maximum likelihood method and the method of moments. Generalized Student’s t-distribution ensures better fitting to empirical data than the classical Student’s t-distribution.

  13. Ringing Artefact Reduction By An Efficient Likelihood Improvement Method

    Science.gov (United States)

    Fuderer, Miha

    1989-10-01

    In MR imaging, the extent of the acquired spatial frequencies of the object is necessarily finite. The resulting image shows artefacts caused by "truncation" of its Fourier components. These are known as Gibbs artefacts or ringing artefacts. These artefacts are particularly. visible when the time-saving reduced acquisition method is used, say, when scanning only the lowest 70% of the 256 data lines. Filtering the data results in loss of resolution. A method is described that estimates the high frequency data from the low-frequency data lines, with the likelihood of the image as criterion. It is a computationally very efficient method, since it requires practically only two extra Fourier transforms, in addition to the normal. reconstruction. The results of this method on MR images of human subjects are promising. Evaluations on a 70% acquisition image show about 20% decrease of the error energy after processing. "Error energy" is defined as the total power of the difference to a 256-data-lines reference image. The elimination of ringing artefacts then appears almost complete..

  14. Philosophy and phylogenetic inference: a comparison of likelihood and parsimony methods in the context of Karl Popper's writings on corroboration.

    Science.gov (United States)

    de Queiroz, K; Poe, S

    2001-06-01

    Advocates of cladistic parsimony methods have invoked the philosophy of Karl Popper in an attempt to argue for the superiority of those methods over phylogenetic methods based on Ronald Fisher's statistical principle of likelihood. We argue that the concept of likelihood in general, and its application to problems of phylogenetic inference in particular, are highly compatible with Popper's philosophy. Examination of Popper's writings reveals that his concept of corroboration is, in fact, based on likelihood. Moreover, because probabilistic assumptions are necessary for calculating the probabilities that define Popper's corroboration, likelihood methods of phylogenetic inference--with their explicit probabilistic basis--are easily reconciled with his concept. In contrast, cladistic parsimony methods, at least as described by certain advocates of those methods, are less easily reconciled with Popper's concept of corroboration. If those methods are interpreted as lacking probabilistic assumptions, then they are incompatible with corroboration. Conversely, if parsimony methods are to be considered compatible with corroboration, then they must be interpreted as carrying implicit probabilistic assumptions. Thus, the non-probabilistic interpretation of cladistic parsimony favored by some advocates of those methods is contradicted by an attempt by the same authors to justify parsimony methods in terms of Popper's concept of corroboration. In addition to being compatible with Popperian corroboration, the likelihood approach to phylogenetic inference permits researchers to test the assumptions of their analytical methods (models) in a way that is consistent with Popper's ideas about the provisional nature of background knowledge.

  15. β-empirical Bayes inference and model diagnosis of microarray data

    Directory of Open Access Journals (Sweden)

    Hossain Mollah Mohammad

    2012-06-01

    Full Text Available Abstract Background Microarray data enables the high-throughput survey of mRNA expression profiles at the genomic level; however, the data presents a challenging statistical problem because of the large number of transcripts with small sample sizes that are obtained. To reduce the dimensionality, various Bayesian or empirical Bayes hierarchical models have been developed. However, because of the complexity of the microarray data, no model can explain the data fully. It is generally difficult to scrutinize the irregular patterns of expression that are not expected by the usual statistical gene by gene models. Results As an extension of empirical Bayes (EB procedures, we have developed the β-empirical Bayes (β-EB approach based on a β-likelihood measure which can be regarded as an ’evidence-based’ weighted (quasi- likelihood inference. The weight of a transcript t is described as a power function of its likelihood, fβ(yt|θ. Genes with low likelihoods have unexpected expression patterns and low weights. By assigning low weights to outliers, the inference becomes robust. The value of β, which controls the balance between the robustness and efficiency, is selected by maximizing the predictive β0-likelihood by cross-validation. The proposed β-EB approach identified six significant (p−5 contaminated transcripts as differentially expressed (DE in normal/tumor tissues from the head and neck of cancer patients. These six genes were all confirmed to be related to cancer; they were not identified as DE genes by the classical EB approach. When applied to the eQTL analysis of Arabidopsis thaliana, the proposed β-EB approach identified some potential master regulators that were missed by the EB approach. Conclusions The simulation data and real gene expression data showed that the proposed β-EB method was robust against outliers. The distribution of the weights was used to scrutinize the irregular patterns of expression and diagnose the model

  16. Corporate governance effect on financial distress likelihood: Evidence from Spain

    Directory of Open Access Journals (Sweden)

    Montserrat Manzaneque

    2016-01-01

    Full Text Available The paper explores some mechanisms of corporate governance (ownership and board characteristics in Spanish listed companies and their impact on the likelihood of financial distress. An empirical study was conducted between 2007 and 2012 using a matched-pairs research design with 308 observations, with half of them classified as distressed and non-distressed. Based on the previous study by Pindado, Rodrigues, and De la Torre (2008, a broader concept of bankruptcy is used to define business failure. Employing several conditional logistic models, as well as to other previous studies on bankruptcy, the results confirm that in difficult situations prior to bankruptcy, the impact of board ownership and proportion of independent directors on business failure likelihood are similar to those exerted in more extreme situations. These results go one step further, to offer a negative relationship between board size and the likelihood of financial distress. This result is interpreted as a form of creating diversity and to improve the access to the information and resources, especially in contexts where the ownership is highly concentrated and large shareholders have a great power to influence the board structure. However, the results confirm that ownership concentration does not have a significant impact on financial distress likelihood in the Spanish context. It is argued that large shareholders are passive as regards an enhanced monitoring of management and, alternatively, they do not have enough incentives to hold back the financial distress. These findings have important implications in the Spanish context, where several changes in the regulatory listing requirements have been carried out with respect to corporate governance, and where there is no empirical evidence regarding this respect.

  17. Maximum likelihood versus likelihood-free quantum system identification in the atom maser

    International Nuclear Information System (INIS)

    Catana, Catalin; Kypraios, Theodore; Guţă, Mădălin

    2014-01-01

    We consider the problem of estimating a dynamical parameter of a Markovian quantum open system (the atom maser), by performing continuous time measurements in the system's output (outgoing atoms). Two estimation methods are investigated and compared. Firstly, the maximum likelihood estimator (MLE) takes into account the full measurement data and is asymptotically optimal in terms of its mean square error. Secondly, the ‘likelihood-free’ method of approximate Bayesian computation (ABC) produces an approximation of the posterior distribution for a given set of summary statistics, by sampling trajectories at different parameter values and comparing them with the measurement data via chosen statistics. Building on previous results which showed that atom counts are poor statistics for certain values of the Rabi angle, we apply MLE to the full measurement data and estimate its Fisher information. We then select several correlation statistics such as waiting times, distribution of successive identical detections, and use them as input of the ABC algorithm. The resulting posterior distribution follows closely the data likelihood, showing that the selected statistics capture ‘most’ statistical information about the Rabi angle. (paper)

  18. Empirical pillar design methods review report: Final report

    International Nuclear Information System (INIS)

    1988-02-01

    This report summarizes and evaluates empirical pillar design methods that may be of use during the conceptual design of a high-level nuclear waste repository in salt. The methods are discussed according to category (i.e, main, submain, and panel pillars; barrier pillars; and shaft pillars). Of the 21 identified for main, submain, and panel pillars, one method, the Confined Core Method, is evaluated as being most appropriate for conceptual design. Five methods are considered potentially applicable. Of six methods identified for barrier pillars, one method based on the Load Transfer Distance concept is considered most appropriate for design. Based on the evaluation of 25 methods identified for shaft pillars, an approximate sizing criterion is proposed for use in conceptual design. Aspects of pillar performance relating to creep, ground deformation, interaction with roof and floor rock, and response to high temperature environments are not adequately addressed by existing empirical design methods. 152 refs., 22 figs., 14 tabs

  19. Validation of Likelihood Ratio Methods Used for Forensic Evidence Evaluation: Application in Forensic Fingerprints

    NARCIS (Netherlands)

    Haraksim, Rudolf

    2014-01-01

    In this chapter the Likelihood Ratio (LR) inference model will be introduced, the theoretical aspects of probabilities will be discussed and the validation framework for LR methods used for forensic evidence evaluation will be presented. Prior to introducing the validation framework, following

  20. Semi-Parametric Maximum Likelihood Method for Interaction in Case-Mother Control-Mother Designs: Package SPmlficmcm

    Directory of Open Access Journals (Sweden)

    Moliere Nguile-Makao

    2015-12-01

    Full Text Available The analysis of interaction effects involving genetic variants and environmental exposures on the risk of adverse obstetric and early-life outcomes is generally performed using standard logistic regression in the case-mother and control-mother design. However such an analysis is inefficient because it does not take into account the natural family-based constraints present in the parent-child relationship. Recently, a new approach based on semi-parametric maximum likelihood estimation was proposed. The advantage of this approach is that it takes into account the parental relationship between the mother and her child in estimation. But a package implementing this method has not been widely available. In this paper, we present SPmlficmcm, an R package implementing this new method and we propose an extension of the method to handle missing offspring genotype data by maximum likelihood estimation. Our choice to treat missing data of the offspring genotype was motivated by the fact that in genetic association studies where the genetic data of mother and child are available, there are usually more missing data on the genotype of the offspring than that of the mother. The package builds a non-linear system from the data and solves and computes the estimates from the gradient and the Hessian matrix of the log profile semi-parametric likelihood function. Finally, we analyze a simulated dataset to show the usefulness of the package.

  1. Discrimination method of large log-likelihood study in differential diagnosis of pulmonary diffuse mild micro-nodule

    International Nuclear Information System (INIS)

    Chen Budong; Ma Daqing; He Wen; Tang Hongqu; Qian Linxue; Zhou Ronglin

    2001-01-01

    Objective: To analyze HRCT and thin-slice CT scan findings in 150 patients with pulmonary diffuse mild micro-nodule, and to find the features with the purpose of identifying random micro-nodule, peri-lymphatic micro-nodule, and centrilobular micro-nodule. Methods: The useful features i 150 patients with pulmonary diffuse mild micro-nodule were translated into scores by means of discrimination method of large log-likelihood to identify the micro-nodular category. Results: The accuracy of diagnosis was 94.0% for random micro-nodule, 76.0% for peri-lymphatic micro-nodule, and 90.0% for centrilobular micro-nodule. Conclusion: HRCT and thin-slice CT scans were helpful in differential diagnosis of pulmonary diffuse mild micro-nodule. The discrimination method of large log-likelihood was propitious to diagnosis and differential diagnosis

  2. Dissociating response conflict and error likelihood in anterior cingulate cortex.

    Science.gov (United States)

    Yeung, Nick; Nieuwenhuis, Sander

    2009-11-18

    Neuroimaging studies consistently report activity in anterior cingulate cortex (ACC) in conditions of high cognitive demand, leading to the view that ACC plays a crucial role in the control of cognitive processes. According to one prominent theory, the sensitivity of ACC to task difficulty reflects its role in monitoring for the occurrence of competition, or "conflict," between responses to signal the need for increased cognitive control. However, a contrasting theory proposes that ACC is the recipient rather than source of monitoring signals, and that ACC activity observed in relation to task demand reflects the role of this region in learning about the likelihood of errors. Response conflict and error likelihood are typically confounded, making the theories difficult to distinguish empirically. The present research therefore used detailed computational simulations to derive contrasting predictions regarding ACC activity and error rate as a function of response speed. The simulations demonstrated a clear dissociation between conflict and error likelihood: fast response trials are associated with low conflict but high error likelihood, whereas slow response trials show the opposite pattern. Using the N2 component as an index of ACC activity, an EEG study demonstrated that when conflict and error likelihood are dissociated in this way, ACC activity tracks conflict and is negatively correlated with error likelihood. These findings support the conflict-monitoring theory and suggest that, in speeded decision tasks, ACC activity reflects current task demands rather than the retrospective coding of past performance.

  3. Filtration of human EEG recordings from physiological artifacts with empirical mode method

    Science.gov (United States)

    Grubov, Vadim V.; Runnova, Anastasiya E.; Khramova, Marina V.

    2017-03-01

    In the paper we propose the new method for dealing with noise and physiological artifacts in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We consider noises and physiological artifacts on EEG as specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from eye-moving artifacts and show high efficiency of the method.

  4. Likelihood ratio data to report the validation of a forensic fingerprint evaluation method

    Directory of Open Access Journals (Sweden)

    Daniel Ramos

    2017-02-01

    Full Text Available Data to which the authors refer to throughout this article are likelihood ratios (LR computed from the comparison of 5–12 minutiae fingermarks with fingerprints. These LRs data are used for the validation of a likelihood ratio (LR method in forensic evidence evaluation. These data present a necessary asset for conducting validation experiments when validating LR methods used in forensic evidence evaluation and set up validation reports. These data can be also used as a baseline for comparing the fingermark evidence in the same minutiae configuration as presented in (D. Meuwly, D. Ramos, R. Haraksim, [1], although the reader should keep in mind that different feature extraction algorithms and different AFIS systems used may produce different LRs values. Moreover, these data may serve as a reproducibility exercise, in order to train the generation of validation reports of forensic methods, according to [1]. Alongside the data, a justification and motivation for the use of methods is given. These methods calculate LRs from the fingerprint/mark data and are subject to a validation procedure. The choice of using real forensic fingerprint in the validation and simulated data in the development is described and justified. Validation criteria are set for the purpose of validation of the LR methods, which are used to calculate the LR values from the data and the validation report. For privacy and data protection reasons, the original fingerprint/mark images cannot be shared. But these images do not constitute the core data for the validation, contrarily to the LRs that are shared.

  5. A Walk on the Wild Side: The Impact of Music on Risk-Taking Likelihood.

    Science.gov (United States)

    Enström, Rickard; Schmaltz, Rodney

    2017-01-01

    From a marketing perspective, there has been substantial interest in on the role of risk-perception on consumer behavior. Specific 'problem music' like rap and heavy metal has long been associated with delinquent behavior, including violence, drug use, and promiscuous sex. Although individuals' risk preferences have been investigated across a range of decision-making situations, there has been little empirical work demonstrating the direct role music may have on the likelihood of engaging in risky activities. In the exploratory study reported here, we assessed the impact of listening to different styles of music while assessing risk-taking likelihood through a psychometric scale. Risk-taking likelihood was measured across ethical, financial, health and safety, recreational and social domains. Through the means of a canonical correlation analysis, the multivariate relationship between different music styles and individual risk-taking likelihood across the different domains is discussed. Our results indicate that listening to different types of music does influence risk-taking likelihood, though not in areas of health and safety.

  6. Development of likelihood estimation method for criticality accidents of mixed oxide fuel fabrication facilities

    International Nuclear Information System (INIS)

    Tamaki, Hitoshi; Yoshida, Kazuo; Kimoto, Tatsuya; Hamaguchi, Yoshikane

    2010-01-01

    A criticality accident in a MOX fuel fabrication facility may occur depending on several parameters, such as mass inventory and plutonium enrichment. MOX handling units in the facility are designed and operated based on the double contingency principle to prevent criticality accidents. Control failures of at least two parameters are needed for the occurrence of criticality accident. To evaluate the probability of such control failures, the criticality conditions of each parameter for a specific handling unit are necessary for accident scenario analysis to be clarified quantitatively with a criticality analysis computer code. In addition to this issue, a computer-based control system for mass inventory is planned to be installed into MOX handling equipment in a commercial MOX fuel fabrication plant. The reliability analysis is another important issue in evaluating the likelihood of control failure caused by software malfunction. A likelihood estimation method for criticality accident has been developed with these issues been taken into consideration. In this paper, an example of analysis with the proposed method and the applicability of the method are also shown through a trial application to a model MOX fabrication facility. (author)

  7. Measurement of the Top Quark Mass by Dynamical Likelihood Method using the Lepton + Jets Events with the Collider Detector at Fermilab

    Energy Technology Data Exchange (ETDEWEB)

    Kubo, Taichi [Univ. of Tsukuba (Japan)

    2008-02-01

    We have measured the top quark mass with the dynamical likelihood method. The data corresponding to an integrated luminosity of 1.7fb-1 was collected in proton antiproton collisions at a center of mass energy of 1.96 TeV with the CDF detector at Fermilab Tevatron during the period March 2002-March 2007. We select t$\\bar{t}$ pair production candidates by requiring one high energy lepton and four jets, in which at least one of jets must be tagged as a b-jet. In order to reconstruct the top quark mass, we use the dynamical likelihood method based on maximum likelihood method where a likelihood is defined as the differential cross section multiplied by the transfer function from observed quantities to parton quantities, as a function of the top quark mass and the jet energy scale(JES). With this method, we measure the top quark mass to be 171.6 ± 2.0 (stat.+ JES) ± 1.3(syst.) = 171.6 ± 2.4 GeV/c2.

  8. Unbinned likelihood analysis of EGRET observations

    International Nuclear Information System (INIS)

    Digel, Seth W.

    2000-01-01

    We present a newly-developed likelihood analysis method for EGRET data that defines the likelihood function without binning the photon data or averaging the instrumental response functions. The standard likelihood analysis applied to EGRET data requires the photons to be binned spatially and in energy, and the point-spread functions to be averaged over energy and inclination angle. The full-width half maximum of the point-spread function increases by about 40% from on-axis to 30 degree sign inclination, and depending on the binning in energy can vary by more than that in a single energy bin. The new unbinned method avoids the loss of information that binning and averaging cause and can properly analyze regions where EGRET viewing periods overlap and photons with different inclination angles would otherwise be combined in the same bin. In the poster, we describe the unbinned analysis method and compare its sensitivity with binned analysis for detecting point sources in EGRET data

  9. Climate reconstruction analysis using coexistence likelihood estimation (CRACLE): a method for the estimation of climate using vegetation.

    Science.gov (United States)

    Harbert, Robert S; Nixon, Kevin C

    2015-08-01

    • Plant distributions have long been understood to be correlated with the environmental conditions to which species are adapted. Climate is one of the major components driving species distributions. Therefore, it is expected that the plants coexisting in a community are reflective of the local environment, particularly climate.• Presented here is a method for the estimation of climate from local plant species coexistence data. The method, Climate Reconstruction Analysis using Coexistence Likelihood Estimation (CRACLE), is a likelihood-based method that employs specimen collection data at a global scale for the inference of species climate tolerance. CRACLE calculates the maximum joint likelihood of coexistence given individual species climate tolerance characterization to estimate the expected climate.• Plant distribution data for more than 4000 species were used to show that this method accurately infers expected climate profiles for 165 sites with diverse climatic conditions. Estimates differ from the WorldClim global climate model by less than 1.5°C on average for mean annual temperature and less than ∼250 mm for mean annual precipitation. This is a significant improvement upon other plant-based climate-proxy methods.• CRACLE validates long hypothesized interactions between climate and local associations of plant species. Furthermore, CRACLE successfully estimates climate that is consistent with the widely used WorldClim model and therefore may be applied to the quantitative estimation of paleoclimate in future studies. © 2015 Botanical Society of America, Inc.

  10. A Penalized Likelihood Framework For High-Dimensional Phylogenetic Comparative Methods And An Application To New-World Monkeys Brain Evolution.

    Science.gov (United States)

    Julien, Clavel; Leandro, Aristide; Hélène, Morlon

    2018-06-19

    Working with high-dimensional phylogenetic comparative datasets is challenging because likelihood-based multivariate methods suffer from low statistical performances as the number of traits p approaches the number of species n and because some computational complications occur when p exceeds n. Alternative phylogenetic comparative methods have recently been proposed to deal with the large p small n scenario but their use and performances are limited. Here we develop a penalized likelihood framework to deal with high-dimensional comparative datasets. We propose various penalizations and methods for selecting the intensity of the penalties. We apply this general framework to the estimation of parameters (the evolutionary trait covariance matrix and parameters of the evolutionary model) and model comparison for the high-dimensional multivariate Brownian (BM), Early-burst (EB), Ornstein-Uhlenbeck (OU) and Pagel's lambda models. We show using simulations that our penalized likelihood approach dramatically improves the estimation of evolutionary trait covariance matrices and model parameters when p approaches n, and allows for their accurate estimation when p equals or exceeds n. In addition, we show that penalized likelihood models can be efficiently compared using Generalized Information Criterion (GIC). We implement these methods, as well as the related estimation of ancestral states and the computation of phylogenetic PCA in the R package RPANDA and mvMORPH. Finally, we illustrate the utility of the new proposed framework by evaluating evolutionary models fit, analyzing integration patterns, and reconstructing evolutionary trajectories for a high-dimensional 3-D dataset of brain shape in the New World monkeys. We find a clear support for an Early-burst model suggesting an early diversification of brain morphology during the ecological radiation of the clade. Penalized likelihood offers an efficient way to deal with high-dimensional multivariate comparative data.

  11. Constraint likelihood analysis for a network of gravitational wave detectors

    International Nuclear Information System (INIS)

    Klimenko, S.; Rakhmanov, M.; Mitselmakher, G.; Mohanty, S.

    2005-01-01

    We propose a coherent method for detection and reconstruction of gravitational wave signals with a network of interferometric detectors. The method is derived by using the likelihood ratio functional for unknown signal waveforms. In the likelihood analysis, the global maximum of the likelihood ratio over the space of waveforms is used as the detection statistic. We identify a problem with this approach. In the case of an aligned pair of detectors, the detection statistic depends on the cross correlation between the detectors as expected, but this dependence disappears even for infinitesimally small misalignments. We solve the problem by applying constraints on the likelihood functional and obtain a new class of statistics. The resulting method can be applied to data from a network consisting of any number of detectors with arbitrary detector orientations. The method allows us reconstruction of the source coordinates and the waveforms of two polarization components of a gravitational wave. We study the performance of the method with numerical simulations and find the reconstruction of the source coordinates to be more accurate than in the standard likelihood method

  12. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

    Science.gov (United States)

    Guindon, Stéphane; Dufayard, Jean-François; Lefort, Vincent; Anisimova, Maria; Hordijk, Wim; Gascuel, Olivier

    2010-05-01

    PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.

  13. Maximum likelihood estimation for integrated diffusion processes

    DEFF Research Database (Denmark)

    Baltazar-Larios, Fernando; Sørensen, Michael

    We propose a method for obtaining maximum likelihood estimates of parameters in diffusion models when the data is a discrete time sample of the integral of the process, while no direct observations of the process itself are available. The data are, moreover, assumed to be contaminated...... EM-algorithm to obtain maximum likelihood estimates of the parameters in the diffusion model. As part of the algorithm, we use a recent simple method for approximate simulation of diffusion bridges. In simulation studies for the Ornstein-Uhlenbeck process and the CIR process the proposed method works...... by measurement errors. Integrated volatility is an example of this type of observations. Another example is ice-core data on oxygen isotopes used to investigate paleo-temperatures. The data can be viewed as incomplete observations of a model with a tractable likelihood function. Therefore we propose a simulated...

  14. A Walk on the Wild Side: The Impact of Music on Risk-Taking Likelihood

    Science.gov (United States)

    Enström, Rickard; Schmaltz, Rodney

    2017-01-01

    From a marketing perspective, there has been substantial interest in on the role of risk-perception on consumer behavior. Specific ‘problem music’ like rap and heavy metal has long been associated with delinquent behavior, including violence, drug use, and promiscuous sex. Although individuals’ risk preferences have been investigated across a range of decision-making situations, there has been little empirical work demonstrating the direct role music may have on the likelihood of engaging in risky activities. In the exploratory study reported here, we assessed the impact of listening to different styles of music while assessing risk-taking likelihood through a psychometric scale. Risk-taking likelihood was measured across ethical, financial, health and safety, recreational and social domains. Through the means of a canonical correlation analysis, the multivariate relationship between different music styles and individual risk-taking likelihood across the different domains is discussed. Our results indicate that listening to different types of music does influence risk-taking likelihood, though not in areas of health and safety. PMID:28539908

  15. A Walk on the Wild Side: The Impact of Music on Risk-Taking Likelihood

    Directory of Open Access Journals (Sweden)

    Rickard Enström

    2017-05-01

    Full Text Available From a marketing perspective, there has been substantial interest in on the role of risk-perception on consumer behavior. Specific ‘problem music’ like rap and heavy metal has long been associated with delinquent behavior, including violence, drug use, and promiscuous sex. Although individuals’ risk preferences have been investigated across a range of decision-making situations, there has been little empirical work demonstrating the direct role music may have on the likelihood of engaging in risky activities. In the exploratory study reported here, we assessed the impact of listening to different styles of music while assessing risk-taking likelihood through a psychometric scale. Risk-taking likelihood was measured across ethical, financial, health and safety, recreational and social domains. Through the means of a canonical correlation analysis, the multivariate relationship between different music styles and individual risk-taking likelihood across the different domains is discussed. Our results indicate that listening to different types of music does influence risk-taking likelihood, though not in areas of health and safety.

  16. Likelihood ratio meta-analysis: New motivation and approach for an old method.

    Science.gov (United States)

    Dormuth, Colin R; Filion, Kristian B; Platt, Robert W

    2016-03-01

    A 95% confidence interval (CI) in an updated meta-analysis may not have the expected 95% coverage. If a meta-analysis is simply updated with additional data, then the resulting 95% CI will be wrong because it will not have accounted for the fact that the earlier meta-analysis failed or succeeded to exclude the null. This situation can be avoided by using the likelihood ratio (LR) as a measure of evidence that does not depend on type-1 error. We show how an LR-based approach, first advanced by Goodman, can be used in a meta-analysis to pool data from separate studies to quantitatively assess where the total evidence points. The method works by estimating the log-likelihood ratio (LogLR) function from each study. Those functions are then summed to obtain a combined function, which is then used to retrieve the total effect estimate, and a corresponding 'intrinsic' confidence interval. Using as illustrations the CAPRIE trial of clopidogrel versus aspirin in the prevention of ischemic events, and our own meta-analysis of higher potency statins and the risk of acute kidney injury, we show that the LR-based method yields the same point estimate as the traditional analysis, but with an intrinsic confidence interval that is appropriately wider than the traditional 95% CI. The LR-based method can be used to conduct both fixed effect and random effects meta-analyses, it can be applied to old and new meta-analyses alike, and results can be presented in a format that is familiar to a meta-analytic audience. Copyright © 2016 Elsevier Inc. All rights reserved.

  17. Empirical Bayes Approaches to Multivariate Fuzzy Partitions.

    Science.gov (United States)

    Woodbury, Max A.; Manton, Kenneth G.

    1991-01-01

    An empirical Bayes-maximum likelihood estimation procedure is presented for the application of fuzzy partition models in describing high dimensional discrete response data. The model describes individuals in terms of partial membership in multiple latent categories that represent bounded discrete spaces. (SLD)

  18. Maximum Likelihood-Based Methods for Target Velocity Estimation with Distributed MIMO Radar

    Directory of Open Access Journals (Sweden)

    Zhenxin Cao

    2018-02-01

    Full Text Available The estimation problem for target velocity is addressed in this in the scenario with a distributed multi-input multi-out (MIMO radar system. A maximum likelihood (ML-based estimation method is derived with the knowledge of target position. Then, in the scenario without the knowledge of target position, an iterative method is proposed to estimate the target velocity by updating the position information iteratively. Moreover, the Carmér-Rao Lower Bounds (CRLBs for both scenarios are derived, and the performance degradation of velocity estimation without the position information is also expressed. Simulation results show that the proposed estimation methods can approach the CRLBs, and the velocity estimation performance can be further improved by increasing either the number of radar antennas or the information accuracy of the target position. Furthermore, compared with the existing methods, a better estimation performance can be achieved.

  19. Dealing with noise and physiological artifacts in human EEG recordings: empirical mode methods

    Science.gov (United States)

    Runnova, Anastasiya E.; Grubov, Vadim V.; Khramova, Marina V.; Hramov, Alexander E.

    2017-04-01

    In the paper we propose the new method for removing noise and physiological artifacts in human EEG recordings based on empirical mode decomposition (Hilbert-Huang transform). As physiological artifacts we consider specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the proposed method with steps including empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing these empirical modes and reconstructing of initial EEG signal. We show the efficiency of the method on the example of filtration of human EEG signal from eye-moving artifacts.

  20. Electronic structure prediction via data-mining the empirical pseudopotential method

    Energy Technology Data Exchange (ETDEWEB)

    Zenasni, H; Aourag, H [LEPM, URMER, Departement of Physics, University Abou Bakr Belkaid, Tlemcen 13000 (Algeria); Broderick, S R; Rajan, K [Department of Materials Science and Engineering, Iowa State University, Ames, Iowa 50011-2230 (United States)

    2010-01-15

    We introduce a new approach for accelerating the calculation of the electronic structure of new materials by utilizing the empirical pseudopotential method combined with data mining tools. Combining data mining with the empirical pseudopotential method allows us to convert an empirical approach to a predictive approach. Here we consider tetrahedrally bounded III-V Bi semiconductors, and through the prediction of form factors based on basic elemental properties we can model the band structure and charge density for these semi-conductors, for which limited results exist. This work represents a unique approach to modeling the electronic structure of a material which may be used to identify new promising semi-conductors and is one of the few efforts utilizing data mining at an electronic level. (Abstract Copyright [2010], Wiley Periodicals, Inc.)

  1. The unfolding of NaI(Tl) γ-ray spectrum based on maximum likelihood method

    International Nuclear Information System (INIS)

    Zhang Qingxian; Ge Liangquan; Gu Yi; Zeng Guoqiang; Lin Yanchang; Wang Guangxi

    2011-01-01

    NaI(Tl) detectors, having a good detection efficiency, are used to detect gamma rays in field surveys. But the poor energy resolution hinders their applications, despite the use of traditional methods to resolve the overlapped gamma-ray peaks. In this paper, the maximum likelihood (ML) solution is used to resolve the spectrum. The ML method,which is capable of decomposing the peaks in energy difference of over 2/3 FWHM, is applied to scale NaI(Tl) the spectrometer. The result shows that the net area is in proportion to the content of isotopes and the precision of scaling is better than the stripping ration method. (authors)

  2. Approximate Likelihood

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    Most physics results at the LHC end in a likelihood ratio test. This includes discovery and exclusion for searches as well as mass, cross-section, and coupling measurements. The use of Machine Learning (multivariate) algorithms in HEP is mainly restricted to searches, which can be reduced to classification between two fixed distributions: signal vs. background. I will show how we can extend the use of ML classifiers to distributions parameterized by physical quantities like masses and couplings as well as nuisance parameters associated to systematic uncertainties. This allows for one to approximate the likelihood ratio while still using a high dimensional feature vector for the data. Both the MEM and ABC approaches mentioned above aim to provide inference on model parameters (like cross-sections, masses, couplings, etc.). ABC is fundamentally tied Bayesian inference and focuses on the “likelihood free” setting where only a simulator is available and one cannot directly compute the likelihood for the dat...

  3. Tapered composite likelihood for spatial max-stable models

    KAUST Repository

    Sang, Huiyan

    2014-05-01

    Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.

  4. Tapered composite likelihood for spatial max-stable models

    KAUST Repository

    Sang, Huiyan; Genton, Marc G.

    2014-01-01

    Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.

  5. Deformation of log-likelihood loss function for multiclass boosting.

    Science.gov (United States)

    Kanamori, Takafumi

    2010-09-01

    The purpose of this paper is to study loss functions in multiclass classification. In classification problems, the decision function is estimated by minimizing an empirical loss function, and then, the output label is predicted by using the estimated decision function. We propose a class of loss functions which is obtained by a deformation of the log-likelihood loss function. There are four main reasons why we focus on the deformed log-likelihood loss function: (1) this is a class of loss functions which has not been deeply investigated so far, (2) in terms of computation, a boosting algorithm with a pseudo-loss is available to minimize the proposed loss function, (3) the proposed loss functions provide a clear correspondence between the decision functions and conditional probabilities of output labels, (4) the proposed loss functions satisfy the statistical consistency of the classification error rate which is a desirable property in classification problems. Based on (3), we show that the deformed log-likelihood loss provides a model of mislabeling which is useful as a statistical model of medical diagnostics. We also propose a robust loss function against outliers in multiclass classification based on our approach. The robust loss function is a natural extension of the existing robust loss function for binary classification. A model of mislabeling and a robust loss function are useful to cope with noisy data. Some numerical studies are presented to show the robustness of the proposed loss function. A mathematical characterization of the deformed log-likelihood loss function is also presented. Copyright 2010 Elsevier Ltd. All rights reserved.

  6. Searching for degenerate Higgs bosons using a profile likelihood ratio method

    CERN Document Server

    Heikkilä, Jaana

    ATLAS and CMS collaborations at the Large Hadron Collider have observed a new resonance con- sistent with the standard model Higgs boson. However, it has been suggested that the observed signal could also be produced by multiple nearly mass-degenerate states that couple differently to the standard model particles. In this work, a method to discriminate between the hypothesis of a single Higgs boson and that of multiple mass-degenerate Higgs bosons was developed. Using the matrix of measured signal strengths in different production and decay modes, parametrizations for the two hypotheses were constructed as a general rank 1 matrix and the most general $5 \\times 4$ matrix, respectively. The test statistic was defined as a ratio of profile likelihoods for the two hypotheses. The method was applied to the CMS measurements. The expected test statistic distribution was estimated twice by generating pseudo-experiments according to both the standard model hypothesis and the single Higgs boson hypothesis best fitting...

  7. What Happened to Remote Usability Testing? An Empirical Study of Three Methods

    DEFF Research Database (Denmark)

    Stage, Jan; Andreasen, M. S.; Nielsen, H. V.

    2007-01-01

    The idea of conducting usability tests remotely emerged ten years ago. Since then, it has been studied empirically, and some software organizations employ remote methods. Yet there are still few comparisons involving more than one remote method. This paper presents results from a systematic...... empirical comparison of three methods for remote usability testing and a conventional laboratorybased think-aloud method. The three remote methods are a remote synchronous condition, where testing is conducted in real time but the test monitor is separated spatially from the test subjects, and two remote...

  8. Detecting the contagion effect in mass killings; a constructive example of the statistical advantages of unbinned likelihood methods.

    Science.gov (United States)

    Towers, Sherry; Mubayi, Anuj; Castillo-Chavez, Carlos

    2018-01-01

    When attempting to statistically distinguish between a null and an alternative hypothesis, many researchers in the life and social sciences turn to binned statistical analysis methods, or methods that are simply based on the moments of a distribution (such as the mean, and variance). These methods have the advantage of simplicity of implementation, and simplicity of explanation. However, when null and alternative hypotheses manifest themselves in subtle differences in patterns in the data, binned analysis methods may be insensitive to these differences, and researchers may erroneously fail to reject the null hypothesis when in fact more sensitive statistical analysis methods might produce a different result when the null hypothesis is actually false. Here, with a focus on two recent conflicting studies of contagion in mass killings as instructive examples, we discuss how the use of unbinned likelihood methods makes optimal use of the information in the data; a fact that has been long known in statistical theory, but perhaps is not as widely appreciated amongst general researchers in the life and social sciences. In 2015, Towers et al published a paper that quantified the long-suspected contagion effect in mass killings. However, in 2017, Lankford & Tomek subsequently published a paper, based upon the same data, that claimed to contradict the results of the earlier study. The former used unbinned likelihood methods, and the latter used binned methods, and comparison of distribution moments. Using these analyses, we also discuss how visualization of the data can aid in determination of the most appropriate statistical analysis methods to distinguish between a null and alternate hypothesis. We also discuss the importance of assessment of the robustness of analysis results to methodological assumptions made (for example, arbitrary choices of number of bins and bin widths when using binned methods); an issue that is widely overlooked in the literature, but is critical

  9. Estimating rate of occurrence of rare events with empirical bayes: A railway application

    International Nuclear Information System (INIS)

    Quigley, John; Bedford, Tim; Walls, Lesley

    2007-01-01

    Classical approaches to estimating the rate of occurrence of events perform poorly when data are few. Maximum likelihood estimators result in overly optimistic point estimates of zero for situations where there have been no events. Alternative empirical-based approaches have been proposed based on median estimators or non-informative prior distributions. While these alternatives offer an improvement over point estimates of zero, they can be overly conservative. Empirical Bayes procedures offer an unbiased approach through pooling data across different hazards to support stronger statistical inference. This paper considers the application of Empirical Bayes to high consequence low-frequency events, where estimates are required for risk mitigation decision support such as as low as reasonably possible. A summary of empirical Bayes methods is given and the choices of estimation procedures to obtain interval estimates are discussed. The approaches illustrated within the case study are based on the estimation of the rate of occurrence of train derailments within the UK. The usefulness of empirical Bayes within this context is discussed

  10. Maximum Likelihood Method for Predicting Environmental Conditions from Assemblage Composition: The R Package bio.infer

    Directory of Open Access Journals (Sweden)

    Lester L. Yuan

    2007-06-01

    Full Text Available This paper provides a brief introduction to the R package bio.infer, a set of scripts that facilitates the use of maximum likelihood (ML methods for predicting environmental conditions from assemblage composition. Environmental conditions can often be inferred from only biological data, and these inferences are useful when other sources of data are unavailable. ML prediction methods are statistically rigorous and applicable to a broader set of problems than more commonly used weighted averaging techniques. However, ML methods require a substantially greater investment of time to program algorithms and to perform computations. This package is designed to reduce the effort required to apply ML prediction methods.

  11. Analysis of Minute Features in Speckled Imagery with Maximum Likelihood Estimation

    Directory of Open Access Journals (Sweden)

    Alejandro C. Frery

    2004-12-01

    Full Text Available This paper deals with numerical problems arising when performing maximum likelihood parameter estimation in speckled imagery using small samples. The noise that appears in images obtained with coherent illumination, as is the case of sonar, laser, ultrasound-B, and synthetic aperture radar, is called speckle, and it can neither be assumed Gaussian nor additive. The properties of speckle noise are well described by the multiplicative model, a statistical framework from which stem several important distributions. Amongst these distributions, one is regarded as the universal model for speckled data, namely, the 𝒢0 law. This paper deals with amplitude data, so the 𝒢A0 distribution will be used. The literature reports that techniques for obtaining estimates (maximum likelihood, based on moments and on order statistics of the parameters of the 𝒢A0 distribution require samples of hundreds, even thousands, of observations in order to obtain sensible values. This is verified for maximum likelihood estimation, and a proposal based on alternate optimization is made to alleviate this situation. The proposal is assessed with real and simulated data, showing that the convergence problems are no longer present. A Monte Carlo experiment is devised to estimate the quality of maximum likelihood estimators in small samples, and real data is successfully analyzed with the proposed alternated procedure. Stylized empirical influence functions are computed and used to choose a strategy for computing maximum likelihood estimates that is resistant to outliers.

  12. Assessing Compatibility of Direct Detection Data: Halo-Independent Global Likelihood Analyses

    CERN Document Server

    Gelmini, Graciela B.

    2016-10-18

    We present two different halo-independent methods utilizing a global maximum likelihood that can assess the compatibility of dark matter direct detection data given a particular dark matter model. The global likelihood we use is comprised of at least one extended likelihood and an arbitrary number of Poisson or Gaussian likelihoods. In the first method we find the global best fit halo function and construct a two sided pointwise confidence band, which can then be compared with those derived from the extended likelihood alone to assess the joint compatibility of the data. In the second method we define a "constrained parameter goodness-of-fit" test statistic, whose $p$-value we then use to define a "plausibility region" (e.g. where $p \\geq 10\\%$). For any halo function not entirely contained within the plausibility region, the level of compatibility of the data is very low (e.g. $p < 10 \\%$). As an example we apply these methods to CDMS-II-Si and SuperCDMS data, assuming dark matter particles with elastic s...

  13. A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation.

    Science.gov (United States)

    Meuwly, Didier; Ramos, Daniel; Haraksim, Rudolf

    2017-07-01

    This Guideline proposes a protocol for the validation of forensic evaluation methods at the source level, using the Likelihood Ratio framework as defined within the Bayes' inference model. In the context of the inference of identity of source, the Likelihood Ratio is used to evaluate the strength of the evidence for a trace specimen, e.g. a fingermark, and a reference specimen, e.g. a fingerprint, to originate from common or different sources. Some theoretical aspects of probabilities necessary for this Guideline were discussed prior to its elaboration, which started after a workshop of forensic researchers and practitioners involved in this topic. In the workshop, the following questions were addressed: "which aspects of a forensic evaluation scenario need to be validated?", "what is the role of the LR as part of a decision process?" and "how to deal with uncertainty in the LR calculation?". The questions: "what to validate?" focuses on the validation methods and criteria and "how to validate?" deals with the implementation of the validation protocol. Answers to these questions were deemed necessary with several objectives. First, concepts typical for validation standards [1], such as performance characteristics, performance metrics and validation criteria, will be adapted or applied by analogy to the LR framework. Second, a validation strategy will be defined. Third, validation methods will be described. Finally, a validation protocol and an example of validation report will be proposed, which can be applied to the forensic fields developing and validating LR methods for the evaluation of the strength of evidence at source level under the following propositions. Copyright © 2016. Published by Elsevier B.V.

  14. Maximum likelihood estimation and EM algorithm of Copas-like selection model for publication bias correction.

    Science.gov (United States)

    Ning, Jing; Chen, Yong; Piao, Jin

    2017-07-01

    Publication bias occurs when the published research results are systematically unrepresentative of the population of studies that have been conducted, and is a potential threat to meaningful meta-analysis. The Copas selection model provides a flexible framework for correcting estimates and offers considerable insight into the publication bias. However, maximizing the observed likelihood under the Copas selection model is challenging because the observed data contain very little information on the latent variable. In this article, we study a Copas-like selection model and propose an expectation-maximization (EM) algorithm for estimation based on the full likelihood. Empirical simulation studies show that the EM algorithm and its associated inferential procedure performs well and avoids the non-convergence problem when maximizing the observed likelihood. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  15. Bias correction for estimated QTL effects using the penalized maximum likelihood method.

    Science.gov (United States)

    Zhang, J; Yue, C; Zhang, Y-M

    2012-04-01

    A penalized maximum likelihood method has been proposed as an important approach to the detection of epistatic quantitative trait loci (QTL). However, this approach is not optimal in two special situations: (1) closely linked QTL with effects in opposite directions and (2) small-effect QTL, because the method produces downwardly biased estimates of QTL effects. The present study aims to correct the bias by using correction coefficients and shifting from the use of a uniform prior on the variance parameter of a QTL effect to that of a scaled inverse chi-square prior. The results of Monte Carlo simulation experiments show that the improved method increases the power from 25 to 88% in the detection of two closely linked QTL of equal size in opposite directions and from 60 to 80% in the identification of QTL with small effects (0.5% of the total phenotypic variance). We used the improved method to detect QTL responsible for the barley kernel weight trait using 145 doubled haploid lines developed in the North American Barley Genome Mapping Project. Application of the proposed method to other shrinkage estimation of QTL effects is discussed.

  16. An Empirical Method for Particle Damping Design

    Directory of Open Access Journals (Sweden)

    Zhi Wei Xu

    2004-01-01

    Full Text Available Particle damping is an effective vibration suppression method. The purpose of this paper is to develop an empirical method for particle damping design based on extensive experiments on three structural objects – steel beam, bond arm and bond head stand. The relationships among several key parameters of structure/particles are obtained. Then the procedures with the use of particle damping are proposed to provide guidelines for practical applications. It is believed that the results presented in this paper would be helpful to effectively implement the particle damping for various structural systems for the purpose of vibration suppression.

  17. APPLICATION OF A GENERALIZED MAXIMUM LIKELIHOOD METHOD IN THE REDUCTION OF MULTICOMPONENT LIQUID-LIQUID EQUILIBRIUM DATA

    Directory of Open Access Journals (Sweden)

    L. STRAGEVITCH

    1997-03-01

    Full Text Available The equations of the method based on the maximum likelihood principle have been rewritten in a suitable generalized form to allow the use of any number of implicit constraints in the determination of model parameters from experimental data and from the associated experimental uncertainties. In addition to the use of any number of constraints, this method also allows data, with different numbers of constraints, to be reduced simultaneously. Application of the method is illustrated in the reduction of liquid-liquid equilibrium data of binary, ternary and quaternary systems simultaneously

  18. Pendeteksian Outlier pada Regresi Nonlinier dengan Metode statistik Likelihood Displacement

    Directory of Open Access Journals (Sweden)

    Siti Tabi'atul Hasanah

    2012-11-01

    Full Text Available Outlier is an observation that much different (extreme from the other observational data, or data can be interpreted that do not follow the general pattern of the model. Sometimes outliers provide information that can not be provided by other data. That's why outliers should not just be eliminated. Outliers can also be an influential observation. There are many methods that can be used to detect of outliers. In previous studies done on outlier detection of linear regression. Next will be developed detection of outliers in nonlinear regression. Nonlinear regression here is devoted to multiplicative nonlinear regression. To detect is use of statistical method likelihood displacement. Statistical methods abbreviated likelihood displacement (LD is a method to detect outliers by removing the suspected outlier data. To estimate the parameters are used to the maximum likelihood method, so we get the estimate of the maximum. By using LD method is obtained i.e likelihood displacement is thought to contain outliers. Further accuracy of LD method in detecting the outliers are shown by comparing the MSE of LD with the MSE from the regression in general. Statistic test used is Λ. Initial hypothesis was rejected when proved so is an outlier.

  19. Modeling gene expression measurement error: a quasi-likelihood approach

    Directory of Open Access Journals (Sweden)

    Strimmer Korbinian

    2003-03-01

    Full Text Available Abstract Background Using suitable error models for gene expression measurements is essential in the statistical analysis of microarray data. However, the true probabilistic model underlying gene expression intensity readings is generally not known. Instead, in currently used approaches some simple parametric model is assumed (usually a transformed normal distribution or the empirical distribution is estimated. However, both these strategies may not be optimal for gene expression data, as the non-parametric approach ignores known structural information whereas the fully parametric models run the risk of misspecification. A further related problem is the choice of a suitable scale for the model (e.g. observed vs. log-scale. Results Here a simple semi-parametric model for gene expression measurement error is presented. In this approach inference is based an approximate likelihood function (the extended quasi-likelihood. Only partial knowledge about the unknown true distribution is required to construct this function. In case of gene expression this information is available in the form of the postulated (e.g. quadratic variance structure of the data. As the quasi-likelihood behaves (almost like a proper likelihood, it allows for the estimation of calibration and variance parameters, and it is also straightforward to obtain corresponding approximate confidence intervals. Unlike most other frameworks, it also allows analysis on any preferred scale, i.e. both on the original linear scale as well as on a transformed scale. It can also be employed in regression approaches to model systematic (e.g. array or dye effects. Conclusions The quasi-likelihood framework provides a simple and versatile approach to analyze gene expression data that does not make any strong distributional assumptions about the underlying error model. For several simulated as well as real data sets it provides a better fit to the data than competing models. In an example it also

  20. Zero-inflated Poisson model based likelihood ratio test for drug safety signal detection.

    Science.gov (United States)

    Huang, Lan; Zheng, Dan; Zalkikar, Jyoti; Tiwari, Ram

    2017-02-01

    In recent decades, numerous methods have been developed for data mining of large drug safety databases, such as Food and Drug Administration's (FDA's) Adverse Event Reporting System, where data matrices are formed by drugs such as columns and adverse events as rows. Often, a large number of cells in these data matrices have zero cell counts and some of them are "true zeros" indicating that the drug-adverse event pairs cannot occur, and these zero counts are distinguished from the other zero counts that are modeled zero counts and simply indicate that the drug-adverse event pairs have not occurred yet or have not been reported yet. In this paper, a zero-inflated Poisson model based likelihood ratio test method is proposed to identify drug-adverse event pairs that have disproportionately high reporting rates, which are also called signals. The maximum likelihood estimates of the model parameters of zero-inflated Poisson model based likelihood ratio test are obtained using the expectation and maximization algorithm. The zero-inflated Poisson model based likelihood ratio test is also modified to handle the stratified analyses for binary and categorical covariates (e.g. gender and age) in the data. The proposed zero-inflated Poisson model based likelihood ratio test method is shown to asymptotically control the type I error and false discovery rate, and its finite sample performance for signal detection is evaluated through a simulation study. The simulation results show that the zero-inflated Poisson model based likelihood ratio test method performs similar to Poisson model based likelihood ratio test method when the estimated percentage of true zeros in the database is small. Both the zero-inflated Poisson model based likelihood ratio test and likelihood ratio test methods are applied to six selected drugs, from the 2006 to 2011 Adverse Event Reporting System database, with varying percentages of observed zero-count cells.

  1. An iterative procedure for obtaining maximum-likelihood estimates of the parameters for a mixture of normal distributions

    Science.gov (United States)

    Peters, B. C., Jr.; Walker, H. F.

    1978-01-01

    This paper addresses the problem of obtaining numerically maximum-likelihood estimates of the parameters for a mixture of normal distributions. In recent literature, a certain successive-approximations procedure, based on the likelihood equations, was shown empirically to be effective in numerically approximating such maximum-likelihood estimates; however, the reliability of this procedure was not established theoretically. Here, we introduce a general iterative procedure, of the generalized steepest-ascent (deflected-gradient) type, which is just the procedure known in the literature when the step-size is taken to be 1. We show that, with probability 1 as the sample size grows large, this procedure converges locally to the strongly consistent maximum-likelihood estimate whenever the step-size lies between 0 and 2. We also show that the step-size which yields optimal local convergence rates for large samples is determined in a sense by the 'separation' of the component normal densities and is bounded below by a number between 1 and 2.

  2. Approximate maximum likelihood estimation for population genetic inference.

    Science.gov (United States)

    Bertl, Johanna; Ewing, Gregory; Kosiol, Carolin; Futschik, Andreas

    2017-11-27

    In many population genetic problems, parameter estimation is obstructed by an intractable likelihood function. Therefore, approximate estimation methods have been developed, and with growing computational power, sampling-based methods became popular. However, these methods such as Approximate Bayesian Computation (ABC) can be inefficient in high-dimensional problems. This led to the development of more sophisticated iterative estimation methods like particle filters. Here, we propose an alternative approach that is based on stochastic approximation. By moving along a simulated gradient or ascent direction, the algorithm produces a sequence of estimates that eventually converges to the maximum likelihood estimate, given a set of observed summary statistics. This strategy does not sample much from low-likelihood regions of the parameter space, and is fast, even when many summary statistics are involved. We put considerable efforts into providing tuning guidelines that improve the robustness and lead to good performance on problems with high-dimensional summary statistics and a low signal-to-noise ratio. We then investigate the performance of our resulting approach and study its properties in simulations. Finally, we re-estimate parameters describing the demographic history of Bornean and Sumatran orang-utans.

  3. An empirical method to estimate bulk particulate refractive index for ocean satellite applications

    Digital Repository Service at National Institute of Oceanography (India)

    Suresh, T.; Desa, E.; Mascarenhas, A.A.M.Q.; Matondkar, S.G.P.; Naik, P.; Nayak, S.R.

    An empirical method is presented here to estimates bulk particulate refractive index using the measured inherent and apparent optical properties from the various waters types of the Arabian Sea. The empirical model, where the bulk refractive index...

  4. Nonuniform Illumination Correction Algorithm for Underwater Images Using Maximum Likelihood Estimation Method

    Directory of Open Access Journals (Sweden)

    Sonali Sachin Sankpal

    2016-01-01

    Full Text Available Scattering and absorption of light is main reason for limited visibility in water. The suspended particles and dissolved chemical compounds in water are also responsible for scattering and absorption of light in water. The limited visibility in water results in degradation of underwater images. The visibility can be increased by using artificial light source in underwater imaging system. But the artificial light illuminates the scene in a nonuniform fashion. It produces bright spot at the center with the dark region at surroundings. In some cases imaging system itself creates dark region in the image by producing shadow on the objects. The problem of nonuniform illumination is neglected by the researchers in most of the image enhancement techniques of underwater images. Also very few methods are discussed showing the results on color images. This paper suggests a method for nonuniform illumination correction for underwater images. The method assumes that natural underwater images are Rayleigh distributed. This paper used maximum likelihood estimation of scale parameter to map distribution of image to Rayleigh distribution. The method is compared with traditional methods for nonuniform illumination correction using no-reference image quality metrics like average luminance, average information entropy, normalized neighborhood function, average contrast, and comprehensive assessment function.

  5. THE GENERALIZED MAXIMUM LIKELIHOOD METHOD APPLIED TO HIGH PRESSURE PHASE EQUILIBRIUM

    Directory of Open Access Journals (Sweden)

    Lúcio CARDOZO-FILHO

    1997-12-01

    Full Text Available The generalized maximum likelihood method was used to determine binary interaction parameters between carbon dioxide and components of orange essential oil. Vapor-liquid equilibrium was modeled with Peng-Robinson and Soave-Redlich-Kwong equations, using a methodology proposed in 1979 by Asselineau, Bogdanic and Vidal. Experimental vapor-liquid equilibrium data on binary mixtures formed with carbon dioxide and compounds usually found in orange essential oil were used to test the model. These systems were chosen to demonstrate that the maximum likelihood method produces binary interaction parameters for cubic equations of state capable of satisfactorily describing phase equilibrium, even for a binary such as ethanol/CO2. Results corroborate that the Peng-Robinson, as well as the Soave-Redlich-Kwong, equation can be used to describe phase equilibrium for the following systems: components of essential oil of orange/CO2.Foi empregado o método da máxima verossimilhança generalizado para determinação de parâmetros de interação binária entre os componentes do óleo essencial de laranja e dióxido de carbono. Foram usados dados experimentais de equilíbrio líquido-vapor de misturas binárias de dióxido de carbono e componentes do óleo essencial de laranja. O equilíbrio líquido-vapor foi modelado com as equações de Peng-Robinson e de Soave-Redlich-Kwong usando a metodologia proposta em 1979 por Asselineau, Bogdanic e Vidal. A escolha destes sistemas teve como objetivo demonstrar que o método da máxima verosimilhança produz parâmetros de interação binária, para equações cúbicas de estado capazes de descrever satisfatoriamente até mesmo o equilíbrio para o binário etanol/CO2. Os resultados comprovam que tanto a equação de Peng-Robinson quanto a de Soave-Redlich-Kwong podem ser empregadas para descrever o equilíbrio de fases para o sistemas: componentes do óleo essencial de laranja/CO2.

  6. Statistical modelling of survival data with random effects h-likelihood approach

    CERN Document Server

    Ha, Il Do; Lee, Youngjo

    2017-01-01

    This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions. The approach presented in the book overcomes shortcomings in the traditional likelihood-based methods for clustered survival data such as intractable integration. The text includes technical materials such as derivations and proofs in each chapter, as well as recently developed software programs in R (“frailtyHL”), while the real-world data examples together with an R package, “frailtyHL” in CRAN, provide readers with useful hands-on tools. Reviewing new developments since the introduction of the h-likelihood to survival analysis (methods for interval estimation of the individual frailty and for variable selection of the fixed effects in the general class of frailty models) and guiding future directions, the book is of interest to research...

  7. Maximum Simulated Likelihood and Expectation-Maximization Methods to Estimate Random Coefficients Logit with Panel Data

    DEFF Research Database (Denmark)

    Cherchi, Elisabetta; Guevara, Cristian

    2012-01-01

    with cross-sectional or with panel data, and (d) EM systematically attained more efficient estimators than the MSL method. The results imply that if the purpose of the estimation is only to determine the ratios of the model parameters (e.g., the value of time), the EM method should be preferred. For all......The random coefficients logit model allows a more realistic representation of agents' behavior. However, the estimation of that model may involve simulation, which may become impractical with many random coefficients because of the curse of dimensionality. In this paper, the traditional maximum...... simulated likelihood (MSL) method is compared with the alternative expectation- maximization (EM) method, which does not require simulation. Previous literature had shown that for cross-sectional data, MSL outperforms the EM method in the ability to recover the true parameters and estimation time...

  8. Evolutionary analysis of apolipoprotein E by Maximum Likelihood and complex network methods

    Directory of Open Access Journals (Sweden)

    Leandro de Jesus Benevides

    Full Text Available Abstract Apolipoprotein E (apo E is a human glycoprotein with 299 amino acids, and it is a major component of very low density lipoproteins (VLDL and a group of high-density lipoproteins (HDL. Phylogenetic studies are important to clarify how various apo E proteins are related in groups of organisms and whether they evolved from a common ancestor. Here, we aimed at performing a phylogenetic study on apo E carrying organisms. We employed a classical and robust method, such as Maximum Likelihood (ML, and compared the results using a more recent approach based on complex networks. Thirty-two apo E amino acid sequences were downloaded from NCBI. A clear separation could be observed among three major groups: mammals, fish and amphibians. The results obtained from ML method, as well as from the constructed networks showed two different groups: one with mammals only (C1 and another with fish (C2, and a single node with the single sequence available for an amphibian. The accordance in results from the different methods shows that the complex networks approach is effective in phylogenetic studies. Furthermore, our results revealed the conservation of apo E among animal groups.

  9. An iterative procedure for obtaining maximum-likelihood estimates of the parameters for a mixture of normal distributions, 2

    Science.gov (United States)

    Peters, B. C., Jr.; Walker, H. F.

    1976-01-01

    The problem of obtaining numerically maximum likelihood estimates of the parameters for a mixture of normal distributions is addressed. In recent literature, a certain successive approximations procedure, based on the likelihood equations, is shown empirically to be effective in numerically approximating such maximum-likelihood estimates; however, the reliability of this procedure was not established theoretically. Here, a general iterative procedure is introduced, of the generalized steepest-ascent (deflected-gradient) type, which is just the procedure known in the literature when the step-size is taken to be 1. With probability 1 as the sample size grows large, it is shown that this procedure converges locally to the strongly consistent maximum-likelihood estimate whenever the step-size lies between 0 and 2. The step-size which yields optimal local convergence rates for large samples is determined in a sense by the separation of the component normal densities and is bounded below by a number between 1 and 2.

  10. Towards Multi-Method Research Approach in Empirical Software Engineering

    Science.gov (United States)

    Mandić, Vladimir; Markkula, Jouni; Oivo, Markku

    This paper presents results of a literature analysis on Empirical Research Approaches in Software Engineering (SE). The analysis explores reasons why traditional methods, such as statistical hypothesis testing and experiment replication are weakly utilized in the field of SE. It appears that basic assumptions and preconditions of the traditional methods are contradicting the actual situation in the SE. Furthermore, we have identified main issues that should be considered by the researcher when selecting the research approach. In virtue of reasons for weak utilization of traditional methods we propose stronger use of Multi-Method approach with Pragmatism as the philosophical standpoint.

  11. Climate Prediction for Brazil's Nordeste: Performance of Empirical and Numerical Modeling Methods.

    Science.gov (United States)

    Moura, Antonio Divino; Hastenrath, Stefan

    2004-07-01

    Comparisons of performance of climate forecast methods require consistency in the predictand and a long common reference period. For Brazil's Nordeste, empirical methods developed at the University of Wisconsin use preseason (October January) rainfall and January indices of the fields of meridional wind component and sea surface temperature (SST) in the tropical Atlantic and the equatorial Pacific as input to stepwise multiple regression and neural networking. These are used to predict the March June rainfall at a network of 27 stations. An experiment at the International Research Institute for Climate Prediction, Columbia University, with a numerical model (ECHAM4.5) used global SST information through February to predict the March June rainfall at three grid points in the Nordeste. The predictands for the empirical and numerical model forecasts are correlated at +0.96, and the period common to the independent portion of record of the empirical prediction and the numerical modeling is 1968 99. Over this period, predicted versus observed rainfall are evaluated in terms of correlation, root-mean-square error, absolute error, and bias. Performance is high for both approaches. Numerical modeling produces a correlation of +0.68, moderate errors, and strong negative bias. For the empirical methods, errors and bias are small, and correlations of +0.73 and +0.82 are reached between predicted and observed rainfall.

  12. Bias correction in the hierarchical likelihood approach to the analysis of multivariate survival data.

    Science.gov (United States)

    Jeon, Jihyoun; Hsu, Li; Gorfine, Malka

    2012-07-01

    Frailty models are useful for measuring unobserved heterogeneity in risk of failures across clusters, providing cluster-specific risk prediction. In a frailty model, the latent frailties shared by members within a cluster are assumed to act multiplicatively on the hazard function. In order to obtain parameter and frailty variate estimates, we consider the hierarchical likelihood (H-likelihood) approach (Ha, Lee and Song, 2001. Hierarchical-likelihood approach for frailty models. Biometrika 88, 233-243) in which the latent frailties are treated as "parameters" and estimated jointly with other parameters of interest. We find that the H-likelihood estimators perform well when the censoring rate is low, however, they are substantially biased when the censoring rate is moderate to high. In this paper, we propose a simple and easy-to-implement bias correction method for the H-likelihood estimators under a shared frailty model. We also extend the method to a multivariate frailty model, which incorporates complex dependence structure within clusters. We conduct an extensive simulation study and show that the proposed approach performs very well for censoring rates as high as 80%. We also illustrate the method with a breast cancer data set. Since the H-likelihood is the same as the penalized likelihood function, the proposed bias correction method is also applicable to the penalized likelihood estimators.

  13. Algorithms of maximum likelihood data clustering with applications

    Science.gov (United States)

    Giada, Lorenzo; Marsili, Matteo

    2002-12-01

    We address the problem of data clustering by introducing an unsupervised, parameter-free approach based on maximum likelihood principle. Starting from the observation that data sets belonging to the same cluster share a common information, we construct an expression for the likelihood of any possible cluster structure. The likelihood in turn depends only on the Pearson's coefficient of the data. We discuss clustering algorithms that provide a fast and reliable approximation to maximum likelihood configurations. Compared to standard clustering methods, our approach has the advantages that (i) it is parameter free, (ii) the number of clusters need not be fixed in advance and (iii) the interpretation of the results is transparent. In order to test our approach and compare it with standard clustering algorithms, we analyze two very different data sets: time series of financial market returns and gene expression data. We find that different maximization algorithms produce similar cluster structures whereas the outcome of standard algorithms has a much wider variability.

  14. LDR: A Package for Likelihood-Based Sufficient Dimension Reduction

    Directory of Open Access Journals (Sweden)

    R. Dennis Cook

    2011-03-01

    Full Text Available We introduce a new mlab software package that implements several recently proposed likelihood-based methods for sufficient dimension reduction. Current capabilities include estimation of reduced subspaces with a fixed dimension d, as well as estimation of d by use of likelihood-ratio testing, permutation testing and information criteria. The methods are suitable for preprocessing data for both regression and classification. Implementations of related estimators are also available. Although the software is more oriented to command-line operation, a graphical user interface is also provided for prototype computations.

  15. An empirical method for dynamic camouflage assessment

    Science.gov (United States)

    Blitch, John G.

    2011-06-01

    As camouflage systems become increasingly sophisticated in their potential to conceal military personnel and precious cargo, evaluation methods need to evolve as well. This paper presents an overview of one such attempt to explore alternative methods for empirical evaluation of dynamic camouflage systems which aspire to keep pace with a soldier's movement through rapidly changing environments that are typical of urban terrain. Motivating factors are covered first, followed by a description of the Blitz Camouflage Assessment (BCA) process and results from an initial proof of concept experiment conducted in November 2006. The conclusion drawn from these results, related literature and the author's personal experience suggest that operational evaluation of personal camouflage needs to be expanded beyond its foundation in signal detection theory and embrace the challenges posed by high levels of cognitive processing.

  16. Logic of likelihood

    International Nuclear Information System (INIS)

    Wall, M.J.W.

    1992-01-01

    The notion of open-quotes probabilityclose quotes is generalized to that of open-quotes likelihood,close quotes and a natural logical structure is shown to exist for any physical theory which predicts likelihoods. Two physically based axioms are given for this logical structure to form an orthomodular poset, with an order-determining set of states. The results strengthen the basis of the quantum logic approach to axiomatic quantum theory. 25 refs

  17. Further Evaluation of Covariate Analysis using Empirical Bayes Estimates in Population Pharmacokinetics: the Perception of Shrinkage and Likelihood Ratio Test.

    Science.gov (United States)

    Xu, Xu Steven; Yuan, Min; Yang, Haitao; Feng, Yan; Xu, Jinfeng; Pinheiro, Jose

    2017-01-01

    Covariate analysis based on population pharmacokinetics (PPK) is used to identify clinically relevant factors. The likelihood ratio test (LRT) based on nonlinear mixed effect model fits is currently recommended for covariate identification, whereas individual empirical Bayesian estimates (EBEs) are considered unreliable due to the presence of shrinkage. The objectives of this research were to investigate the type I error for LRT and EBE approaches, to confirm the similarity of power between the LRT and EBE approaches from a previous report and to explore the influence of shrinkage on LRT and EBE inferences. Using an oral one-compartment PK model with a single covariate impacting on clearance, we conducted a wide range of simulations according to a two-way factorial design. The results revealed that the EBE-based regression not only provided almost identical power for detecting a covariate effect, but also controlled the false positive rate better than the LRT approach. Shrinkage of EBEs is likely not the root cause for decrease in power or inflated false positive rate although the size of the covariate effect tends to be underestimated at high shrinkage. In summary, contrary to the current recommendations, EBEs may be a better choice for statistical tests in PPK covariate analysis compared to LRT. We proposed a three-step covariate modeling approach for population PK analysis to utilize the advantages of EBEs while overcoming their shortcomings, which allows not only markedly reducing the run time for population PK analysis, but also providing more accurate covariate tests.

  18. ldr: An R Software Package for Likelihood-Based Su?cient Dimension Reduction

    Directory of Open Access Journals (Sweden)

    Kofi Placid Adragni

    2014-11-01

    Full Text Available In regression settings, a su?cient dimension reduction (SDR method seeks the core information in a p-vector predictor that completely captures its relationship with a response. The reduced predictor may reside in a lower dimension d < p, improving ability to visualize data and predict future observations, and mitigating dimensionality issues when carrying out further analysis. We introduce ldr, a new R software package that implements three recently proposed likelihood-based methods for SDR: covariance reduction, likelihood acquired directions, and principal fitted components. All three methods reduce the dimensionality of the data by pro jection into lower dimensional subspaces. The package also implements a variable screening method built upon principal ?tted components which makes use of ?exible basis functions to capture the dependencies between the predictors and the response. Examples are given to demonstrate likelihood-based SDR analyses using ldr, including estimation of the dimension of reduction subspaces and selection of basis functions. The ldr package provides a framework that we hope to grow into a comprehensive library of likelihood-based SDR methodologies.

  19. Maximum Likelihood Estimation and Inference With Examples in R, SAS and ADMB

    CERN Document Server

    Millar, Russell B

    2011-01-01

    This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statis

  20. Searching for degenerate Higgs bosons a profile likelihood ratio method to test for mass-degenerate states in the presence of censored data and uncertainties

    CERN Document Server

    David, André; Petrucciani, Giovanni

    2015-01-01

    Using the likelihood ratio test statistic, we present a method which can be employed to test the hypothesis of a single Higgs boson using the matrix of measured signal strengths. This method can be applied in the presence of censored data and takes into account uncertainties on the measurements. The p-value against the hypothesis of a single Higgs boson is defined from the expected distribution of the test statistic, generated using pseudo-experiments. The applicability of the likelihood-based test is demonstrated using numerical examples with uncertainties and missing matrix elements.

  1. Efficient method for computing the maximum-likelihood quantum state from measurements with additive Gaussian noise.

    Science.gov (United States)

    Smolin, John A; Gambetta, Jay M; Smith, Graeme

    2012-02-17

    We provide an efficient method for computing the maximum-likelihood mixed quantum state (with density matrix ρ) given a set of measurement outcomes in a complete orthonormal operator basis subject to Gaussian noise. Our method works by first changing basis yielding a candidate density matrix μ which may have nonphysical (negative) eigenvalues, and then finding the nearest physical state under the 2-norm. Our algorithm takes at worst O(d(4)) for the basis change plus O(d(3)) for finding ρ where d is the dimension of the quantum state. In the special case where the measurement basis is strings of Pauli operators, the basis change takes only O(d(3)) as well. The workhorse of the algorithm is a new linear-time method for finding the closest probability distribution (in Euclidean distance) to a set of real numbers summing to one.

  2. Factors Associated with Young Adults’ Pregnancy Likelihood

    Science.gov (United States)

    Kitsantas, Panagiota; Lindley, Lisa L.; Wu, Huichuan

    2014-01-01

    OBJECTIVES While progress has been made to reduce adolescent pregnancies in the United States, rates of unplanned pregnancy among young adults (18–29 years) remain high. In this study, we assessed factors associated with perceived likelihood of pregnancy (likelihood of getting pregnant/getting partner pregnant in the next year) among sexually experienced young adults who were not trying to get pregnant and had ever used contraceptives. METHODS We conducted a secondary analysis of 660 young adults, 18–29 years old in the United States, from the cross-sectional National Survey of Reproductive and Contraceptive Knowledge. Logistic regression and classification tree analyses were conducted to generate profiles of young adults most likely to report anticipating a pregnancy in the next year. RESULTS Nearly one-third (32%) of young adults indicated they believed they had at least some likelihood of becoming pregnant in the next year. Young adults who believed that avoiding pregnancy was not very important were most likely to report pregnancy likelihood (odds ratio [OR], 5.21; 95% CI, 2.80–9.69), as were young adults for whom avoiding a pregnancy was important but not satisfied with their current contraceptive method (OR, 3.93; 95% CI, 1.67–9.24), attended religious services frequently (OR, 3.0; 95% CI, 1.52–5.94), were uninsured (OR, 2.63; 95% CI, 1.31–5.26), and were likely to have unprotected sex in the next three months (OR, 1.77; 95% CI, 1.04–3.01). DISCUSSION These results may help guide future research and the development of pregnancy prevention interventions targeting sexually experienced young adults. PMID:25782849

  3. Assessing compatibility of direct detection data: halo-independent global likelihood analyses

    Energy Technology Data Exchange (ETDEWEB)

    Gelmini, Graciela B. [Department of Physics and Astronomy, UCLA,475 Portola Plaza, Los Angeles, CA 90095 (United States); Huh, Ji-Haeng [CERN Theory Division,CH-1211, Geneva 23 (Switzerland); Witte, Samuel J. [Department of Physics and Astronomy, UCLA,475 Portola Plaza, Los Angeles, CA 90095 (United States)

    2016-10-18

    We present two different halo-independent methods to assess the compatibility of several direct dark matter detection data sets for a given dark matter model using a global likelihood consisting of at least one extended likelihood and an arbitrary number of Gaussian or Poisson likelihoods. In the first method we find the global best fit halo function (we prove that it is a unique piecewise constant function with a number of down steps smaller than or equal to a maximum number that we compute) and construct a two-sided pointwise confidence band at any desired confidence level, which can then be compared with those derived from the extended likelihood alone to assess the joint compatibility of the data. In the second method we define a “constrained parameter goodness-of-fit” test statistic, whose p-value we then use to define a “plausibility region” (e.g. where p≥10%). For any halo function not entirely contained within the plausibility region, the level of compatibility of the data is very low (e.g. p<10%). We illustrate these methods by applying them to CDMS-II-Si and SuperCDMS data, assuming dark matter particles with elastic spin-independent isospin-conserving interactions or exothermic spin-independent isospin-violating interactions.

  4. Superfast maximum-likelihood reconstruction for quantum tomography

    Science.gov (United States)

    Shang, Jiangwei; Zhang, Zhengyun; Ng, Hui Khoon

    2017-06-01

    Conventional methods for computing maximum-likelihood estimators (MLE) often converge slowly in practical situations, leading to a search for simplifying methods that rely on additional assumptions for their validity. In this work, we provide a fast and reliable algorithm for maximum-likelihood reconstruction that avoids this slow convergence. Our method utilizes the state-of-the-art convex optimization scheme, an accelerated projected-gradient method, that allows one to accommodate the quantum nature of the problem in a different way than in the standard methods. We demonstrate the power of our approach by comparing its performance with other algorithms for n -qubit state tomography. In particular, an eight-qubit situation that purportedly took weeks of computation time in 2005 can now be completed in under a minute for a single set of data, with far higher accuracy than previously possible. This refutes the common claim that MLE reconstruction is slow and reduces the need for alternative methods that often come with difficult-to-verify assumptions. In fact, recent methods assuming Gaussian statistics or relying on compressed sensing ideas are demonstrably inapplicable for the situation under consideration here. Our algorithm can be applied to general optimization problems over the quantum state space; the philosophy of projected gradients can further be utilized for optimization contexts with general constraints.

  5. Earthquake likelihood model testing

    Science.gov (United States)

    Schorlemmer, D.; Gerstenberger, M.C.; Wiemer, S.; Jackson, D.D.; Rhoades, D.A.

    2007-01-01

    INTRODUCTIONThe Regional Earthquake Likelihood Models (RELM) project aims to produce and evaluate alternate models of earthquake potential (probability per unit volume, magnitude, and time) for California. Based on differing assumptions, these models are produced to test the validity of their assumptions and to explore which models should be incorporated in seismic hazard and risk evaluation. Tests based on physical and geological criteria are useful but we focus on statistical methods using future earthquake catalog data only. We envision two evaluations: a test of consistency with observed data and a comparison of all pairs of models for relative consistency. Both tests are based on the likelihood method, and both are fully prospective (i.e., the models are not adjusted to fit the test data). To be tested, each model must assign a probability to any possible event within a specified region of space, time, and magnitude. For our tests the models must use a common format: earthquake rates in specified “bins” with location, magnitude, time, and focal mechanism limits.Seismology cannot yet deterministically predict individual earthquakes; however, it should seek the best possible models for forecasting earthquake occurrence. This paper describes the statistical rules of an experiment to examine and test earthquake forecasts. The primary purposes of the tests described below are to evaluate physical models for earthquakes, assure that source models used in seismic hazard and risk studies are consistent with earthquake data, and provide quantitative measures by which models can be assigned weights in a consensus model or be judged as suitable for particular regions.In this paper we develop a statistical method for testing earthquake likelihood models. A companion paper (Schorlemmer and Gerstenberger 2007, this issue) discusses the actual implementation of these tests in the framework of the RELM initiative.Statistical testing of hypotheses is a common task and a

  6. Empirical Evidence or Intuition? An Activity Involving the Scientific Method

    Science.gov (United States)

    Overway, Ken

    2007-01-01

    Students need to have basic understanding of scientific method during their introductory science classes and for this purpose an activity was devised which involved a game based on famous Monty Hall game problem. This particular activity allowed students to banish or confirm their intuition based on empirical evidence.

  7. Comparison of artificial intelligence methods and empirical equations to estimate daily solar radiation

    Science.gov (United States)

    Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

    2016-08-01

    In the present research, three artificial intelligence methods including Gene Expression Programming (GEP), Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) as well as, 48 empirical equations (10, 12 and 26 equations were temperature-based, sunshine-based and meteorological parameters-based, respectively) were used to estimate daily solar radiation in Kerman, Iran in the period of 1992-2009. To develop the GEP, ANN and ANFIS models, depending on the used empirical equations, various combinations of minimum air temperature, maximum air temperature, mean air temperature, extraterrestrial radiation, actual sunshine duration, maximum possible sunshine duration, sunshine duration ratio, relative humidity and precipitation were considered as inputs in the mentioned intelligent methods. To compare the accuracy of empirical equations and intelligent models, root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE) and determination coefficient (R2) indices were used. The results showed that in general, sunshine-based and meteorological parameters-based scenarios in ANN and ANFIS models presented high accuracy than mentioned empirical equations. Moreover, the most accurate method in the studied region was ANN11 scenario with five inputs. The values of RMSE, MAE, MARE and R2 indices for the mentioned model were 1.850 MJ m-2 day-1, 1.184 MJ m-2 day-1, 9.58% and 0.935, respectively.

  8. Multi-Channel Maximum Likelihood Pitch Estimation

    DEFF Research Database (Denmark)

    Christensen, Mads Græsbøll

    2012-01-01

    In this paper, a method for multi-channel pitch estimation is proposed. The method is a maximum likelihood estimator and is based on a parametric model where the signals in the various channels share the same fundamental frequency but can have different amplitudes, phases, and noise characteristics....... This essentially means that the model allows for different conditions in the various channels, like different signal-to-noise ratios, microphone characteristics and reverberation. Moreover, the method does not assume that a certain array structure is used but rather relies on a more general model and is hence...

  9. A maximum pseudo-likelihood approach for estimating species trees under the coalescent model

    Directory of Open Access Journals (Sweden)

    Edwards Scott V

    2010-10-01

    Full Text Available Abstract Background Several phylogenetic approaches have been developed to estimate species trees from collections of gene trees. However, maximum likelihood approaches for estimating species trees under the coalescent model are limited. Although the likelihood of a species tree under the multispecies coalescent model has already been derived by Rannala and Yang, it can be shown that the maximum likelihood estimate (MLE of the species tree (topology, branch lengths, and population sizes from gene trees under this formula does not exist. In this paper, we develop a pseudo-likelihood function of the species tree to obtain maximum pseudo-likelihood estimates (MPE of species trees, with branch lengths of the species tree in coalescent units. Results We show that the MPE of the species tree is statistically consistent as the number M of genes goes to infinity. In addition, the probability that the MPE of the species tree matches the true species tree converges to 1 at rate O(M -1. The simulation results confirm that the maximum pseudo-likelihood approach is statistically consistent even when the species tree is in the anomaly zone. We applied our method, Maximum Pseudo-likelihood for Estimating Species Trees (MP-EST to a mammal dataset. The four major clades found in the MP-EST tree are consistent with those in the Bayesian concatenation tree. The bootstrap supports for the species tree estimated by the MP-EST method are more reasonable than the posterior probability supports given by the Bayesian concatenation method in reflecting the level of uncertainty in gene trees and controversies over the relationship of four major groups of placental mammals. Conclusions MP-EST can consistently estimate the topology and branch lengths (in coalescent units of the species tree. Although the pseudo-likelihood is derived from coalescent theory, and assumes no gene flow or horizontal gene transfer (HGT, the MP-EST method is robust to a small amount of HGT in the

  10. Calibrating a combined energy systems analysis and controller design method with empirical data

    International Nuclear Information System (INIS)

    Murphy, Gavin Bruce; Counsell, John; Allison, John; Brindley, Joseph

    2013-01-01

    The drive towards low carbon constructions has seen buildings increasingly utilise many different energy systems simultaneously to control the human comfort of the indoor environment; such as ventilation with heat recovery, various heating solutions and applications of renewable energy. This paper describes a dynamic modelling and simulation method (IDEAS – Inverse Dynamics based Energy Assessment and Simulation) for analysing the energy utilisation of a building and its complex servicing systems. The IDEAS case study presented in this paper is based upon small perturbation theory and can be used for the analysis of the performance of complex energy systems and also for the design of smart control systems. This paper presents a process of how any dynamic model can be calibrated against a more empirical based data model, in this case the UK Government's SAP (Standard Assessment Procedure). The research targets of this work are building simulation experts for analysing the energy use of a building and also control engineers to assist in the design of smart control systems for dwellings. The calibration process presented is transferable and has applications for simulation experts to assist in calibrating any dynamic building simulation method with an empirical based method. - Highlights: • Presentation of an energy systems analysis method for assessing the energy utilisation of buildings and their complex servicing systems. • An inverse dynamics based controller design method is detailed. • Method of how a dynamic model can be calibrated with an empirical based model

  11. Maximum likelihood estimation of the parameters of nonminimum phase and noncausal ARMA models

    DEFF Research Database (Denmark)

    Rasmussen, Klaus Bolding

    1994-01-01

    The well-known prediction-error-based maximum likelihood (PEML) method can only handle minimum phase ARMA models. This paper presents a new method known as the back-filtering-based maximum likelihood (BFML) method, which can handle nonminimum phase and noncausal ARMA models. The BFML method...... is identical to the PEML method in the case of a minimum phase ARMA model, and it turns out that the BFML method incorporates a noncausal ARMA filter with poles outside the unit circle for estimation of the parameters of a causal, nonminimum phase ARMA model...

  12. Evaluation of registration methods on thoracic CT : the EMPIRE10 challenge

    NARCIS (Netherlands)

    Murphy, K.; Ginneken, van B.; Reinhardt, J.M.; Kabus, S.; Ding, K.; Deng, Xiang; Cao, K.; Du, K.; Christensen, G.E.; Garcia, V.; Vercauteren, T.; Ayache, N.; Commowick, O.; Malandain, G.; Glocker, B.; Paragios, N.; Navab, N.; Gorbunova, V.; Sporring, J.; Bruijne, de M.; Han, Xiao; Heinrich, M.P.; Schnabel, J.A.; Jenkinson, M.; Lorenz, C.; Modat, M.; McClelland, J.R.; Ourselin, S.; Muenzing, S.E.A.; Viergever, M.A.; Nigris, De D.; Collins, D.L.; Arbel, T.; Peroni, M.; Li, R.; Sharp, G.; Schmidt-Richberg, A.; Ehrhardt, J.; Werner, R.; Smeets, D.; Loeckx, D.; Song, G.; Tustison, N.; Avants, B.; Gee, J.C.; Staring, M.; Klein, S.; Stoel, B.C.; Urschler, M.; Werlberger, M.; Vandemeulebroucke, J.; Rit, S.; Sarrut, D.; Pluim, J.P.W.

    2011-01-01

    EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intrapatient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This

  13. A note on estimating errors from the likelihood function

    International Nuclear Information System (INIS)

    Barlow, Roger

    2005-01-01

    The points at which the log likelihood falls by 12 from its maximum value are often used to give the 'errors' on a result, i.e. the 68% central confidence interval. The validity of this is examined for two simple cases: a lifetime measurement and a Poisson measurement. Results are compared with the exact Neyman construction and with the simple Bartlett approximation. It is shown that the accuracy of the log likelihood method is poor, and the Bartlett construction explains why it is flawed

  14. Likelihood functions for the analysis of single-molecule binned photon sequences

    Energy Technology Data Exchange (ETDEWEB)

    Gopich, Irina V., E-mail: irinag@niddk.nih.gov [Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892 (United States)

    2012-03-02

    Graphical abstract: Folding of a protein with attached fluorescent dyes, the underlying conformational trajectory of interest, and the observed binned photon trajectory. Highlights: Black-Right-Pointing-Pointer A sequence of photon counts can be analyzed using a likelihood function. Black-Right-Pointing-Pointer The exact likelihood function for a two-state kinetic model is provided. Black-Right-Pointing-Pointer Several approximations are considered for an arbitrary kinetic model. Black-Right-Pointing-Pointer Improved likelihood functions are obtained to treat sequences of FRET efficiencies. - Abstract: We consider the analysis of a class of experiments in which the number of photons in consecutive time intervals is recorded. Sequence of photon counts or, alternatively, of FRET efficiencies can be studied using likelihood-based methods. For a kinetic model of the conformational dynamics and state-dependent Poisson photon statistics, the formalism to calculate the exact likelihood that this model describes such sequences of photons or FRET efficiencies is developed. Explicit analytic expressions for the likelihood function for a two-state kinetic model are provided. The important special case when conformational dynamics are so slow that at most a single transition occurs in a time bin is considered. By making a series of approximations, we eventually recover the likelihood function used in hidden Markov models. In this way, not only is insight gained into the range of validity of this procedure, but also an improved likelihood function can be obtained.

  15. Maximum likelihood estimation of finite mixture model for economic data

    Science.gov (United States)

    Phoong, Seuk-Yen; Ismail, Mohd Tahir

    2014-06-01

    Finite mixture model is a mixture model with finite-dimension. This models are provides a natural representation of heterogeneity in a finite number of latent classes. In addition, finite mixture models also known as latent class models or unsupervised learning models. Recently, maximum likelihood estimation fitted finite mixture models has greatly drawn statistician's attention. The main reason is because maximum likelihood estimation is a powerful statistical method which provides consistent findings as the sample sizes increases to infinity. Thus, the application of maximum likelihood estimation is used to fit finite mixture model in the present paper in order to explore the relationship between nonlinear economic data. In this paper, a two-component normal mixture model is fitted by maximum likelihood estimation in order to investigate the relationship among stock market price and rubber price for sampled countries. Results described that there is a negative effect among rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia.

  16. MXLKID: a maximum likelihood parameter identifier

    International Nuclear Information System (INIS)

    Gavel, D.T.

    1980-07-01

    MXLKID (MaXimum LiKelihood IDentifier) is a computer program designed to identify unknown parameters in a nonlinear dynamic system. Using noisy measurement data from the system, the maximum likelihood identifier computes a likelihood function (LF). Identification of system parameters is accomplished by maximizing the LF with respect to the parameters. The main body of this report briefly summarizes the maximum likelihood technique and gives instructions and examples for running the MXLKID program. MXLKID is implemented LRLTRAN on the CDC7600 computer at LLNL. A detailed mathematical description of the algorithm is given in the appendices. 24 figures, 6 tables

  17. Elemental composition of cosmic rays using a maximum likelihood method

    International Nuclear Information System (INIS)

    Ruddick, K.

    1996-01-01

    We present a progress report on our attempts to determine the composition of cosmic rays in the knee region of the energy spectrum. We have used three different devices to measure properties of the extensive air showers produced by primary cosmic rays: the Soudan 2 underground detector measures the muon flux deep underground, a proportional tube array samples shower density at the surface of the earth, and a Cherenkov array observes light produced high in the atmosphere. We have begun maximum likelihood fits to these measurements with the hope of determining the nuclear mass number A on an event by event basis. (orig.)

  18. New BFA Method Based on Attractor Neural Network and Likelihood Maximization

    Czech Academy of Sciences Publication Activity Database

    Frolov, A. A.; Húsek, Dušan; Polyakov, P.Y.; Snášel, V.

    2014-01-01

    Roč. 132, 20 May (2014), s. 14-29 ISSN 0925-2312 Grant - others:GA MŠk(CZ) ED1.1.00/02.0070; GA MŠk(CZ) EE.2.3.20.0073 Program:ED Institutional support: RVO:67985807 Keywords : recurrent neural network * associative memory * Hebbian learning rule * neural network application * data mining * statistics * Boolean factor analysis * information gain * dimension reduction * likelihood-maximization * bars problem Subject RIV: IN - Informatics, Computer Science Impact factor: 2.083, year: 2014

  19. Finite mixture model: A maximum likelihood estimation approach on time series data

    Science.gov (United States)

    Yen, Phoong Seuk; Ismail, Mohd Tahir; Hamzah, Firdaus Mohamad

    2014-09-01

    Recently, statistician emphasized on the fitting of finite mixture model by using maximum likelihood estimation as it provides asymptotic properties. In addition, it shows consistency properties as the sample sizes increases to infinity. This illustrated that maximum likelihood estimation is an unbiased estimator. Moreover, the estimate parameters obtained from the application of maximum likelihood estimation have smallest variance as compared to others statistical method as the sample sizes increases. Thus, maximum likelihood estimation is adopted in this paper to fit the two-component mixture model in order to explore the relationship between rubber price and exchange rate for Malaysia, Thailand, Philippines and Indonesia. Results described that there is a negative effect among rubber price and exchange rate for all selected countries.

  20. Adaptive Unscented Kalman Filter using Maximum Likelihood Estimation

    DEFF Research Database (Denmark)

    Mahmoudi, Zeinab; Poulsen, Niels Kjølstad; Madsen, Henrik

    2017-01-01

    The purpose of this study is to develop an adaptive unscented Kalman filter (UKF) by tuning the measurement noise covariance. We use the maximum likelihood estimation (MLE) and the covariance matching (CM) method to estimate the noise covariance. The multi-step prediction errors generated...

  1. Use of (D, MUF) and maximum-likelihood methods for detecting falsification and diversion in data-verification problems

    International Nuclear Information System (INIS)

    Goldman, A.S.; Beedgen, R.

    1982-01-01

    The investigation of data falsification and/or diversion is of major concern in nuclear materials accounting procedures used in international safeguards. In this paper, two procedures, denoted by (D,MUF) and LR (Likelihood Ratio), are discussed and compared when testing the hypothesis that neither diversion nor falsification has taken place versus the one-sided alternative that at least one of these parameters is positive. Critical regions and detection probabilities are given for both tests. It is shown that the LR method outperforms (D,MUF) when diversion and falsification take place

  2. Application of the Method of Maximum Likelihood to Identification of Bipedal Walking Robots

    Czech Academy of Sciences Publication Activity Database

    Dolinský, Kamil; Čelikovský, Sergej

    (2017) ISSN 1063-6536 R&D Projects: GA ČR(CZ) GA17-04682S Institutional support: RVO:67985556 Keywords : Control * identification * maximum likelihood (ML) * walking robots Subject RIV: BC - Control Systems Theory Impact factor: 3.882, year: 2016 http://ieeexplore.ieee.org/document/7954032/

  3. Comparison of two interpolation methods for empirical mode decomposition based evaluation of radiographic femur bone images.

    Science.gov (United States)

    Udhayakumar, Ganesan; Sujatha, Chinnaswamy Manoharan; Ramakrishnan, Swaminathan

    2013-01-01

    Analysis of bone strength in radiographic images is an important component of estimation of bone quality in diseases such as osteoporosis. Conventional radiographic femur bone images are used to analyze its architecture using bi-dimensional empirical mode decomposition method. Surface interpolation of local maxima and minima points of an image is a crucial part of bi-dimensional empirical mode decomposition method and the choice of appropriate interpolation depends on specific structure of the problem. In this work, two interpolation methods of bi-dimensional empirical mode decomposition are analyzed to characterize the trabecular femur bone architecture of radiographic images. The trabecular bone regions of normal and osteoporotic femur bone images (N = 40) recorded under standard condition are used for this study. The compressive and tensile strength regions of the images are delineated using pre-processing procedures. The delineated images are decomposed into their corresponding intrinsic mode functions using interpolation methods such as Radial basis function multiquadratic and hierarchical b-spline techniques. Results show that bi-dimensional empirical mode decomposition analyses using both interpolations are able to represent architectural variations of femur bone radiographic images. As the strength of the bone depends on architectural variation in addition to bone mass, this study seems to be clinically useful.

  4. The fine-tuning cost of the likelihood in SUSY models

    International Nuclear Information System (INIS)

    Ghilencea, D.M.; Ross, G.G.

    2013-01-01

    In SUSY models, the fine-tuning of the electroweak (EW) scale with respect to their parameters γ i ={m 0 ,m 1/2 ,μ 0 ,A 0 ,B 0 ,…} and the maximal likelihood L to fit the experimental data are usually regarded as two different problems. We show that, if one regards the EW minimum conditions as constraints that fix the EW scale, this commonly held view is not correct and that the likelihood contains all the information about fine-tuning. In this case we show that the corrected likelihood is equal to the ratio L/Δ of the usual likelihood L and the traditional fine-tuning measure Δ of the EW scale. A similar result is obtained for the integrated likelihood over the set {γ i }, that can be written as a surface integral of the ratio L/Δ, with the surface in γ i space determined by the EW minimum constraints. As a result, a large likelihood actually demands a large ratio L/Δ or equivalently, a small χ new 2 =χ old 2 +2lnΔ. This shows the fine-tuning cost to the likelihood (χ new 2 ) of the EW scale stability enforced by SUSY, that is ignored in data fits. A good χ new 2 /d.o.f.≈1 thus demands SUSY models have a fine-tuning amount Δ≪exp(d.o.f./2), which provides a model-independent criterion for acceptable fine-tuning. If this criterion is not met, one can thus rule out SUSY models without a further χ 2 /d.o.f. analysis. Numerical methods to fit the data can easily be adapted to account for this effect.

  5. Maximum likelihood of phylogenetic networks.

    Science.gov (United States)

    Jin, Guohua; Nakhleh, Luay; Snir, Sagi; Tuller, Tamir

    2006-11-01

    Horizontal gene transfer (HGT) is believed to be ubiquitous among bacteria, and plays a major role in their genome diversification as well as their ability to develop resistance to antibiotics. In light of its evolutionary significance and implications for human health, developing accurate and efficient methods for detecting and reconstructing HGT is imperative. In this article we provide a new HGT-oriented likelihood framework for many problems that involve phylogeny-based HGT detection and reconstruction. Beside the formulation of various likelihood criteria, we show that most of these problems are NP-hard, and offer heuristics for efficient and accurate reconstruction of HGT under these criteria. We implemented our heuristics and used them to analyze biological as well as synthetic data. In both cases, our criteria and heuristics exhibited very good performance with respect to identifying the correct number of HGT events as well as inferring their correct location on the species tree. Implementation of the criteria as well as heuristics and hardness proofs are available from the authors upon request. Hardness proofs can also be downloaded at http://www.cs.tau.ac.il/~tamirtul/MLNET/Supp-ML.pdf

  6. Obtaining reliable Likelihood Ratio tests from simulated likelihood functions

    DEFF Research Database (Denmark)

    Andersen, Laura Mørch

    It is standard practice by researchers and the default option in many statistical programs to base test statistics for mixed models on simulations using asymmetric draws (e.g. Halton draws). This paper shows that when the estimated likelihood functions depend on standard deviations of mixed param...

  7. Gaussian copula as a likelihood function for environmental models

    Science.gov (United States)

    Wani, O.; Espadas, G.; Cecinati, F.; Rieckermann, J.

    2017-12-01

    Parameter estimation of environmental models always comes with uncertainty. To formally quantify this parametric uncertainty, a likelihood function needs to be formulated, which is defined as the probability of observations given fixed values of the parameter set. A likelihood function allows us to infer parameter values from observations using Bayes' theorem. The challenge is to formulate a likelihood function that reliably describes the error generating processes which lead to the observed monitoring data, such as rainfall and runoff. If the likelihood function is not representative of the error statistics, the parameter inference will give biased parameter values. Several uncertainty estimation methods that are currently being used employ Gaussian processes as a likelihood function, because of their favourable analytical properties. Box-Cox transformation is suggested to deal with non-symmetric and heteroscedastic errors e.g. for flow data which are typically more uncertain in high flows than in periods with low flows. Problem with transformations is that the results are conditional on hyper-parameters, for which it is difficult to formulate the analyst's belief a priori. In an attempt to address this problem, in this research work we suggest learning the nature of the error distribution from the errors made by the model in the "past" forecasts. We use a Gaussian copula to generate semiparametric error distributions . 1) We show that this copula can be then used as a likelihood function to infer parameters, breaking away from the practice of using multivariate normal distributions. Based on the results from a didactical example of predicting rainfall runoff, 2) we demonstrate that the copula captures the predictive uncertainty of the model. 3) Finally, we find that the properties of autocorrelation and heteroscedasticity of errors are captured well by the copula, eliminating the need to use transforms. In summary, our findings suggest that copulas are an

  8. A nonparametric empirical Bayes framework for large-scale multiple testing.

    Science.gov (United States)

    Martin, Ryan; Tokdar, Surya T

    2012-07-01

    We propose a flexible and identifiable version of the 2-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the nonnull cases. We use a computationally efficient predictive recursion (PR) marginal likelihood procedure to estimate the model parameters, even the nonparametric mixing distribution. This leads to a nonparametric empirical Bayes testing procedure, which we call PRtest, based on thresholding the estimated local false discovery rates. Simulations and real data examples demonstrate that, compared to existing approaches, PRtest's careful handling of the nonnull density can give a much better fit in the tails of the mixture distribution which, in turn, can lead to more realistic conclusions.

  9. Maximum likelihood as a common computational framework in tomotherapy

    International Nuclear Information System (INIS)

    Olivera, G.H.; Shepard, D.M.; Reckwerdt, P.J.; Ruchala, K.; Zachman, J.; Fitchard, E.E.; Mackie, T.R.

    1998-01-01

    Tomotherapy is a dose delivery technique using helical or axial intensity modulated beams. One of the strengths of the tomotherapy concept is that it can incorporate a number of processes into a single piece of equipment. These processes include treatment optimization planning, dose reconstruction and kilovoltage/megavoltage image reconstruction. A common computational technique that could be used for all of these processes would be very appealing. The maximum likelihood estimator, originally developed for emission tomography, can serve as a useful tool in imaging and radiotherapy. We believe that this approach can play an important role in the processes of optimization planning, dose reconstruction and kilovoltage and/or megavoltage image reconstruction. These processes involve computations that require comparable physical methods. They are also based on equivalent assumptions, and they have similar mathematical solutions. As a result, the maximum likelihood approach is able to provide a common framework for all three of these computational problems. We will demonstrate how maximum likelihood methods can be applied to optimization planning, dose reconstruction and megavoltage image reconstruction in tomotherapy. Results for planning optimization, dose reconstruction and megavoltage image reconstruction will be presented. Strengths and weaknesses of the methodology are analysed. Future directions for this work are also suggested. (author)

  10. Bias Correction for the Maximum Likelihood Estimate of Ability. Research Report. ETS RR-05-15

    Science.gov (United States)

    Zhang, Jinming

    2005-01-01

    Lord's bias function and the weighted likelihood estimation method are effective in reducing the bias of the maximum likelihood estimate of an examinee's ability under the assumption that the true item parameters are known. This paper presents simulation studies to determine the effectiveness of these two methods in reducing the bias when the item…

  11. Evaluating Method Engineer Performance: an error classification and preliminary empirical study

    Directory of Open Access Journals (Sweden)

    Steven Kelly

    1998-11-01

    Full Text Available We describe an approach to empirically test the use of metaCASE environments to model methods. Both diagrams and matrices have been proposed as a means for presenting the methods. These different paradigms may have their own effects on how easily and well users can model methods. We extend Batra's classification of errors in data modelling to cover metamodelling, and use it to measure the performance of a group of metamodellers using either diagrams or matrices. The tentative results from this pilot study confirm the usefulness of the classification, and show some interesting differences between the paradigms.

  12. Empiric auto-titrating CPAP in people with suspected obstructive sleep apnea.

    Science.gov (United States)

    Drummond, Fitzgerald; Doelken, Peter; Ahmed, Qanta A; Gilbert, Gregory E; Strange, Charlie; Herpel, Laura; Frye, Michael D

    2010-04-15

    Efficient diagnosis and treatment of obstructive sleep apnea (OSA) can be difficult because of time delays imposed by clinic visits and serial overnight polysomnography. In some cases, it may be desirable to initiate treatment for suspected OSA prior to polysomnography. Our objective was to compare the improvement of daytime sleepiness and sleep-related quality of life of patients with high clinical likelihood of having OSA who were randomly assigned to receive empiric auto-titrating continuous positive airway pressure (CPAP) while awaiting polysomnogram versus current usual care. Serial patients referred for overnight polysomnography who had high clinical likelihood of having OSA were randomly assigned to usual care or immediate initiation of auto-titrating CPAP. Epworth Sleepiness Scale (ESS) scores and the Functional Outcomes of Sleep Questionnaire (FOSQ) scores were obtained at baseline, 1 month after randomization, and again after initiation of fixed CPAP in control subjects and after the sleep study in auto-CPAP patients. One hundred nine patients were randomized. Baseline demographics, daytime sleepiness, and sleep-related quality of life scores were similar between groups. One-month ESS and FOSQ scores were improved in the group empirically treated with auto-titrating CPAP. ESS scores improved in the first month by a mean of -3.2 (confidence interval -1.6 to -4.8, p life in a cohort of patients awaiting polysomnography who had a high pretest probability of having OSA. Additional studies are needed to evaluate the applicability of empiric treatment to other populations.

  13. Safe semi-supervised learning based on weighted likelihood.

    Science.gov (United States)

    Kawakita, Masanori; Takeuchi, Jun'ichi

    2014-05-01

    We are interested in developing a safe semi-supervised learning that works in any situation. Semi-supervised learning postulates that n(') unlabeled data are available in addition to n labeled data. However, almost all of the previous semi-supervised methods require additional assumptions (not only unlabeled data) to make improvements on supervised learning. If such assumptions are not met, then the methods possibly perform worse than supervised learning. Sokolovska, Cappé, and Yvon (2008) proposed a semi-supervised method based on a weighted likelihood approach. They proved that this method asymptotically never performs worse than supervised learning (i.e., it is safe) without any assumption. Their method is attractive because it is easy to implement and is potentially general. Moreover, it is deeply related to a certain statistical paradox. However, the method of Sokolovska et al. (2008) assumes a very limited situation, i.e., classification, discrete covariates, n(')→∞ and a maximum likelihood estimator. In this paper, we extend their method by modifying the weight. We prove that our proposal is safe in a significantly wide range of situations as long as n≤n('). Further, we give a geometrical interpretation of the proof of safety through the relationship with the above-mentioned statistical paradox. Finally, we show that the above proposal is asymptotically safe even when n(')methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Maximum Likelihood Compton Polarimetry with the Compton Spectrometer and Imager

    Energy Technology Data Exchange (ETDEWEB)

    Lowell, A. W.; Boggs, S. E; Chiu, C. L.; Kierans, C. A.; Sleator, C.; Tomsick, J. A.; Zoglauer, A. C. [Space Sciences Laboratory, University of California, Berkeley (United States); Chang, H.-K.; Tseng, C.-H.; Yang, C.-Y. [Institute of Astronomy, National Tsing Hua University, Taiwan (China); Jean, P.; Ballmoos, P. von [IRAP Toulouse (France); Lin, C.-H. [Institute of Physics, Academia Sinica, Taiwan (China); Amman, M. [Lawrence Berkeley National Laboratory (United States)

    2017-10-20

    Astrophysical polarization measurements in the soft gamma-ray band are becoming more feasible as detectors with high position and energy resolution are deployed. Previous work has shown that the minimum detectable polarization (MDP) of an ideal Compton polarimeter can be improved by ∼21% when an unbinned, maximum likelihood method (MLM) is used instead of the standard approach of fitting a sinusoid to a histogram of azimuthal scattering angles. Here we outline a procedure for implementing this maximum likelihood approach for real, nonideal polarimeters. As an example, we use the recent observation of GRB 160530A with the Compton Spectrometer and Imager. We find that the MDP for this observation is reduced by 20% when the MLM is used instead of the standard method.

  15. Maximum-likelihood fitting of data dominated by Poisson statistical uncertainties

    International Nuclear Information System (INIS)

    Stoneking, M.R.; Den Hartog, D.J.

    1996-06-01

    The fitting of data by χ 2 -minimization is valid only when the uncertainties in the data are normally distributed. When analyzing spectroscopic or particle counting data at very low signal level (e.g., a Thomson scattering diagnostic), the uncertainties are distributed with a Poisson distribution. The authors have developed a maximum-likelihood method for fitting data that correctly treats the Poisson statistical character of the uncertainties. This method maximizes the total probability that the observed data are drawn from the assumed fit function using the Poisson probability function to determine the probability for each data point. The algorithm also returns uncertainty estimates for the fit parameters. They compare this method with a χ 2 -minimization routine applied to both simulated and real data. Differences in the returned fits are greater at low signal level (less than ∼20 counts per measurement). the maximum-likelihood method is found to be more accurate and robust, returning a narrower distribution of values for the fit parameters with fewer outliers

  16. A biclustering algorithm for binary matrices based on penalized Bernoulli likelihood

    KAUST Repository

    Lee, Seokho; Huang, Jianhua Z.

    2013-01-01

    We propose a new biclustering method for binary data matrices using the maximum penalized Bernoulli likelihood estimation. Our method applies a multi-layer model defined on the logits of the success probabilities, where each layer represents a

  17. Efficiency indicators versus forntier methods: an empirical investigation of italian public hospitals

    Directory of Open Access Journals (Sweden)

    Lorenzo Clementi

    2013-05-01

    Full Text Available Efficiency has a key-role in the measurement of the impact of the National Health Service (NHS reforms. We investigate the issue of inefficiency in health sector and provide empirical evidence derived from Italian public hospitals. Despite the importance of efficiency measurement in health care services, only recently advanced econometric methods have been applied to hospital data. We provide a synoptic survey of few empirical analyses of efficiency measurement in health care services. An estimate of the cost efficiency level in Italian public hospitals during 2001-2003 is obtained through a sample. We propose an efficiency indicator and provide cost frontiers for such hospitals, using stochastic frontier analysis (SFA for longitudinal data.

  18. Optimized Large-scale CMB Likelihood and Quadratic Maximum Likelihood Power Spectrum Estimation

    Science.gov (United States)

    Gjerløw, E.; Colombo, L. P. L.; Eriksen, H. K.; Górski, K. M.; Gruppuso, A.; Jewell, J. B.; Plaszczynski, S.; Wehus, I. K.

    2015-11-01

    We revisit the problem of exact cosmic microwave background (CMB) likelihood and power spectrum estimation with the goal of minimizing computational costs through linear compression. This idea was originally proposed for CMB purposes by Tegmark et al., and here we develop it into a fully functioning computational framework for large-scale polarization analysis, adopting WMAP as a working example. We compare five different linear bases (pixel space, harmonic space, noise covariance eigenvectors, signal-to-noise covariance eigenvectors, and signal-plus-noise covariance eigenvectors) in terms of compression efficiency, and find that the computationally most efficient basis is the signal-to-noise eigenvector basis, which is closely related to the Karhunen-Loeve and Principal Component transforms, in agreement with previous suggestions. For this basis, the information in 6836 unmasked WMAP sky map pixels can be compressed into a smaller set of 3102 modes, with a maximum error increase of any single multipole of 3.8% at ℓ ≤ 32 and a maximum shift in the mean values of a joint distribution of an amplitude-tilt model of 0.006σ. This compression reduces the computational cost of a single likelihood evaluation by a factor of 5, from 38 to 7.5 CPU seconds, and it also results in a more robust likelihood by implicitly regularizing nearly degenerate modes. Finally, we use the same compression framework to formulate a numerically stable and computationally efficient variation of the Quadratic Maximum Likelihood implementation, which requires less than 3 GB of memory and 2 CPU minutes per iteration for ℓ ≤ 32, rendering low-ℓ QML CMB power spectrum analysis fully tractable on a standard laptop.

  19. Tourism forecasting using modified empirical mode decomposition and group method of data handling

    Science.gov (United States)

    Yahya, N. A.; Samsudin, R.; Shabri, A.

    2017-09-01

    In this study, a hybrid model using modified Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) model is proposed for tourism forecasting. This approach reconstructs intrinsic mode functions (IMFs) produced by EMD using trial and error method. The new component and the remaining IMFs is then predicted respectively using GMDH model. Finally, the forecasted results for each component are aggregated to construct an ensemble forecast. The data used in this experiment are monthly time series data of tourist arrivals from China, Thailand and India to Malaysia from year 2000 to 2016. The performance of the model is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) where conventional GMDH model and EMD-GMDH model are used as benchmark models. Empirical results proved that the proposed model performed better forecasts than the benchmarked models.

  20. Bayes Empirical Bayes Inference of Amino Acid Sites Under Positive Selection

    DEFF Research Database (Denmark)

    Yang, Ziheng; Wong, Wendy Shuk Wan; Nielsen, Rasmus

    2005-01-01

    , with > 1 indicating positive selection. Statistical distributions are used to model the variation in among sites, allowing a subset of sites to have > 1 while the rest of the sequence may be under purifying selection with ... probabilities that a site comes from the site class with > 1. Current implementations, however, use the naive EB (NEB) approach and fail to account for sampling errors in maximum likelihood estimates of model parameters, such as the proportions and ratios for the site classes. In small data sets lacking...... information, this approach may lead to unreliable posterior probability calculations. In this paper, we develop a Bayes empirical Bayes (BEB) approach to the problem, which assigns a prior to the model parameters and integrates over their uncertainties. We compare the new and old methods on real and simulated...

  1. Interface of the polarizable continuum model of solvation with semi-empirical methods in the GAMESS program

    DEFF Research Database (Denmark)

    Svendsen, Casper Steinmann; Blædel, Kristoffer L.; Christensen, Anders Steen

    2013-01-01

    An interface between semi-empirical methods and the polarized continuum model (PCM) of solvation successfully implemented into GAMESS following the approach by Chudinov et al (Chem. Phys. 1992, 160, 41). The interface includes energy gradients and is parallelized. For large molecules such as ubiq......An interface between semi-empirical methods and the polarized continuum model (PCM) of solvation successfully implemented into GAMESS following the approach by Chudinov et al (Chem. Phys. 1992, 160, 41). The interface includes energy gradients and is parallelized. For large molecules...

  2. Receiver-operating characteristic curves and likelihood ratios: improvements over traditional methods for the evaluation and application of veterinary clinical pathology tests

    DEFF Research Database (Denmark)

    Gardner, Ian A.; Greiner, Matthias

    2006-01-01

    Receiver-operating characteristic (ROC) curves provide a cutoff-independent method for the evaluation of continuous or ordinal tests used in clinical pathology laboratories. The area under the curve is a useful overall measure of test accuracy and can be used to compare different tests (or...... different equipment) used by the same tester, as well as the accuracy of different diagnosticians that use the same test material. To date, ROC analysis has not been widely used in veterinary clinical pathology studies, although it should be considered a useful complement to estimates of sensitivity...... and specificity in test evaluation studies. In addition, calculation of likelihood ratios can potentially improve the clinical utility of such studies because likelihood ratios provide an indication of how the post-test probability changes as a function of the magnitude of the test results. For ordinal test...

  3. Aircraft directional stability and vertical tail design: A review of semi-empirical methods

    Science.gov (United States)

    Ciliberti, Danilo; Della Vecchia, Pierluigi; Nicolosi, Fabrizio; De Marco, Agostino

    2017-11-01

    Aircraft directional stability and control are related to vertical tail design. The safety, performance, and flight qualities of an aircraft also depend on a correct empennage sizing. Specifically, the vertical tail is responsible for the aircraft yaw stability and control. If these characteristics are not well balanced, the entire aircraft design may fail. Stability and control are often evaluated, especially in the preliminary design phase, with semi-empirical methods, which are based on the results of experimental investigations performed in the past decades, and occasionally are merged with data provided by theoretical assumptions. This paper reviews the standard semi-empirical methods usually applied in the estimation of airplane directional stability derivatives in preliminary design, highlighting the advantages and drawbacks of these approaches that were developed from wind tunnel tests performed mainly on fighter airplane configurations of the first decades of the past century, and discussing their applicability on current transport aircraft configurations. Recent investigations made by the authors have shown the limit of these methods, proving the existence of aerodynamic interference effects in sideslip conditions which are not adequately considered in classical formulations. The article continues with a concise review of the numerical methods for aerodynamics and their applicability in aircraft design, highlighting how Reynolds-Averaged Navier-Stokes (RANS) solvers are well-suited to attain reliable results in attached flow conditions, with reasonable computational times. From the results of RANS simulations on a modular model of a representative regional turboprop airplane layout, the authors have developed a modern method to evaluate the vertical tail and fuselage contributions to aircraft directional stability. The investigation on the modular model has permitted an effective analysis of the aerodynamic interference effects by moving, changing, and

  4. Advanced statistical methods in data science

    CERN Document Server

    Chen, Jiahua; Lu, Xuewen; Yi, Grace; Yu, Hao

    2016-01-01

    This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a fu...

  5. Maximum Likelihood Blind Channel Estimation for Space-Time Coding Systems

    Directory of Open Access Journals (Sweden)

    Hakan A. Çırpan

    2002-05-01

    Full Text Available Sophisticated signal processing techniques have to be developed for capacity enhancement of future wireless communication systems. In recent years, space-time coding is proposed to provide significant capacity gains over the traditional communication systems in fading wireless channels. Space-time codes are obtained by combining channel coding, modulation, transmit diversity, and optional receive diversity in order to provide diversity at the receiver and coding gain without sacrificing the bandwidth. In this paper, we consider the problem of blind estimation of space-time coded signals along with the channel parameters. Both conditional and unconditional maximum likelihood approaches are developed and iterative solutions are proposed. The conditional maximum likelihood algorithm is based on iterative least squares with projection whereas the unconditional maximum likelihood approach is developed by means of finite state Markov process modelling. The performance analysis issues of the proposed methods are studied. Finally, some simulation results are presented.

  6. Robust Gaussian Process Regression with a Student-t Likelihood

    NARCIS (Netherlands)

    Jylänki, P.P.; Vanhatalo, J.; Vehtari, A.

    2011-01-01

    This paper considers the robust and efficient implementation of Gaussian process regression with a Student-t observation model, which has a non-log-concave likelihood. The challenge with the Student-t model is the analytically intractable inference which is why several approximative methods have

  7. Implementation and assessment of a likelihood ratio approach for the evaluation of LA-ICP-MS evidence in forensic glass analysis.

    Science.gov (United States)

    van Es, Andrew; Wiarda, Wim; Hordijk, Maarten; Alberink, Ivo; Vergeer, Peter

    2017-05-01

    For the comparative analysis of glass fragments, a method using Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) is in use at the NFI, giving measurements of the concentration of 18 elements. An important question is how to evaluate the results as evidence that a glass sample originates from a known glass source or from an arbitrary different glass source. One approach is the use of matching criteria e.g. based on a t-test or overlap of confidence intervals. An important drawback of this method is the fact that the rarity of the glass composition is not taken into account. A similar match can have widely different evidential values. In addition the use of fixed matching criteria can give rise to a "fall off the cliff" effect. Small differences may result in a match or a non-match. In this work a likelihood ratio system is presented, largely based on the two-level model as proposed by Aitken and Lucy [1], and Aitken, Zadora and Lucy [2]. Results show that the output from the two-level model gives good discrimination between same and different source hypotheses, but a post-hoc calibration step is necessary to improve the accuracy of the likelihood ratios. Subsequently, the robustness and performance of the LR system are studied. Results indicate that the output of the LR system is robust to the sample properties of the dataset used for calibration. Furthermore, the empirical upper and lower bound method [3], designed to deal with extrapolation errors in the density models, results in minimum and maximum values of the LR outputted by the system of 3.1×10 -3 and 3.4×10 4 . Calibration of the system, as measured by empirical cross-entropy, shows good behavior over the complete prior range. Rates of misleading evidence are small: for same-source comparisons, 0.3% of LRs support a different-source hypothesis; for different-source comparisons, 0.2% supports a same-source hypothesis. The authors use the LR system in reporting of glass cases to

  8. Secondary Analysis under Cohort Sampling Designs Using Conditional Likelihood

    Directory of Open Access Journals (Sweden)

    Olli Saarela

    2012-01-01

    Full Text Available Under cohort sampling designs, additional covariate data are collected on cases of a specific type and a randomly selected subset of noncases, primarily for the purpose of studying associations with a time-to-event response of interest. With such data available, an interest may arise to reuse them for studying associations between the additional covariate data and a secondary non-time-to-event response variable, usually collected for the whole study cohort at the outset of the study. Following earlier literature, we refer to such a situation as secondary analysis. We outline a general conditional likelihood approach for secondary analysis under cohort sampling designs and discuss the specific situations of case-cohort and nested case-control designs. We also review alternative methods based on full likelihood and inverse probability weighting. We compare the alternative methods for secondary analysis in two simulated settings and apply them in a real-data example.

  9. The scale-dependent market trend: Empirical evidences using the lagged DFA method

    Science.gov (United States)

    Li, Daye; Kou, Zhun; Sun, Qiankun

    2015-09-01

    In this paper we make an empirical research and test the efficiency of 44 important market indexes in multiple scales. A modified method based on the lagged detrended fluctuation analysis is utilized to maximize the information of long-term correlations from the non-zero lags and keep the margin of errors small when measuring the local Hurst exponent. Our empirical result illustrates that a common pattern can be found in the majority of the measured market indexes which tend to be persistent (with the local Hurst exponent > 0.5) in the small time scale, whereas it displays significant anti-persistent characteristics in large time scales. Moreover, not only the stock markets but also the foreign exchange markets share this pattern. Considering that the exchange markets are only weakly synchronized with the economic cycles, it can be concluded that the economic cycles can cause anti-persistence in the large time scale but there are also other factors at work. The empirical result supports the view that financial markets are multi-fractal and it indicates that deviations from efficiency and the type of model to describe the trend of market price are dependent on the forecasting horizon.

  10. PERBANDINGAN ESTIMASI KEMAMPUAN LATEN ANTARA METODE MAKSIMUM LIKELIHOOD DAN METODE BAYES

    Directory of Open Access Journals (Sweden)

    Heri Retnawati

    2015-10-01

    Full Text Available Studi ini bertujuan untuk membandingkan ketepatan estimasi kemampuan laten (latent trait pada model logistik dengan metode maksimum likelihood (ML gabungan dan bayes. Studi ini menggunakan metode simulasi Monte Carlo, dengan model data ujian nasional matematika SMP. Variabel simulasi adalah panjang tes dan banyaknya peserta.  Data dibangkitkan dengan menggunakan SAS/IML dengan replikasi 40 kali, dan tiap data diestimasi dengan ML dan Bayes. Hasil estimasi kemudian dibandingkan dengan kemampuan yang sebenarnya, dengan menghitung mean square of error (MSE dan korelasi antara kemampuan laten yang sebenarnya dan hasil estimasi. Metode yang memiliki MSE lebih kecil dikatakan sebagai metode estimasi yang lebih baik. Hasil studi menunjukkan bahwa pada estimasi kemampuan laten dengan 15, 20, 25, dan 30 butir dengan 500 dan 1.000 peserta, hasil MSE belum stabil, namun ketika peserta menjadi 1.500 orang, diperoleh akurasi estimasi kemampuan yang hampir sama baik estimasi antara metode ML dan metode Bayes. Pada estimasi dengan 15 dan 20 butir dan peserta 500, 1.000, dan 1.500, hasil MSE belum stabil, dan ketika estimasi melibatkan 25 dan 30 butir, baik dengan peserta 500, 1.000, maupun 1.500 akan diperoleh hasil yang lebih akurat dengan metode ML. Kata kunci: estimasi kemampuan, metode maksimum likelihood, metode Bayes     THE COMPARISON OF ESTIMATION OF LATENT TRAITS USING MAXIMUM LIKELIHOOD AND BAYES METHODS Abstract This study aimed to compare the accuracy of the estimation of latent ability (latent trait in the logistic model using maximum likelihood (ML and Bayes methods. This study uses a quantitative approach that is the Monte Carlo simulation method using students responses to national examination as data model, and variables are the length of the test and the number of participants. The data were generated using SAS/IML with replication 40 times, and each datum is then estimated by ML and Bayes. The estimation results are then compared with the

  11. Can Machines Learn Respiratory Virus Epidemiology?: A Comparative Study of Likelihood-Free Methods for the Estimation of Epidemiological Dynamics

    Directory of Open Access Journals (Sweden)

    Heidi L. Tessmer

    2018-03-01

    Full Text Available To estimate and predict the transmission dynamics of respiratory viruses, the estimation of the basic reproduction number, R0, is essential. Recently, approximate Bayesian computation methods have been used as likelihood free methods to estimate epidemiological model parameters, particularly R0. In this paper, we explore various machine learning approaches, the multi-layer perceptron, convolutional neural network, and long-short term memory, to learn and estimate the parameters. Further, we compare the accuracy of the estimates and time requirements for machine learning and the approximate Bayesian computation methods on both simulated and real-world epidemiological data from outbreaks of influenza A(H1N1pdm09, mumps, and measles. We find that the machine learning approaches can be verified and tested faster than the approximate Bayesian computation method, but that the approximate Bayesian computation method is more robust across different datasets.

  12. Maximum Likelihood Approach for RFID Tag Set Cardinality Estimation with Detection Errors

    DEFF Research Database (Denmark)

    Nguyen, Chuyen T.; Hayashi, Kazunori; Kaneko, Megumi

    2013-01-01

    Abstract Estimation schemes of Radio Frequency IDentification (RFID) tag set cardinality are studied in this paper using Maximum Likelihood (ML) approach. We consider the estimation problem under the model of multiple independent reader sessions with detection errors due to unreliable radio...... is evaluated under dierent system parameters and compared with that of the conventional method via computer simulations assuming flat Rayleigh fading environments and framed-slotted ALOHA based protocol. Keywords RFID tag cardinality estimation maximum likelihood detection error...

  13. Empirical methods for estimating future climatic conditions

    International Nuclear Information System (INIS)

    Anon.

    1990-01-01

    Applying the empirical approach permits the derivation of estimates of the future climate that are nearly independent of conclusions based on theoretical (model) estimates. This creates an opportunity to compare these results with those derived from the model simulations of the forthcoming changes in climate, thus increasing confidence in areas of agreement and focusing research attention on areas of disagreements. The premise underlying this approach for predicting anthropogenic climate change is based on associating the conditions of the climatic optimums of the Holocene, Eemian, and Pliocene with corresponding stages of the projected increase of mean global surface air temperature. Provided that certain assumptions are fulfilled in matching the value of the increased mean temperature for a certain epoch with the model-projected change in global mean temperature in the future, the empirical approach suggests that relationships leading to the regional variations in air temperature and other meteorological elements could be deduced and interpreted based on use of empirical data describing climatic conditions for past warm epochs. Considerable care must be taken, of course, in making use of these spatial relationships, especially in accounting for possible large-scale differences that might, in some cases, result from different factors contributing to past climate changes than future changes and, in other cases, might result from the possible influences of changes in orography and geography on regional climatic conditions over time

  14. An Empirical Review of Research Methodologies and Methods in Creativity Studies (2003-2012)

    Science.gov (United States)

    Long, Haiying

    2014-01-01

    Based on the data collected from 5 prestigious creativity journals, research methodologies and methods of 612 empirical studies on creativity, published between 2003 and 2012, were reviewed and compared to those in gifted education. Major findings included: (a) Creativity research was predominantly quantitative and psychometrics and experiment…

  15. Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models

    NARCIS (Netherlands)

    Mesters, G.; Koopman, S.J.; Ooms, M.

    2016-01-01

    An exact maximum likelihood method is developed for the estimation of parameters in a non-Gaussian nonlinear density function that depends on a latent Gaussian dynamic process with long-memory properties. Our method relies on the method of importance sampling and on a linear Gaussian approximating

  16. Bioactive conformational generation of small molecules: A comparative analysis between force-field and multiple empirical criteria based methods

    Directory of Open Access Journals (Sweden)

    Jiang Hualiang

    2010-11-01

    Full Text Available Abstract Background Conformational sampling for small molecules plays an essential role in drug discovery research pipeline. Based on multi-objective evolution algorithm (MOEA, we have developed a conformational generation method called Cyndi in the previous study. In this work, in addition to Tripos force field in the previous version, Cyndi was updated by incorporation of MMFF94 force field to assess the conformational energy more rationally. With two force fields against a larger dataset of 742 bioactive conformations of small ligands extracted from PDB, a comparative analysis was performed between pure force field based method (FFBM and multiple empirical criteria based method (MECBM hybrided with different force fields. Results Our analysis reveals that incorporating multiple empirical rules can significantly improve the accuracy of conformational generation. MECBM, which takes both empirical and force field criteria as the objective functions, can reproduce about 54% (within 1Å RMSD of the bioactive conformations in the 742-molecule testset, much higher than that of pure force field method (FFBM, about 37%. On the other hand, MECBM achieved a more complete and efficient sampling of the conformational space because the average size of unique conformations ensemble per molecule is about 6 times larger than that of FFBM, while the time scale for conformational generation is nearly the same as FFBM. Furthermore, as a complementary comparison study between the methods with and without empirical biases, we also tested the performance of the three conformational generation methods in MacroModel in combination with different force fields. Compared with the methods in MacroModel, MECBM is more competitive in retrieving the bioactive conformations in light of accuracy but has much lower computational cost. Conclusions By incorporating different energy terms with several empirical criteria, the MECBM method can produce more reasonable conformational

  17. The Laplace Likelihood Ratio Test for Heteroscedasticity

    Directory of Open Access Journals (Sweden)

    J. Martin van Zyl

    2011-01-01

    Full Text Available It is shown that the likelihood ratio test for heteroscedasticity, assuming the Laplace distribution, gives good results for Gaussian and fat-tailed data. The likelihood ratio test, assuming normality, is very sensitive to any deviation from normality, especially when the observations are from a distribution with fat tails. Such a likelihood test can also be used as a robust test for a constant variance in residuals or a time series if the data is partitioned into groups.

  18. Computation of the Likelihood in Biallelic Diffusion Models Using Orthogonal Polynomials

    Directory of Open Access Journals (Sweden)

    Claus Vogl

    2014-11-01

    Full Text Available In population genetics, parameters describing forces such as mutation, migration and drift are generally inferred from molecular data. Lately, approximate methods based on simulations and summary statistics have been widely applied for such inference, even though these methods waste information. In contrast, probabilistic methods of inference can be shown to be optimal, if their assumptions are met. In genomic regions where recombination rates are high relative to mutation rates, polymorphic nucleotide sites can be assumed to evolve independently from each other. The distribution of allele frequencies at a large number of such sites has been called “allele-frequency spectrum” or “site-frequency spectrum” (SFS. Conditional on the allelic proportions, the likelihoods of such data can be modeled as binomial. A simple model representing the evolution of allelic proportions is the biallelic mutation-drift or mutation-directional selection-drift diffusion model. With series of orthogonal polynomials, specifically Jacobi and Gegenbauer polynomials, or the related spheroidal wave function, the diffusion equations can be solved efficiently. In the neutral case, the product of the binomial likelihoods with the sum of such polynomials leads to finite series of polynomials, i.e., relatively simple equations, from which the exact likelihoods can be calculated. In this article, the use of orthogonal polynomials for inferring population genetic parameters is investigated.

  19. Practical aspects of a maximum likelihood estimation method to extract stability and control derivatives from flight data

    Science.gov (United States)

    Iliff, K. W.; Maine, R. E.

    1976-01-01

    A maximum likelihood estimation method was applied to flight data and procedures to facilitate the routine analysis of a large amount of flight data were described. Techniques that can be used to obtain stability and control derivatives from aircraft maneuvers that are less than ideal for this purpose are described. The techniques involve detecting and correcting the effects of dependent or nearly dependent variables, structural vibration, data drift, inadequate instrumentation, and difficulties with the data acquisition system and the mathematical model. The use of uncertainty levels and multiple maneuver analysis also proved to be useful in improving the quality of the estimated coefficients. The procedures used for editing the data and for overall analysis are also discussed.

  20. Empirical Hamiltonians

    International Nuclear Information System (INIS)

    Peggs, S.; Talman, R.

    1987-01-01

    As proton accelerators get larger, and include more magnets, the conventional tracking programs which simulate them run slower. The purpose of this paper is to describe a method, still under development, in which element-by-element tracking around one turn is replaced by a single man, which can be processed far faster. It is assumed for this method that a conventional program exists which can perform faithful tracking in the lattice under study for some hundreds of turns, with all lattice parameters held constant. An empirical map is then generated by comparison with the tracking program. A procedure has been outlined for determining an empirical Hamiltonian, which can represent motion through many nonlinear kicks, by taking data from a conventional tracking program. Though derived by an approximate method this Hamiltonian is analytic in form and can be subjected to further analysis of varying degrees of mathematical rigor. Even though the empirical procedure has only been described in one transverse dimension, there is good reason to hope that it can be extended to include two transverse dimensions, so that it can become a more practical tool in realistic cases

  1. Assessing different parameters estimation methods of Weibull distribution to compute wind power density

    International Nuclear Information System (INIS)

    Mohammadi, Kasra; Alavi, Omid; Mostafaeipour, Ali; Goudarzi, Navid; Jalilvand, Mahdi

    2016-01-01

    Highlights: • Effectiveness of six numerical methods is evaluated to determine wind power density. • More appropriate method for computing the daily wind power density is estimated. • Four windy stations located in the south part of Alberta, Canada namely is investigated. • The more appropriate parameters estimation method was not identical among all examined stations. - Abstract: In this study, the effectiveness of six numerical methods is evaluated to determine the shape (k) and scale (c) parameters of Weibull distribution function for the purpose of calculating the wind power density. The selected methods are graphical method (GP), empirical method of Justus (EMJ), empirical method of Lysen (EML), energy pattern factor method (EPF), maximum likelihood method (ML) and modified maximum likelihood method (MML). The purpose of this study is to identify the more appropriate method for computing the wind power density in four stations distributed in Alberta province of Canada namely Edmonton City Center Awos, Grande Prairie A, Lethbridge A and Waterton Park Gate. To provide a complete analysis, the evaluations are performed on both daily and monthly scales. The results indicate that the precision of computed wind power density values change when different parameters estimation methods are used to determine the k and c parameters. Four methods of EMJ, EML, EPF and ML present very favorable efficiency while the GP method shows weak ability for all stations. However, it is found that the more effective method is not similar among stations owing to the difference in the wind characteristics.

  2. Band structure calculation of GaSe-based nanostructures using empirical pseudopotential method

    International Nuclear Information System (INIS)

    Osadchy, A V; Obraztsova, E D; Volotovskiy, S G; Golovashkin, D L; Savin, V V

    2016-01-01

    In this paper we present the results of band structure computer simulation of GaSe- based nanostructures using the empirical pseudopotential method. Calculations were performed using a specially developed software that allows performing simulations using cluster computing. Application of this method significantly reduces the demands on computing resources compared to traditional approaches based on ab-initio techniques and provides receiving the adequate comparable results. The use of cluster computing allows to obtain information for structures that require an explicit account of a significant number of atoms, such as quantum dots and quantum pillars. (paper)

  3. Generalized linear models with random effects unified analysis via H-likelihood

    CERN Document Server

    Lee, Youngjo; Pawitan, Yudi

    2006-01-01

    Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors.Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives. Complementing theory with examples, many of...

  4. Empirical source strength correlations for rans-based acoustic analogy methods

    Science.gov (United States)

    Kube-McDowell, Matthew Tyndall

    JeNo is a jet noise prediction code based on an acoustic analogy method developed by Mani, Gliebe, Balsa, and Khavaran. Using the flow predictions from a standard Reynolds-averaged Navier-Stokes computational fluid dynamics solver, JeNo predicts the overall sound pressure level and angular spectra for high-speed hot jets over a range of observer angles, with a processing time suitable for rapid design purposes. JeNo models the noise from hot jets as a combination of two types of noise sources; quadrupole sources dependent on velocity fluctuations, which represent the major noise of turbulent mixing, and dipole sources dependent on enthalpy fluctuations, which represent the effects of thermal variation. These two sources are modeled by JeNo as propagating independently into the far-field, with no cross-correlation at the observer location. However, high-fidelity computational fluid dynamics solutions demonstrate that this assumption is false. In this thesis, the theory, assumptions, and limitations of the JeNo code are briefly discussed, and a modification to the acoustic analogy method is proposed in which the cross-correlation of the two primary noise sources is allowed to vary with the speed of the jet and the observer location. As a proof-of-concept implementation, an empirical correlation correction function is derived from comparisons between JeNo's noise predictions and a set of experimental measurements taken for the Air Force Aero-Propulsion Laboratory. The empirical correlation correction is then applied to JeNo's predictions of a separate data set of hot jets tested at NASA's Glenn Research Center. Metrics are derived to measure the qualitative and quantitative performance of JeNo's acoustic predictions, and the empirical correction is shown to provide a quantitative improvement in the noise prediction at low observer angles with no freestream flow, and a qualitative improvement in the presence of freestream flow. However, the results also demonstrate

  5. Empirical data and moral theory. A plea for integrated empirical ethics.

    Science.gov (United States)

    Molewijk, Bert; Stiggelbout, Anne M; Otten, Wilma; Dupuis, Heleen M; Kievit, Job

    2004-01-01

    Ethicists differ considerably in their reasons for using empirical data. This paper presents a brief overview of four traditional approaches to the use of empirical data: "the prescriptive applied ethicists," "the theorists," "the critical applied ethicists," and "the particularists." The main aim of this paper is to introduce a fifth approach of more recent date (i.e. "integrated empirical ethics") and to offer some methodological directives for research in integrated empirical ethics. All five approaches are presented in a table for heuristic purposes. The table consists of eight columns: "view on distinction descriptive-prescriptive sciences," "location of moral authority," "central goal(s)," "types of normativity," "use of empirical data," "method," "interaction empirical data and moral theory," and "cooperation with descriptive sciences." Ethicists can use the table in order to identify their own approach. Reflection on these issues prior to starting research in empirical ethics should lead to harmonization of the different scientific disciplines and effective planning of the final research design. Integrated empirical ethics (IEE) refers to studies in which ethicists and descriptive scientists cooperate together continuously and intensively. Both disciplines try to integrate moral theory and empirical data in order to reach a normative conclusion with respect to a specific social practice. IEE is not wholly prescriptive or wholly descriptive since IEE assumes an interdepence between facts and values and between the empirical and the normative. The paper ends with three suggestions for consideration on some of the future challenges of integrated empirical ethics.

  6. Coalescent-based species tree inference from gene tree topologies under incomplete lineage sorting by maximum likelihood.

    Science.gov (United States)

    Wu, Yufeng

    2012-03-01

    Incomplete lineage sorting can cause incongruence between the phylogenetic history of genes (the gene tree) and that of the species (the species tree), which can complicate the inference of phylogenies. In this article, I present a new coalescent-based algorithm for species tree inference with maximum likelihood. I first describe an improved method for computing the probability of a gene tree topology given a species tree, which is much faster than an existing algorithm by Degnan and Salter (2005). Based on this method, I develop a practical algorithm that takes a set of gene tree topologies and infers species trees with maximum likelihood. This algorithm searches for the best species tree by starting from initial species trees and performing heuristic search to obtain better trees with higher likelihood. This algorithm, called STELLS (which stands for Species Tree InfErence with Likelihood for Lineage Sorting), has been implemented in a program that is downloadable from the author's web page. The simulation results show that the STELLS algorithm is more accurate than an existing maximum likelihood method for many datasets, especially when there is noise in gene trees. I also show that the STELLS algorithm is efficient and can be applied to real biological datasets. © 2011 The Author. Evolution© 2011 The Society for the Study of Evolution.

  7. Robust fluence map optimization via alternating direction method of multipliers with empirical parameter optimization

    International Nuclear Information System (INIS)

    Gao, Hao

    2016-01-01

    For the treatment planning during intensity modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT), beam fluence maps can be first optimized via fluence map optimization (FMO) under the given dose prescriptions and constraints to conformally deliver the radiation dose to the targets while sparing the organs-at-risk, and then segmented into deliverable MLC apertures via leaf or arc sequencing algorithms. This work is to develop an efficient algorithm for FMO based on alternating direction method of multipliers (ADMM). Here we consider FMO with the least-square cost function and non-negative fluence constraints, and its solution algorithm is based on ADMM, which is efficient and simple-to-implement. In addition, an empirical method for optimizing the ADMM parameter is developed to improve the robustness of the ADMM algorithm. The ADMM based FMO solver was benchmarked with the quadratic programming method based on the interior-point (IP) method using the CORT dataset. The comparison results suggested the ADMM solver had a similar plan quality with slightly smaller total objective function value than IP. A simple-to-implement ADMM based FMO solver with empirical parameter optimization is proposed for IMRT or VMAT. (paper)

  8. Estimation of Model's Marginal likelihood Using Adaptive Sparse Grid Surrogates in Bayesian Model Averaging

    Science.gov (United States)

    Zeng, X.

    2015-12-01

    A large number of model executions are required to obtain alternative conceptual models' predictions and their posterior probabilities in Bayesian model averaging (BMA). The posterior model probability is estimated through models' marginal likelihood and prior probability. The heavy computation burden hinders the implementation of BMA prediction, especially for the elaborated marginal likelihood estimator. For overcoming the computation burden of BMA, an adaptive sparse grid (SG) stochastic collocation method is used to build surrogates for alternative conceptual models through the numerical experiment of a synthetical groundwater model. BMA predictions depend on model posterior weights (or marginal likelihoods), and this study also evaluated four marginal likelihood estimators, including arithmetic mean estimator (AME), harmonic mean estimator (HME), stabilized harmonic mean estimator (SHME), and thermodynamic integration estimator (TIE). The results demonstrate that TIE is accurate in estimating conceptual models' marginal likelihoods. The BMA-TIE has better predictive performance than other BMA predictions. TIE has high stability for estimating conceptual model's marginal likelihood. The repeated estimated conceptual model's marginal likelihoods by TIE have significant less variability than that estimated by other estimators. In addition, the SG surrogates are efficient to facilitate BMA predictions, especially for BMA-TIE. The number of model executions needed for building surrogates is 4.13%, 6.89%, 3.44%, and 0.43% of the required model executions of BMA-AME, BMA-HME, BMA-SHME, and BMA-TIE, respectively.

  9. Approximate likelihood approaches for detecting the influence of primordial gravitational waves in cosmic microwave background polarization

    Science.gov (United States)

    Pan, Zhen; Anderes, Ethan; Knox, Lloyd

    2018-05-01

    One of the major targets for next-generation cosmic microwave background (CMB) experiments is the detection of the primordial B-mode signal. Planning is under way for Stage-IV experiments that are projected to have instrumental noise small enough to make lensing and foregrounds the dominant source of uncertainty for estimating the tensor-to-scalar ratio r from polarization maps. This makes delensing a crucial part of future CMB polarization science. In this paper we present a likelihood method for estimating the tensor-to-scalar ratio r from CMB polarization observations, which combines the benefits of a full-scale likelihood approach with the tractability of the quadratic delensing technique. This method is a pixel space, all order likelihood analysis of the quadratic delensed B modes, and it essentially builds upon the quadratic delenser by taking into account all order lensing and pixel space anomalies. Its tractability relies on a crucial factorization of the pixel space covariance matrix of the polarization observations which allows one to compute the full Gaussian approximate likelihood profile, as a function of r , at the same computational cost of a single likelihood evaluation.

  10. Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology

    Science.gov (United States)

    Alsing, Justin; Wandelt, Benjamin; Feeney, Stephen

    2018-03-01

    Many statistical models in cosmology can be simulated forwards but have intractable likelihood functions. Likelihood-free inference methods allow us to perform Bayesian inference from these models using only forward simulations, free from any likelihood assumptions or approximations. Likelihood-free inference generically involves simulating mock data and comparing to the observed data; this comparison in data-space suffers from the curse of dimensionality and requires compression of the data to a small number of summary statistics to be tractable. In this paper we use massive asymptotically-optimal data compression to reduce the dimensionality of the data-space to just one number per parameter, providing a natural and optimal framework for summary statistic choice for likelihood-free inference. Secondly, we present the first cosmological application of Density Estimation Likelihood-Free Inference (DELFI), which learns a parameterized model for joint distribution of data and parameters, yielding both the parameter posterior and the model evidence. This approach is conceptually simple, requires less tuning than traditional Approximate Bayesian Computation approaches to likelihood-free inference and can give high-fidelity posteriors from orders of magnitude fewer forward simulations. As an additional bonus, it enables parameter inference and Bayesian model comparison simultaneously. We demonstrate Density Estimation Likelihood-Free Inference with massive data compression on an analysis of the joint light-curve analysis supernova data, as a simple validation case study. We show that high-fidelity posterior inference is possible for full-scale cosmological data analyses with as few as ˜104 simulations, with substantial scope for further improvement, demonstrating the scalability of likelihood-free inference to large and complex cosmological datasets.

  11. Empirical research in medical ethics: How conceptual accounts on normative-empirical collaboration may improve research practice

    Science.gov (United States)

    2012-01-01

    Background The methodology of medical ethics during the last few decades has shifted from a predominant use of normative-philosophical analyses to an increasing involvement of empirical methods. The articles which have been published in the course of this so-called 'empirical turn' can be divided into conceptual accounts of empirical-normative collaboration and studies which use socio-empirical methods to investigate ethically relevant issues in concrete social contexts. Discussion A considered reference to normative research questions can be expected from good quality empirical research in medical ethics. However, a significant proportion of empirical studies currently published in medical ethics lacks such linkage between the empirical research and the normative analysis. In the first part of this paper, we will outline two typical shortcomings of empirical studies in medical ethics with regard to a link between normative questions and empirical data: (1) The complete lack of normative analysis, and (2) cryptonormativity and a missing account with regard to the relationship between 'is' and 'ought' statements. Subsequently, two selected concepts of empirical-normative collaboration will be presented and how these concepts may contribute to improve the linkage between normative and empirical aspects of empirical research in medical ethics will be demonstrated. Based on our analysis, as well as our own practical experience with empirical research in medical ethics, we conclude with a sketch of concrete suggestions for the conduct of empirical research in medical ethics. Summary High quality empirical research in medical ethics is in need of a considered reference to normative analysis. In this paper, we demonstrate how conceptual approaches of empirical-normative collaboration can enhance empirical research in medical ethics with regard to the link between empirical research and normative analysis. PMID:22500496

  12. Expert elicitation on ultrafine particles: likelihood of health effects and causal pathways

    Directory of Open Access Journals (Sweden)

    Brunekreef Bert

    2009-07-01

    Full Text Available Abstract Background Exposure to fine ambient particulate matter (PM has consistently been associated with increased morbidity and mortality. The relationship between exposure to ultrafine particles (UFP and health effects is less firmly established. If UFP cause health effects independently from coarser fractions, this could affect health impact assessment of air pollution, which would possibly lead to alternative policy options to be considered to reduce the disease burden of PM. Therefore, we organized an expert elicitation workshop to assess the evidence for a causal relationship between exposure to UFP and health endpoints. Methods An expert elicitation on the health effects of ambient ultrafine particle exposure was carried out, focusing on: 1 the likelihood of causal relationships with key health endpoints, and 2 the likelihood of potential causal pathways for cardiac events. Based on a systematic peer-nomination procedure, fourteen European experts (epidemiologists, toxicologists and clinicians were selected, of whom twelve attended. They were provided with a briefing book containing key literature. After a group discussion, individual expert judgments in the form of ratings of the likelihood of causal relationships and pathways were obtained using a confidence scheme adapted from the one used by the Intergovernmental Panel on Climate Change. Results The likelihood of an independent causal relationship between increased short-term UFP exposure and increased all-cause mortality, hospital admissions for cardiovascular and respiratory diseases, aggravation of asthma symptoms and lung function decrements was rated medium to high by most experts. The likelihood for long-term UFP exposure to be causally related to all cause mortality, cardiovascular and respiratory morbidity and lung cancer was rated slightly lower, mostly medium. The experts rated the likelihood of each of the six identified possible causal pathways separately. Out of these

  13. Interface of the polarizable continuum model of solvation with semi-empirical methods in the GAMESS program

    DEFF Research Database (Denmark)

    Svendsen, Casper Steinmann; Blædel, Kristoffer; Christensen, Anders S

    2013-01-01

    An interface between semi-empirical methods and the polarized continuum model (PCM) of solvation successfully implemented into GAMESS following the approach by Chudinov et al (Chem. Phys. 1992, 160, 41). The interface includes energy gradients and is parallelized. For large molecules such as ubiq......An interface between semi-empirical methods and the polarized continuum model (PCM) of solvation successfully implemented into GAMESS following the approach by Chudinov et al (Chem. Phys. 1992, 160, 41). The interface includes energy gradients and is parallelized. For large molecules...... such as ubiquitin a reasonable speedup (up to a factor of six) is observed for up to 16 cores. The SCF convergence is greatly improved by PCM for proteins compared to the gas phase....

  14. An empirical method for approximating stream baseflow time series using groundwater table fluctuations

    Science.gov (United States)

    Meshgi, Ali; Schmitter, Petra; Babovic, Vladan; Chui, Ting Fong May

    2014-11-01

    Developing reliable methods to estimate stream baseflow has been a subject of interest due to its importance in catchment response and sustainable watershed management. However, to date, in the absence of complex numerical models, baseflow is most commonly estimated using statistically derived empirical approaches that do not directly incorporate physically-meaningful information. On the other hand, Artificial Intelligence (AI) tools such as Genetic Programming (GP) offer unique capabilities to reduce the complexities of hydrological systems without losing relevant physical information. This study presents a simple-to-use empirical equation to estimate baseflow time series using GP so that minimal data is required and physical information is preserved. A groundwater numerical model was first adopted to simulate baseflow for a small semi-urban catchment (0.043 km2) located in Singapore. GP was then used to derive an empirical equation relating baseflow time series to time series of groundwater table fluctuations, which are relatively easily measured and are physically related to baseflow generation. The equation was then generalized for approximating baseflow in other catchments and validated for a larger vegetation-dominated basin located in the US (24 km2). Overall, this study used GP to propose a simple-to-use equation to predict baseflow time series based on only three parameters: minimum daily baseflow of the entire period, area of the catchment and groundwater table fluctuations. It serves as an alternative approach for baseflow estimation in un-gauged systems when only groundwater table and soil information is available, and is thus complementary to other methods that require discharge measurements.

  15. Empirical Bayes ranking and selection methods via semiparametric hierarchical mixture models in microarray studies.

    Science.gov (United States)

    Noma, Hisashi; Matsui, Shigeyuki

    2013-05-20

    The main purpose of microarray studies is screening of differentially expressed genes as candidates for further investigation. Because of limited resources in this stage, prioritizing genes are relevant statistical tasks in microarray studies. For effective gene selections, parametric empirical Bayes methods for ranking and selection of genes with largest effect sizes have been proposed (Noma et al., 2010; Biostatistics 11: 281-289). The hierarchical mixture model incorporates the differential and non-differential components and allows information borrowing across differential genes with separation from nuisance, non-differential genes. In this article, we develop empirical Bayes ranking methods via a semiparametric hierarchical mixture model. A nonparametric prior distribution, rather than parametric prior distributions, for effect sizes is specified and estimated using the "smoothing by roughening" approach of Laird and Louis (1991; Computational statistics and data analysis 12: 27-37). We present applications to childhood and infant leukemia clinical studies with microarrays for exploring genes related to prognosis or disease progression. Copyright © 2012 John Wiley & Sons, Ltd.

  16. Prediction of shear wave velocity using empirical correlations and artificial intelligence methods

    Science.gov (United States)

    Maleki, Shahoo; Moradzadeh, Ali; Riabi, Reza Ghavami; Gholami, Raoof; Sadeghzadeh, Farhad

    2014-06-01

    Good understanding of mechanical properties of rock formations is essential during the development and production phases of a hydrocarbon reservoir. Conventionally, these properties are estimated from the petrophysical logs with compression and shear sonic data being the main input to the correlations. This is while in many cases the shear sonic data are not acquired during well logging, which may be for cost saving purposes. In this case, shear wave velocity is estimated using available empirical correlations or artificial intelligent methods proposed during the last few decades. In this paper, petrophysical logs corresponding to a well drilled in southern part of Iran were used to estimate the shear wave velocity using empirical correlations as well as two robust artificial intelligence methods knows as Support Vector Regression (SVR) and Back-Propagation Neural Network (BPNN). Although the results obtained by SVR seem to be reliable, the estimated values are not very precise and considering the importance of shear sonic data as the input into different models, this study suggests acquiring shear sonic data during well logging. It is important to note that the benefits of having reliable shear sonic data for estimation of rock formation mechanical properties will compensate the possible additional costs for acquiring a shear log.

  17. Prediction of shear wave velocity using empirical correlations and artificial intelligence methods

    Directory of Open Access Journals (Sweden)

    Shahoo Maleki

    2014-06-01

    Full Text Available Good understanding of mechanical properties of rock formations is essential during the development and production phases of a hydrocarbon reservoir. Conventionally, these properties are estimated from the petrophysical logs with compression and shear sonic data being the main input to the correlations. This is while in many cases the shear sonic data are not acquired during well logging, which may be for cost saving purposes. In this case, shear wave velocity is estimated using available empirical correlations or artificial intelligent methods proposed during the last few decades. In this paper, petrophysical logs corresponding to a well drilled in southern part of Iran were used to estimate the shear wave velocity using empirical correlations as well as two robust artificial intelligence methods knows as Support Vector Regression (SVR and Back-Propagation Neural Network (BPNN. Although the results obtained by SVR seem to be reliable, the estimated values are not very precise and considering the importance of shear sonic data as the input into different models, this study suggests acquiring shear sonic data during well logging. It is important to note that the benefits of having reliable shear sonic data for estimation of rock formation mechanical properties will compensate the possible additional costs for acquiring a shear log.

  18. Comparison of a semi-empirical method with some model codes for gamma-ray spectrum calculation

    Energy Technology Data Exchange (ETDEWEB)

    Sheng, Fan; Zhixiang, Zhao [Chinese Nuclear Data Center, Beijing, BJ (China)

    1996-06-01

    Gamma-ray spectra calculated by a semi-empirical method are compared with those calculated by the model codes such as GNASH, TNG, UNF and NDCP-1. The results of the calculations are discussed. (2 tabs., 3 figs.).

  19. PERFORMANCE ANALYSIS OF METHODS FOR ESTIMATING WEIBULL PARAMETERS FOR WIND SPEED DISTRIBUTION IN THE DISTRICT OF MAROUA

    Directory of Open Access Journals (Sweden)

    D. Kidmo Kaoga

    2015-07-01

    Full Text Available In this study, five numerical Weibull distribution methods, namely, the maximum likelihood method, the modified maximum likelihood method (MLM, the energy pattern factor method (EPF, the graphical method (GM, and the empirical method (EM were explored using hourly synoptic data collected from 1985 to 2013 in the district of Maroua in Cameroon. The performance analysis revealed that the MLM was the most accurate model followed by the EPF and the GM. Furthermore, the comparison between the wind speed standard deviation predicted by the proposed models and the measured data showed that the MLM has a smaller relative error of -3.33% on average compared to -11.67% on average for the EPF and -8.86% on average for the GM. As a result, the MLM was precisely recommended to estimate the scale and shape parameters for an accurate and efficient wind energy potential evaluation.

  20. PERFORMANCE ANALYSIS OF METHODS FOR ESTIMATING WEIBULL PARAMETERS FOR WIND SPEED DISTRIBUTION IN THE DISTRICT OF MAROUA

    Directory of Open Access Journals (Sweden)

    D. Kidmo Kaoga

    2014-12-01

    Full Text Available In this study, five numerical Weibull distribution methods, namely, the maximum likelihood method, the modified maximum likelihood method (MLM, the energy pattern factor method (EPF, the graphical method (GM, and the empirical method (EM were explored using hourly synoptic data collected from 1985 to 2013 in the district of Maroua in Cameroon. The performance analysis revealed that the MLM was the most accurate model followed by the EPF and the GM. Furthermore, the comparison between the wind speed standard deviation predicted by the proposed models and the measured data showed that the MLM has a smaller relative error of -3.33% on average compared to -11.67% on average for the EPF and -8.86% on average for the GM. As a result, the MLM was precisely recommended to estimate the scale and shape parameters for an accurate and efficient wind energy potential evaluation.

  1. Likelihood inference for unions of interacting discs

    DEFF Research Database (Denmark)

    Møller, Jesper; Helisová, Katarina

    To the best of our knowledge, this is the first paper which discusses likelihood inference or a random set using a germ-grain model, where the individual grains are unobservable edge effects occur, and other complications appear. We consider the case where the grains form a disc process modelled...... is specified with respect to a given marked Poisson model (i.e. a Boolean model). We show how edge effects and other complications can be handled by considering a certain conditional likelihood. Our methodology is illustrated by analyzing Peter Diggle's heather dataset, where we discuss the results...... of simulation-based maximum likelihood inference and the effect of specifying different reference Poisson models....

  2. Posterior distributions for likelihood ratios in forensic science.

    Science.gov (United States)

    van den Hout, Ardo; Alberink, Ivo

    2016-09-01

    Evaluation of evidence in forensic science is discussed using posterior distributions for likelihood ratios. Instead of eliminating the uncertainty by integrating (Bayes factor) or by conditioning on parameter values, uncertainty in the likelihood ratio is retained by parameter uncertainty derived from posterior distributions. A posterior distribution for a likelihood ratio can be summarised by the median and credible intervals. Using the posterior mean of the distribution is not recommended. An analysis of forensic data for body height estimation is undertaken. The posterior likelihood approach has been criticised both theoretically and with respect to applicability. This paper addresses the latter and illustrates an interesting application area. Copyright © 2016 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.

  3. Multiple Improvements of Multiple Imputation Likelihood Ratio Tests

    OpenAIRE

    Chan, Kin Wai; Meng, Xiao-Li

    2017-01-01

    Multiple imputation (MI) inference handles missing data by first properly imputing the missing values $m$ times, and then combining the $m$ analysis results from applying a complete-data procedure to each of the completed datasets. However, the existing method for combining likelihood ratio tests has multiple defects: (i) the combined test statistic can be negative in practice when the reference null distribution is a standard $F$ distribution; (ii) it is not invariant to re-parametrization; ...

  4. Penalized Maximum Likelihood Estimation for univariate normal mixture distributions

    International Nuclear Information System (INIS)

    Ridolfi, A.; Idier, J.

    2001-01-01

    Due to singularities of the likelihood function, the maximum likelihood approach for the estimation of the parameters of normal mixture models is an acknowledged ill posed optimization problem. Ill posedness is solved by penalizing the likelihood function. In the Bayesian framework, it amounts to incorporating an inverted gamma prior in the likelihood function. A penalized version of the EM algorithm is derived, which is still explicit and which intrinsically assures that the estimates are not singular. Numerical evidence of the latter property is put forward with a test

  5. Maximum-Likelihood Detection Of Noncoherent CPM

    Science.gov (United States)

    Divsalar, Dariush; Simon, Marvin K.

    1993-01-01

    Simplified detectors proposed for use in maximum-likelihood-sequence detection of symbols in alphabet of size M transmitted by uncoded, full-response continuous phase modulation over radio channel with additive white Gaussian noise. Structures of receivers derived from particular interpretation of maximum-likelihood metrics. Receivers include front ends, structures of which depends only on M, analogous to those in receivers of coherent CPM. Parts of receivers following front ends have structures, complexity of which would depend on N.

  6. A semi-empirical method for measuring thickness of pipe-wall using gamma scattering technique

    International Nuclear Information System (INIS)

    Vo Hoang Nguyen; Hua Tuyet Le; Le Dinh Minh Quan; Hoang Duc Tam; Le Bao Tran; Tran Thien Thanh; Tran Nguyen Thuy Ngan; Chau Van Tao; VNUHCM-University of Science, Ho Chi Minh City; Huynh Dinh Chuong

    2016-01-01

    In this work, we propose a semi-empirical method for determining the thickness of pipe-wall, of which the determination is performed by combining the experimental and Monte Carlo simulation data. The testing measurements show that this is an efficient method to measure the thickness of pipe-wall. In addition, this work also shows that it could use a NaI(Tl) scintillation detector and a low activity source to measure the thickness of pipe-wall, which is simple, quick and high accuracy method. (author)

  7. Empirical Evidence on Student-t Log-Returns of Diversified World Stock Indices

    OpenAIRE

    Eckhard Platen; Renata Rendek

    2007-01-01

    The aim of this paper is to document some empirical facts related to log-returns of diversified world stock indices when these are denominated in different currencies. Motivated by earlier results, we have obtained the estimated distribution of log-returns for a range of world stock indices over long observation periods. We expand previous studies by applying the maximum likelihood ratio test to the large class of generalized hyperbolic distributions, and investigate the log-returns of a vari...

  8. Semi-empirical Determination of Detection Efficiency for Voluminous Source by Effective Solid Angle Method

    Energy Technology Data Exchange (ETDEWEB)

    Kang, M. Y.; Kim, J. H.; Choi, H. D. [Seoul National Univ., Seoul (Korea, Republic of); Sun, G. M. [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

    2014-05-15

    In the field of γ-ray measurements, the determination of full energy (FE) absorption peak efficiency for a voluminous sample is difficult, because the preparation of the certified radiation source with the same chemical composition and geometry for the original voluminous sample is not easy. In order to solve this inconvenience, simulation or semi-empirical methods are preferred in many cases. Effective Solid Angle (ESA) Code which includes semi-empirical approach has been developed by the Applied Nuclear Physics Group in Seoul National University. In this study, we validated ESA code by using Marinelli type voluminous KRISS (Korea Research Institute of Standards and Science) CRM (Certified Reference Materials) sources and IAEA standard γ-ray point sources. And semi-empirically determined efficiency curve for voluminous source by using the ESA code is compared with the experimental value. We calculated the efficiency curve of voluminous source from the measured efficiency of standard point source by using the ESA code. We will carry out the ESA code validation by measurement of various CRM volume sources with detector of different efficiency.

  9. Likelihood analysis of parity violation in the compound nucleus

    International Nuclear Information System (INIS)

    Bowman, D.; Sharapov, E.

    1993-01-01

    We discuss the determination of the root mean-squared matrix element of the parity-violating interaction between compound-nuclear states using likelihood analysis. We briefly review the relevant features of the statistical model of the compound nucleus and the formalism of likelihood analysis. We then discuss the application of likelihood analysis to data on panty-violating longitudinal asymmetries. The reliability of the extracted value of the matrix element and errors assigned to the matrix element is stressed. We treat the situations where the spins of the p-wave resonances are not known and known using experimental data and Monte Carlo techniques. We conclude that likelihood analysis provides a reliable way to determine M and its confidence interval. We briefly discuss some problems associated with the normalization of the likelihood function

  10. MAXIMUM LIKELIHOOD CLASSIFICATION OF HIGH-RESOLUTION SAR IMAGES IN URBAN AREA

    Directory of Open Access Journals (Sweden)

    M. Soheili Majd

    2012-09-01

    Full Text Available In this work, we propose a state-of-the-art on statistical analysis of polarimetric synthetic aperture radar (SAR data, through the modeling of several indices. We concentrate on eight ground classes which have been carried out from amplitudes, co-polarisation ratio, depolarization ratios, and other polarimetric descriptors. To study their different statistical behaviours, we consider Gauss, log- normal, Beta I, Weibull, Gamma, and Fisher statistical models and estimate their parameters using three methods: method of moments (MoM, maximum-likelihood (ML methodology, and log-cumulants method (MoML. Then, we study the opportunity of introducing this information in an adapted supervised classification scheme based on Maximum–Likelihood and Fisher pdf. Our work relies on an image of a suburban area, acquired by the airborne RAMSES SAR sensor of ONERA. The results prove the potential of such data to discriminate urban surfaces and show the usefulness of adapting any classical classification algorithm however classification maps present a persistant class confusion between flat gravelled or concrete roofs and trees.

  11. Empirical philosophy of science

    DEFF Research Database (Denmark)

    Wagenknecht, Susann; Nersessian, Nancy J.; Andersen, Hanne

    2015-01-01

    A growing number of philosophers of science make use of qualitative empirical data, a development that may reconfigure the relations between philosophy and sociology of science and that is reminiscent of efforts to integrate history and philosophy of science. Therefore, the first part...... of this introduction to the volume Empirical Philosophy of Science outlines the history of relations between philosophy and sociology of science on the one hand, and philosophy and history of science on the other. The second part of this introduction offers an overview of the papers in the volume, each of which...... is giving its own answer to questions such as: Why does the use of qualitative empirical methods benefit philosophical accounts of science? And how should these methods be used by the philosopher?...

  12. Empirical method for simulation of water tables by digital computers

    International Nuclear Information System (INIS)

    Carnahan, C.L.; Fenske, P.R.

    1975-09-01

    An empirical method is described for computing a matrix of water-table elevations from a matrix of topographic elevations and a set of observed water-elevation control points which may be distributed randomly over the area of interest. The method is applicable to regions, such as the Great Basin, where the water table can be assumed to conform to a subdued image of overlying topography. A first approximation to the water table is computed by smoothing a matrix of topographic elevations and adjusting each node of the smoothed matrix according to a linear regression between observed water elevations and smoothed topographic elevations. Each observed control point is assumed to exert a radially decreasing influence on the first approximation surface. The first approximation is then adjusted further to conform to observed water-table elevations near control points. Outside the domain of control, the first approximation is assumed to represent the most probable configuration of the water table. The method has been applied to the Nevada Test Site and the Hot Creek Valley areas in Nevada

  13. Empirical Philosophy of Science

    DEFF Research Database (Denmark)

    Mansnerus, Erika; Wagenknecht, Susann

    2015-01-01

    knowledge takes place through the integration of the empirical or historical research into the philosophical studies, as Chang, Nersessian, Thagard and Schickore argue in their work. Building upon their contributions we will develop a blueprint for an Empirical Philosophy of Science that draws upon...... qualitative methods from the social sciences in order to advance our philosophical understanding of science in practice. We will regard the relationship between philosophical conceptualization and empirical data as an iterative dialogue between theory and data, which is guided by a particular ‘feeling with......Empirical insights are proven fruitful for the advancement of Philosophy of Science, but the integration of philosophical concepts and empirical data poses considerable methodological challenges. Debates in Integrated History and Philosophy of Science suggest that the advancement of philosophical...

  14. THESEUS: maximum likelihood superpositioning and analysis of macromolecular structures.

    Science.gov (United States)

    Theobald, Douglas L; Wuttke, Deborah S

    2006-09-01

    THESEUS is a command line program for performing maximum likelihood (ML) superpositions and analysis of macromolecular structures. While conventional superpositioning methods use ordinary least-squares (LS) as the optimization criterion, ML superpositions provide substantially improved accuracy by down-weighting variable structural regions and by correcting for correlations among atoms. ML superpositioning is robust and insensitive to the specific atoms included in the analysis, and thus it does not require subjective pruning of selected variable atomic coordinates. Output includes both likelihood-based and frequentist statistics for accurate evaluation of the adequacy of a superposition and for reliable analysis of structural similarities and differences. THESEUS performs principal components analysis for analyzing the complex correlations found among atoms within a structural ensemble. ANSI C source code and selected binaries for various computing platforms are available under the GNU open source license from http://monkshood.colorado.edu/theseus/ or http://www.theseus3d.org.

  15. LASER: A Maximum Likelihood Toolkit for Detecting Temporal Shifts in Diversification Rates From Molecular Phylogenies

    Directory of Open Access Journals (Sweden)

    Daniel L. Rabosky

    2006-01-01

    Full Text Available Rates of species origination and extinction can vary over time during evolutionary radiations, and it is possible to reconstruct the history of diversification using molecular phylogenies of extant taxa only. Maximum likelihood methods provide a useful framework for inferring temporal variation in diversification rates. LASER is a package for the R programming environment that implements maximum likelihood methods based on the birth-death process to test whether diversification rates have changed over time. LASER contrasts the likelihood of phylogenetic data under models where diversification rates have changed over time to alternative models where rates have remained constant over time. Major strengths of the package include the ability to detect temporal increases in diversification rates and the inference of diversification parameters under multiple rate-variable models of diversification. The program and associated documentation are freely available from the R package archive at http://cran.r-project.org.

  16. Critical factors in the empirical performance of temporal difference and evolutionary methods for reinforcement learning

    NARCIS (Netherlands)

    Whiteson, S.; Taylor, M.E.; Stone, P.

    2010-01-01

    Temporal difference and evolutionary methods are two of the most common approaches to solving reinforcement learning problems. However, there is little consensus on their relative merits and there have been few empirical studies that directly compare their performance. This article aims to address

  17. Statistical analysis of COMPTEL maximum likelihood-ratio distributions: evidence for a signal from previously undetected AGN

    International Nuclear Information System (INIS)

    Williams, O. R.; Bennett, K.; Much, R.; Schoenfelder, V.; Blom, J. J.; Ryan, J.

    1997-01-01

    The maximum likelihood-ratio method is frequently used in COMPTEL analysis to determine the significance of a point source at a given location. In this paper we do not consider whether the likelihood-ratio at a particular location indicates a detection, but rather whether distributions of likelihood-ratios derived from many locations depart from that expected for source free data. We have constructed distributions of likelihood-ratios by reading values from standard COMPTEL maximum-likelihood ratio maps at positions corresponding to the locations of different categories of AGN. Distributions derived from the locations of Seyfert galaxies are indistinguishable, according to a Kolmogorov-Smirnov test, from those obtained from ''random'' locations, but differ slightly from those obtained from the locations of flat spectrum radio loud quasars, OVVs, and BL Lac objects. This difference is not due to known COMPTEL sources, since regions near these sources are excluded from the analysis. We suggest that it might arise from a number of sources with fluxes below the COMPTEL detection threshold

  18. Maintaining symmetry of simulated likelihood functions

    DEFF Research Database (Denmark)

    Andersen, Laura Mørch

    This paper suggests solutions to two different types of simulation errors related to Quasi-Monte Carlo integration. Likelihood functions which depend on standard deviations of mixed parameters are symmetric in nature. This paper shows that antithetic draws preserve this symmetry and thereby...... improves precision substantially. Another source of error is that models testing away mixing dimensions must replicate the relevant dimensions of the quasi-random draws in the simulation of the restricted likelihood. These simulation errors are ignored in the standard estimation procedures used today...

  19. COSMIC MICROWAVE BACKGROUND LIKELIHOOD APPROXIMATION BY A GAUSSIANIZED BLACKWELL-RAO ESTIMATOR

    International Nuclear Information System (INIS)

    Rudjord, Oe.; Groeneboom, N. E.; Eriksen, H. K.; Huey, Greg; Gorski, K. M.; Jewell, J. B.

    2009-01-01

    We introduce a new cosmic microwave background (CMB) temperature likelihood approximation called the Gaussianized Blackwell-Rao estimator. This estimator is derived by transforming the observed marginal power spectrum distributions obtained by the CMB Gibbs sampler into standard univariate Gaussians, and then approximating their joint transformed distribution by a multivariate Gaussian. The method is exact for full-sky coverage and uniform noise and an excellent approximation for sky cuts and scanning patterns relevant for modern satellite experiments such as the Wilkinson Microwave Anisotropy Probe (WMAP) and Planck. The result is a stable, accurate, and computationally very efficient CMB temperature likelihood representation that allows the user to exploit the unique error propagation capabilities of the Gibbs sampler to high ls. A single evaluation of this estimator between l = 2 and 200 takes ∼0.2 CPU milliseconds, while for comparison, a singe pixel space likelihood evaluation between l = 2 and 30 for a map with ∼2500 pixels requires ∼20 s. We apply this tool to the five-year WMAP temperature data, and re-estimate the angular temperature power spectrum, C l , and likelihood, L(C l ), for l ≤ 200, and derive new cosmological parameters for the standard six-parameter ΛCDM model. Our spectrum is in excellent agreement with the official WMAP spectrum, but we find slight differences in the derived cosmological parameters. Most importantly, the spectral index of scalar perturbations is n s = 0.973 ± 0.014, 1.9σ away from unity and 0.6σ higher than the official WMAP result, n s = 0.965 ± 0.014. This suggests that an exact likelihood treatment is required to higher ls than previously believed, reinforcing and extending our conclusions from the three-year WMAP analysis. In that case, we found that the suboptimal likelihood approximation adopted between l = 12 and 30 by the WMAP team biased n s low by 0.4σ, while here we find that the same approximation

  20. Likelihood-Based Inference of B Cell Clonal Families.

    Directory of Open Access Journals (Sweden)

    Duncan K Ralph

    2016-10-01

    Full Text Available The human immune system depends on a highly diverse collection of antibody-making B cells. B cell receptor sequence diversity is generated by a random recombination process called "rearrangement" forming progenitor B cells, then a Darwinian process of lineage diversification and selection called "affinity maturation." The resulting receptors can be sequenced in high throughput for research and diagnostics. Such a collection of sequences contains a mixture of various lineages, each of which may be quite numerous, or may consist of only a single member. As a step to understanding the process and result of this diversification, one may wish to reconstruct lineage membership, i.e. to cluster sampled sequences according to which came from the same rearrangement events. We call this clustering problem "clonal family inference." In this paper we describe and validate a likelihood-based framework for clonal family inference based on a multi-hidden Markov Model (multi-HMM framework for B cell receptor sequences. We describe an agglomerative algorithm to find a maximum likelihood clustering, two approximate algorithms with various trade-offs of speed versus accuracy, and a third, fast algorithm for finding specific lineages. We show that under simulation these algorithms greatly improve upon existing clonal family inference methods, and that they also give significantly different clusters than previous methods when applied to two real data sets.

  1. Application of empirical mode decomposition method for characterization of random vibration signals

    Directory of Open Access Journals (Sweden)

    Setyamartana Parman

    2016-07-01

    Full Text Available Characterization of finite measured signals is a great of importance in dynamical modeling and system identification. This paper addresses an approach for characterization of measured random vibration signals where the approach rests on a method called empirical mode decomposition (EMD. The applicability of proposed approach is tested in one numerical and experimental data from a structural system, namely spar platform. The results are three main signal components, comprising: noise embedded in the measured signal as the first component, first intrinsic mode function (IMF called as the wave frequency response (WFR as the second component and second IMF called as the low frequency response (LFR as the third component while the residue is the trend. Band-pass filter (BPF method is taken as benchmark for the results obtained from EMD method.

  2. Measurement of the top quark mass with the dynamical likelihood method using lepton plus jets events with b-tags in p anti-p collisions at s**(1/2) = 1.96-TeV

    Energy Technology Data Exchange (ETDEWEB)

    Abulencia, A.; Acosta, D.; Adelman, Jahred A.; Affolder, Anthony A.; Akimoto, T.; Albrow, M.G.; Ambrose, D.; Amerio, S.; Amidei, D.; Anastassov, A.; Anikeev, K.; /Taiwan,

    2005-12-01

    This report describes a measurement of the top quark mass, M{sub top}, with the dynamical likelihood method (DLM) using the CDF II detector at the Fermilab Tevatron. The Tevatron produces top/anti-top (t{bar t}) pairs in p{bar p} collisions at a center-of-mass energy of 1.96 TeV. The data sample used in this analysis was accumulated from March 2002 through August 2004, which corresponds to an integrated luminosity of 318 pb{sup -1}. They use the t{bar t} candidates in the ''lepton+jets'' decay channel, requiring at least one jet identified as a b quark by finding an displaced secondary vertex. The DLM defines a likelihood for each event based on the differential cross section as a function of M{sub top} per unit phase space volume of the final partons, multiplied by the transfer functions from jet to parton energies. The method takes into account all possible jet combinations in an event, and the likelihood is multiplied event by event to derive the top quark mass by the maximum likelihood method. Using 63 t{bar t} candidates observed in the data, with 9.2 events expected from background, they measure the top quark mass to be 173.2{sub -2.4}{sup +2.6}(stat.) {+-} 3.2(syst.) GeV/c{sup 2}, or 173.2{sub -4.0}{sup +4.1} GeV/c{sup 2}.

  3. An empirical Bayes method for updating inferences in analysis of quantitative trait loci using information from related genome scans.

    Science.gov (United States)

    Zhang, Kui; Wiener, Howard; Beasley, Mark; George, Varghese; Amos, Christopher I; Allison, David B

    2006-08-01

    Individual genome scans for quantitative trait loci (QTL) mapping often suffer from low statistical power and imprecise estimates of QTL location and effect. This lack of precision yields large confidence intervals for QTL location, which are problematic for subsequent fine mapping and positional cloning. In prioritizing areas for follow-up after an initial genome scan and in evaluating the credibility of apparent linkage signals, investigators typically examine the results of other genome scans of the same phenotype and informally update their beliefs about which linkage signals in their scan most merit confidence and follow-up via a subjective-intuitive integration approach. A method that acknowledges the wisdom of this general paradigm but formally borrows information from other scans to increase confidence in objectivity would be a benefit. We developed an empirical Bayes analytic method to integrate information from multiple genome scans. The linkage statistic obtained from a single genome scan study is updated by incorporating statistics from other genome scans as prior information. This technique does not require that all studies have an identical marker map or a common estimated QTL effect. The updated linkage statistic can then be used for the estimation of QTL location and effect. We evaluate the performance of our method by using extensive simulations based on actual marker spacing and allele frequencies from available data. Results indicate that the empirical Bayes method can account for between-study heterogeneity, estimate the QTL location and effect more precisely, and provide narrower confidence intervals than results from any single individual study. We also compared the empirical Bayes method with a method originally developed for meta-analysis (a closely related but distinct purpose). In the face of marked heterogeneity among studies, the empirical Bayes method outperforms the comparator.

  4. Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

    Directory of Open Access Journals (Sweden)

    Yajie Zou

    2017-01-01

    Full Text Available Hotspot identification (HSID is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections similar to the target site from which safety performance functions (SPF used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

  5. Maximum likelihood estimation of ancestral codon usage bias parameters in Drosophila

    DEFF Research Database (Denmark)

    Nielsen, Rasmus; Bauer DuMont, Vanessa L; Hubisz, Melissa J

    2007-01-01

    : the selection coefficient for optimal codon usage (S), allowing joint maximum likelihood estimation of S and the dN/dS ratio. We apply the method to previously published data from Drosophila melanogaster, Drosophila simulans, and Drosophila yakuba and show, in accordance with previous results, that the D...

  6. EQPlanar: a maximum-likelihood method for accurate organ activity estimation from whole body planar projections

    International Nuclear Information System (INIS)

    Song, N; Frey, E C; He, B; Wahl, R L

    2011-01-01

    Optimizing targeted radionuclide therapy requires patient-specific estimation of organ doses. The organ doses are estimated from quantitative nuclear medicine imaging studies, many of which involve planar whole body scans. We have previously developed the quantitative planar (QPlanar) processing method and demonstrated its ability to provide more accurate activity estimates than conventional geometric-mean-based planar (CPlanar) processing methods using physical phantom and simulation studies. The QPlanar method uses the maximum likelihood-expectation maximization algorithm, 3D organ volume of interests (VOIs), and rigorous models of physical image degrading factors to estimate organ activities. However, the QPlanar method requires alignment between the 3D organ VOIs and the 2D planar projections and assumes uniform activity distribution in each VOI. This makes application to patients challenging. As a result, in this paper we propose an extended QPlanar (EQPlanar) method that provides independent-organ rigid registration and includes multiple background regions. We have validated this method using both Monte Carlo simulation and patient data. In the simulation study, we evaluated the precision and accuracy of the method in comparison to the original QPlanar method. For the patient studies, we compared organ activity estimates at 24 h after injection with those from conventional geometric mean-based planar quantification using a 24 h post-injection quantitative SPECT reconstruction as the gold standard. We also compared the goodness of fit of the measured and estimated projections obtained from the EQPlanar method to those from the original method at four other time points where gold standard data were not available. In the simulation study, more accurate activity estimates were provided by the EQPlanar method for all the organs at all the time points compared with the QPlanar method. Based on the patient data, we concluded that the EQPlanar method provided a

  7. On-line validation of linear process models using generalized likelihood ratios

    International Nuclear Information System (INIS)

    Tylee, J.L.

    1981-12-01

    A real-time method for testing the validity of linear models of nonlinear processes is described and evaluated. Using generalized likelihood ratios, the model dynamics are continually monitored to see if the process has moved far enough away from the nominal linear model operating point to justify generation of a new linear model. The method is demonstrated using a seventh-order model of a natural circulation steam generator

  8. A note on the relationships between multiple imputation, maximum likelihood and fully Bayesian methods for missing responses in linear regression models.

    Science.gov (United States)

    Chen, Qingxia; Ibrahim, Joseph G

    2014-07-01

    Multiple Imputation, Maximum Likelihood and Fully Bayesian methods are the three most commonly used model-based approaches in missing data problems. Although it is easy to show that when the responses are missing at random (MAR), the complete case analysis is unbiased and efficient, the aforementioned methods are still commonly used in practice for this setting. To examine the performance of and relationships between these three methods in this setting, we derive and investigate small sample and asymptotic expressions of the estimates and standard errors, and fully examine how these estimates are related for the three approaches in the linear regression model when the responses are MAR. We show that when the responses are MAR in the linear model, the estimates of the regression coefficients using these three methods are asymptotically equivalent to the complete case estimates under general conditions. One simulation and a real data set from a liver cancer clinical trial are given to compare the properties of these methods when the responses are MAR.

  9. Basics of Bayesian methods.

    Science.gov (United States)

    Ghosh, Sujit K

    2010-01-01

    Bayesian methods are rapidly becoming popular tools for making statistical inference in various fields of science including biology, engineering, finance, and genetics. One of the key aspects of Bayesian inferential method is its logical foundation that provides a coherent framework to utilize not only empirical but also scientific information available to a researcher. Prior knowledge arising from scientific background, expert judgment, or previously collected data is used to build a prior distribution which is then combined with current data via the likelihood function to characterize the current state of knowledge using the so-called posterior distribution. Bayesian methods allow the use of models of complex physical phenomena that were previously too difficult to estimate (e.g., using asymptotic approximations). Bayesian methods offer a means of more fully understanding issues that are central to many practical problems by allowing researchers to build integrated models based on hierarchical conditional distributions that can be estimated even with limited amounts of data. Furthermore, advances in numerical integration methods, particularly those based on Monte Carlo methods, have made it possible to compute the optimal Bayes estimators. However, there is a reasonably wide gap between the background of the empirically trained scientists and the full weight of Bayesian statistical inference. Hence, one of the goals of this chapter is to bridge the gap by offering elementary to advanced concepts that emphasize linkages between standard approaches and full probability modeling via Bayesian methods.

  10. The fine-tuning cost of the likelihood in SUSY models

    CERN Document Server

    Ghilencea, D M

    2013-01-01

    In SUSY models, the fine tuning of the electroweak (EW) scale with respect to their parameters gamma_i={m_0, m_{1/2}, mu_0, A_0, B_0,...} and the maximal likelihood L to fit the experimental data are usually regarded as two different problems. We show that, if one regards the EW minimum conditions as constraints that fix the EW scale, this commonly held view is not correct and that the likelihood contains all the information about fine-tuning. In this case we show that the corrected likelihood is equal to the ratio L/Delta of the usual likelihood L and the traditional fine tuning measure Delta of the EW scale. A similar result is obtained for the integrated likelihood over the set {gamma_i}, that can be written as a surface integral of the ratio L/Delta, with the surface in gamma_i space determined by the EW minimum constraints. As a result, a large likelihood actually demands a large ratio L/Delta or equivalently, a small chi^2_{new}=chi^2_{old}+2*ln(Delta). This shows the fine-tuning cost to the likelihood ...

  11. Maximum-likelihood estimation of the hyperbolic parameters from grouped observations

    DEFF Research Database (Denmark)

    Jensen, Jens Ledet

    1988-01-01

    a least-squares problem. The second procedure Hypesti first approaches the maximum-likelihood estimate by iterating in the profile-log likelihood function for the scale parameter. Close to the maximum of the likelihood function, the estimation is brought to an end by iteration, using all four parameters...

  12. Robust Biometric Score Fusion by Naive Likelihood Ratio via Receiver Operating Characteristics

    NARCIS (Netherlands)

    Tao, Q.; Veldhuis, Raymond N.J.

    This paper presents a novel method of fusing multiple biometrics on the matching score level. We estimate the likelihood ratios of the fused biometric scores, via individual receiver operating characteristics (ROC) which construct the Naive Bayes classifier. Using a limited number of operation

  13. Targeted maximum likelihood estimation for a binary treatment: A tutorial.

    Science.gov (United States)

    Luque-Fernandez, Miguel Angel; Schomaker, Michael; Rachet, Bernard; Schnitzer, Mireille E

    2018-04-23

    When estimating the average effect of a binary treatment (or exposure) on an outcome, methods that incorporate propensity scores, the G-formula, or targeted maximum likelihood estimation (TMLE) are preferred over naïve regression approaches, which are biased under misspecification of a parametric outcome model. In contrast propensity score methods require the correct specification of an exposure model. Double-robust methods only require correct specification of either the outcome or the exposure model. Targeted maximum likelihood estimation is a semiparametric double-robust method that improves the chances of correct model specification by allowing for flexible estimation using (nonparametric) machine-learning methods. It therefore requires weaker assumptions than its competitors. We provide a step-by-step guided implementation of TMLE and illustrate it in a realistic scenario based on cancer epidemiology where assumptions about correct model specification and positivity (ie, when a study participant had 0 probability of receiving the treatment) are nearly violated. This article provides a concise and reproducible educational introduction to TMLE for a binary outcome and exposure. The reader should gain sufficient understanding of TMLE from this introductory tutorial to be able to apply the method in practice. Extensive R-code is provided in easy-to-read boxes throughout the article for replicability. Stata users will find a testing implementation of TMLE and additional material in the Appendix S1 and at the following GitHub repository: https://github.com/migariane/SIM-TMLE-tutorial. © 2018 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

  14. Likelihood ratio sequential sampling models of recognition memory.

    Science.gov (United States)

    Osth, Adam F; Dennis, Simon; Heathcote, Andrew

    2017-02-01

    The mirror effect - a phenomenon whereby a manipulation produces opposite effects on hit and false alarm rates - is benchmark regularity of recognition memory. A likelihood ratio decision process, basing recognition on the relative likelihood that a stimulus is a target or a lure, naturally predicts the mirror effect, and so has been widely adopted in quantitative models of recognition memory. Glanzer, Hilford, and Maloney (2009) demonstrated that likelihood ratio models, assuming Gaussian memory strength, are also capable of explaining regularities observed in receiver-operating characteristics (ROCs), such as greater target than lure variance. Despite its central place in theorising about recognition memory, however, this class of models has not been tested using response time (RT) distributions. In this article, we develop a linear approximation to the likelihood ratio transformation, which we show predicts the same regularities as the exact transformation. This development enabled us to develop a tractable model of recognition-memory RT based on the diffusion decision model (DDM), with inputs (drift rates) provided by an approximate likelihood ratio transformation. We compared this "LR-DDM" to a standard DDM where all targets and lures receive their own drift rate parameters. Both were implemented as hierarchical Bayesian models and applied to four datasets. Model selection taking into account parsimony favored the LR-DDM, which requires fewer parameters than the standard DDM but still fits the data well. These results support log-likelihood based models as providing an elegant explanation of the regularities of recognition memory, not only in terms of choices made but also in terms of the times it takes to make them. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. Practical likelihood analysis for spatial generalized linear mixed models

    DEFF Research Database (Denmark)

    Bonat, W. H.; Ribeiro, Paulo Justiniano

    2016-01-01

    We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are......, respectively, examples of binomial and count datasets modeled by spatial generalized linear mixed models. Our results show that the Laplace approximation provides similar estimates to Markov Chain Monte Carlo likelihood, Monte Carlo expectation maximization, and modified Laplace approximation. Some advantages...... of Laplace approximation include the computation of the maximized log-likelihood value, which can be used for model selection and tests, and the possibility to obtain realistic confidence intervals for model parameters based on profile likelihoods. The Laplace approximation also avoids the tuning...

  16. High-order Composite Likelihood Inference for Max-Stable Distributions and Processes

    KAUST Repository

    Castruccio, Stefano; Huser, Raphaë l; Genton, Marc G.

    2015-01-01

    In multivariate or spatial extremes, inference for max-stable processes observed at a large collection of locations is a very challenging problem in computational statistics, and current approaches typically rely on less expensive composite likelihoods constructed from small subsets of data. In this work, we explore the limits of modern state-of-the-art computational facilities to perform full likelihood inference and to efficiently evaluate high-order composite likelihoods. With extensive simulations, we assess the loss of information of composite likelihood estimators with respect to a full likelihood approach for some widely-used multivariate or spatial extreme models, we discuss how to choose composite likelihood truncation to improve the efficiency, and we also provide recommendations for practitioners. This article has supplementary material online.

  17. High-order Composite Likelihood Inference for Max-Stable Distributions and Processes

    KAUST Repository

    Castruccio, Stefano

    2015-09-29

    In multivariate or spatial extremes, inference for max-stable processes observed at a large collection of locations is a very challenging problem in computational statistics, and current approaches typically rely on less expensive composite likelihoods constructed from small subsets of data. In this work, we explore the limits of modern state-of-the-art computational facilities to perform full likelihood inference and to efficiently evaluate high-order composite likelihoods. With extensive simulations, we assess the loss of information of composite likelihood estimators with respect to a full likelihood approach for some widely-used multivariate or spatial extreme models, we discuss how to choose composite likelihood truncation to improve the efficiency, and we also provide recommendations for practitioners. This article has supplementary material online.

  18. Y-STR frequency surveying method

    DEFF Research Database (Denmark)

    Willuweit, Sascha; Caliebe, Amke; Andersen, Mikkel Meyer

    2011-01-01

    Reasonable formalized methods to estimate the frequencies of DNA profiles generated from lineage markers have been proposed in the past years and were discussed in the forensic community. Recently, collections of population data on the frequencies of variations in Y chromosomal STR profiles have...... reached a new quality with the establishment of the comprehensive neatly quality-controlled reference database YHRD. Grounded on such unrivalled empirical material from hundreds of populations studies the core assumption of the Haplotype Frequency Surveying Method originally described 10 years ago can...... be tested and improved. Here we provide new approaches to calculate the parameters used in the frequency surveying method: a maximum likelihood estimation of the regression parameters (r1, r2, s1 and s2) and a revised Frequency Surveying framework with variable binning and a database preprocessing to take...

  19. Empirical properties of inter-cancellation durations in the Chinese stock market

    Directory of Open Access Journals (Sweden)

    Gao-Feng eGu

    2014-03-01

    Full Text Available Order cancellation process plays a crucial role in the dynamics of price formation in order-driven stock markets and is important in the construction and validation of computational finance models. Based on the order flow data of 23 liquid stocks traded on the Shenzhen Stock Exchange in 2003, we investigate the empirical statistical properties of inter-cancellation durations in units of events defined as the waiting times between two consecutive cancellations. The inter-cancellation durations for both buy and sell orders of all the stocks favor a $q$-exponential distribution when the maximum likelihood estimation method is adopted; In contrast, both cancelled buy orders of 9 stocks and cancelled sell orders of 4 stocks prefer Weibull distribution when the nonlinear least-square estimation is used. Applying detrended fluctuation analysis (DFA, centered detrending moving average (CDMA and multifractal detrended fluctuation analysis (MF-DFA methods, we unveil that the inter-cancellation duration time series process long memory and multifractal nature for both buy and sell cancellations of all the stocks. Our findings show that order cancellation processes exhibit long-range correlated bursty behaviors and are thus not Poissonian.

  20. Empirical pseudo-potential studies on electronic structure

    Indian Academy of Sciences (India)

    Theoretical investigations of electronic structure of quantum dots is of current interest in nanophase materials. Empirical theories such as effective mass approximation, tight binding methods and empirical pseudo-potential method are capable of explaining the experimentally observed optical properties. We employ the ...

  1. Sensitivity of ab Initio vs Empirical Methods in Computing Structural Effects on NMR Chemical Shifts for the Example of Peptides.

    Science.gov (United States)

    Sumowski, Chris Vanessa; Hanni, Matti; Schweizer, Sabine; Ochsenfeld, Christian

    2014-01-14

    The structural sensitivity of NMR chemical shifts as computed by quantum chemical methods is compared to a variety of empirical approaches for the example of a prototypical peptide, the 38-residue kaliotoxin KTX comprising 573 atoms. Despite the simplicity of empirical chemical shift prediction programs, the agreement with experimental results is rather good, underlining their usefulness. However, we show in our present work that they are highly insensitive to structural changes, which renders their use for validating predicted structures questionable. In contrast, quantum chemical methods show the expected high sensitivity to structural and electronic changes. This appears to be independent of the quantum chemical approach or the inclusion of solvent effects. For the latter, explicit solvent simulations with increasing number of snapshots were performed for two conformers of an eight amino acid sequence. In conclusion, the empirical approaches neither provide the expected magnitude nor the patterns of NMR chemical shifts determined by the clearly more costly ab initio methods upon structural changes. This restricts the use of empirical prediction programs in studies where peptide and protein structures are utilized for the NMR chemical shift evaluation such as in NMR refinement processes, structural model verifications, or calculations of NMR nuclear spin relaxation rates.

  2. Elaboration likelihood and the perceived value of labels

    DEFF Research Database (Denmark)

    Poulsen, Carsten Stig; Juhl, Hans Jørn

    2001-01-01

    In this paper the increasingly popular method of choice based on conjoint analysis is used and data are collected by pairwise comparisons. A latent class model is formulated allowing that the resulting data can be analyzed with segmentation in mind. The empirical study is on food labeling...

  3. Accelerated maximum likelihood parameter estimation for stochastic biochemical systems

    Directory of Open Access Journals (Sweden)

    Daigle Bernie J

    2012-05-01

    Full Text Available Abstract Background A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs. MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence. Results We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM2: an accelerated method for calculating MLEs that combines advances in rare event simulation with a computationally efficient version of the Monte Carlo expectation-maximization (MCEM algorithm. Our method requires no prior knowledge regarding parameter values, and it automatically provides a multivariate parameter uncertainty estimate. We applied the method to five stochastic systems of increasing complexity, progressing from an analytically tractable pure-birth model to a computationally demanding model of yeast-polarization. Our results demonstrate that MCEM2 substantially accelerates MLE computation on all tested models when compared to a stand-alone version of MCEM. Additionally, we show how our method identifies parameter values for certain classes of models more accurately than two recently proposed computationally efficient methods

  4. On the Reliability of Source Time Functions Estimated Using Empirical Green's Function Methods

    Science.gov (United States)

    Gallegos, A. C.; Xie, J.; Suarez Salas, L.

    2017-12-01

    The Empirical Green's Function (EGF) method (Hartzell, 1978) has been widely used to extract source time functions (STFs). In this method, seismograms generated by collocated events with different magnitudes are deconvolved. Under a fundamental assumption that the STF of the small event is a delta function, the deconvolved Relative Source Time Function (RSTF) yields the large event's STF. While this assumption can be empirically justified by examination of differences in event size and frequency content of the seismograms, there can be a lack of rigorous justification of the assumption. In practice, a small event might have a finite duration when the RSTF is retrieved and interpreted as the large event STF with a bias. In this study, we rigorously analyze this bias using synthetic waveforms generated by convolving a realistic Green's function waveform with pairs of finite-duration triangular or parabolic STFs. The RSTFs are found using a time-domain based matrix deconvolution. We find when the STFs of smaller events are finite, the RSTFs are a series of narrow non-physical spikes. Interpreting these RSTFs as a series of high-frequency source radiations would be very misleading. The only reliable and unambiguous information we can retrieve from these RSTFs is the difference in durations and the moment ratio of the two STFs. We can apply a Tikhonov smoothing to obtain a single-pulse RSTF, but its duration is dependent on the choice of weighting, which may be subjective. We then test the Multi-Channel Deconvolution (MCD) method (Plourde & Bostock, 2017) which assumes that both STFs have finite durations to be solved for. A concern about the MCD method is that the number of unknown parameters is larger, which would tend to make the problem rank-deficient. Because the kernel matrix is dependent on the STFs to be solved for under a positivity constraint, we can only estimate the rank-deficiency with a semi-empirical approach. Based on the results so far, we find that the

  5. Maximum likelihood positioning for gamma-ray imaging detectors with depth of interaction measurement

    International Nuclear Information System (INIS)

    Lerche, Ch.W.; Ros, A.; Monzo, J.M.; Aliaga, R.J.; Ferrando, N.; Martinez, J.D.; Herrero, V.; Esteve, R.; Gadea, R.; Colom, R.J.; Toledo, J.; Mateo, F.; Sebastia, A.; Sanchez, F.; Benlloch, J.M.

    2009-01-01

    The center of gravity algorithm leads to strong artifacts for gamma-ray imaging detectors that are based on monolithic scintillation crystals and position sensitive photo-detectors. This is a consequence of using the centroids as position estimates. The fact that charge division circuits can also be used to compute the standard deviation of the scintillation light distribution opens a way out of this drawback. We studied the feasibility of maximum likelihood estimation for computing the true gamma-ray photo-conversion position from the centroids and the standard deviation of the light distribution. The method was evaluated on a test detector that consists of the position sensitive photomultiplier tube H8500 and a monolithic LSO crystal (42mmx42mmx10mm). Spatial resolution was measured for the centroids and the maximum likelihood estimates. The results suggest that the maximum likelihood positioning is feasible and partially removes the strong artifacts of the center of gravity algorithm.

  6. Maximum likelihood positioning for gamma-ray imaging detectors with depth of interaction measurement

    Energy Technology Data Exchange (ETDEWEB)

    Lerche, Ch.W. [Grupo de Sistemas Digitales, ITACA, Universidad Politecnica de Valencia, 46022 Valencia (Spain)], E-mail: lerche@ific.uv.es; Ros, A. [Grupo de Fisica Medica Nuclear, IFIC, Universidad de Valencia-Consejo Superior de Investigaciones Cientificas, 46980 Paterna (Spain); Monzo, J.M.; Aliaga, R.J.; Ferrando, N.; Martinez, J.D.; Herrero, V.; Esteve, R.; Gadea, R.; Colom, R.J.; Toledo, J.; Mateo, F.; Sebastia, A. [Grupo de Sistemas Digitales, ITACA, Universidad Politecnica de Valencia, 46022 Valencia (Spain); Sanchez, F.; Benlloch, J.M. [Grupo de Fisica Medica Nuclear, IFIC, Universidad de Valencia-Consejo Superior de Investigaciones Cientificas, 46980 Paterna (Spain)

    2009-06-01

    The center of gravity algorithm leads to strong artifacts for gamma-ray imaging detectors that are based on monolithic scintillation crystals and position sensitive photo-detectors. This is a consequence of using the centroids as position estimates. The fact that charge division circuits can also be used to compute the standard deviation of the scintillation light distribution opens a way out of this drawback. We studied the feasibility of maximum likelihood estimation for computing the true gamma-ray photo-conversion position from the centroids and the standard deviation of the light distribution. The method was evaluated on a test detector that consists of the position sensitive photomultiplier tube H8500 and a monolithic LSO crystal (42mmx42mmx10mm). Spatial resolution was measured for the centroids and the maximum likelihood estimates. The results suggest that the maximum likelihood positioning is feasible and partially removes the strong artifacts of the center of gravity algorithm.

  7. Performance and separation occurrence of binary probit regression estimator using maximum likelihood method and Firths approach under different sample size

    Science.gov (United States)

    Lusiana, Evellin Dewi

    2017-12-01

    The parameters of binary probit regression model are commonly estimated by using Maximum Likelihood Estimation (MLE) method. However, MLE method has limitation if the binary data contains separation. Separation is the condition where there are one or several independent variables that exactly grouped the categories in binary response. It will result the estimators of MLE method become non-convergent, so that they cannot be used in modeling. One of the effort to resolve the separation is using Firths approach instead. This research has two aims. First, to identify the chance of separation occurrence in binary probit regression model between MLE method and Firths approach. Second, to compare the performance of binary probit regression model estimator that obtained by MLE method and Firths approach using RMSE criteria. Those are performed using simulation method and under different sample size. The results showed that the chance of separation occurrence in MLE method for small sample size is higher than Firths approach. On the other hand, for larger sample size, the probability decreased and relatively identic between MLE method and Firths approach. Meanwhile, Firths estimators have smaller RMSE than MLEs especially for smaller sample sizes. But for larger sample sizes, the RMSEs are not much different. It means that Firths estimators outperformed MLE estimator.

  8. Efficient estimators for likelihood ratio sensitivity indices of complex stochastic dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Arampatzis, Georgios; Katsoulakis, Markos A.; Rey-Bellet, Luc [Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts 01003 (United States)

    2016-03-14

    We demonstrate that centered likelihood ratio estimators for the sensitivity indices of complex stochastic dynamics are highly efficient with low, constant in time variance and consequently they are suitable for sensitivity analysis in long-time and steady-state regimes. These estimators rely on a new covariance formulation of the likelihood ratio that includes as a submatrix a Fisher information matrix for stochastic dynamics and can also be used for fast screening of insensitive parameters and parameter combinations. The proposed methods are applicable to broad classes of stochastic dynamics such as chemical reaction networks, Langevin-type equations and stochastic models in finance, including systems with a high dimensional parameter space and/or disparate decorrelation times between different observables. Furthermore, they are simple to implement as a standard observable in any existing simulation algorithm without additional modifications.

  9. Value and depreciation of mineral resources over the very long run: An empirical contrast of different methods

    OpenAIRE

    Rubio Varas, M. del Mar

    2005-01-01

    The paper contrasts empirically the results of alternative methods for estimating the value and the depreciation of mineral resources. The historical data of Mexico and Venezuela, covering the period 1920s-1980s, is used to contrast the results of several methods. These are the present value, the net price method, the user cost method and the imputed income method. The paper establishes that the net price and the user cost are not competing methods as such, but alternative adjustments to diff...

  10. Prediction of Physicochemical Properties of Organic Molecules Using Semi-Empirical Methods

    International Nuclear Information System (INIS)

    Kim, Chan Kyung; Kim, Chang Kon; Kim, Miri; Lee, Hai Whang; Cho, Soo Gyeong

    2013-01-01

    Prediction of physicochemical properties of organic molecules is an important process in chemistry and chemical engineering. The MSEP approach developed in our lab calculates the molecular surface electrostatic potential (ESP) on van der Waals (vdW) surfaces of molecules. This approach includes geometry optimization and frequency calculation using hybrid density functional theory, B3LYP, at the 6-31G(d) basis set to find minima on the potential energy surface, and is known to give satisfactory QSPR results for various properties of organic molecules. However, this MSEP method is not applicable to screen large database because geometry optimization and frequency calculation require considerable computing time. To develop a fast but yet reliable approach, we have re-examined our previous work on organic molecules using two semi-empirical methods, AM1 and PM3. This new approach can be an efficient protocol in designing new molecules with improved properties

  11. Inference for the Sharpe Ratio Using a Likelihood-Based Approach

    Directory of Open Access Journals (Sweden)

    Ying Liu

    2012-01-01

    Full Text Available The Sharpe ratio is the prominent risk-adjusted performance measure used by practitioners. Statistical testing of this ratio using its asymptotic distribution has lagged behind its use. In this paper, highly accurate likelihood analysis is applied for inference on the Sharpe ratio. Both the one- and two-sample problems are considered. The methodology has O(n−3/2 distributional accuracy and can be implemented using any parametric return distribution structure. Simulations are provided to demonstrate the method's superior accuracy over existing methods used for testing in the literature.

  12. Weighted profile likelihood-based confidence interval for the difference between two proportions with paired binomial data.

    Science.gov (United States)

    Pradhan, Vivek; Saha, Krishna K; Banerjee, Tathagata; Evans, John C

    2014-07-30

    Inference on the difference between two binomial proportions in the paired binomial setting is often an important problem in many biomedical investigations. Tang et al. (2010, Statistics in Medicine) discussed six methods to construct confidence intervals (henceforth, we abbreviate it as CI) for the difference between two proportions in paired binomial setting using method of variance estimates recovery. In this article, we propose weighted profile likelihood-based CIs for the difference between proportions of a paired binomial distribution. However, instead of the usual likelihood, we use weighted likelihood that is essentially making adjustments to the cell frequencies of a 2 × 2 table in the spirit of Agresti and Min (2005, Statistics in Medicine). We then conduct numerical studies to compare the performances of the proposed CIs with that of Tang et al. and Agresti and Min in terms of coverage probabilities and expected lengths. Our numerical study clearly indicates that the weighted profile likelihood-based intervals and Jeffreys interval (cf. Tang et al.) are superior in terms of achieving the nominal level, and in terms of expected lengths, they are competitive. Finally, we illustrate the use of the proposed CIs with real-life examples. Copyright © 2014 John Wiley & Sons, Ltd.

  13. Constructing valid density matrices on an NMR quantum information processor via maximum likelihood estimation

    Energy Technology Data Exchange (ETDEWEB)

    Singh, Harpreet; Arvind; Dorai, Kavita, E-mail: kavita@iisermohali.ac.in

    2016-09-07

    Estimation of quantum states is an important step in any quantum information processing experiment. A naive reconstruction of the density matrix from experimental measurements can often give density matrices which are not positive, and hence not physically acceptable. How do we ensure that at all stages of reconstruction, we keep the density matrix positive? Recently a method has been suggested based on maximum likelihood estimation, wherein the density matrix is guaranteed to be positive definite. We experimentally implement this protocol on an NMR quantum information processor. We discuss several examples and compare with the standard method of state estimation. - Highlights: • State estimation using maximum likelihood method was performed on an NMR quantum information processor. • Physically valid density matrices were obtained every time in contrast to standard quantum state tomography. • Density matrices of several different entangled and separable states were reconstructed for two and three qubits.

  14. Simulation-based marginal likelihood for cluster strong lensing cosmology

    Science.gov (United States)

    Killedar, M.; Borgani, S.; Fabjan, D.; Dolag, K.; Granato, G.; Meneghetti, M.; Planelles, S.; Ragone-Figueroa, C.

    2018-01-01

    Comparisons between observed and predicted strong lensing properties of galaxy clusters have been routinely used to claim either tension or consistency with Λ cold dark matter cosmology. However, standard approaches to such cosmological tests are unable to quantify the preference for one cosmology over another. We advocate approximating the relevant Bayes factor using a marginal likelihood that is based on the following summary statistic: the posterior probability distribution function for the parameters of the scaling relation between Einstein radii and cluster mass, α and β. We demonstrate, for the first time, a method of estimating the marginal likelihood using the X-ray selected z > 0.5 Massive Cluster Survey clusters as a case in point and employing both N-body and hydrodynamic simulations of clusters. We investigate the uncertainty in this estimate and consequential ability to compare competing cosmologies, which arises from incomplete descriptions of baryonic processes, discrepancies in cluster selection criteria, redshift distribution and dynamical state. The relation between triaxial cluster masses at various overdensities provides a promising alternative to the strong lensing test.

  15. Multiscale empirical interpolation for solving nonlinear PDEs

    KAUST Repository

    Calo, Victor M.

    2014-12-01

    In this paper, we propose a multiscale empirical interpolation method for solving nonlinear multiscale partial differential equations. The proposed method combines empirical interpolation techniques and local multiscale methods, such as the Generalized Multiscale Finite Element Method (GMsFEM). To solve nonlinear equations, the GMsFEM is used to represent the solution on a coarse grid with multiscale basis functions computed offline. Computing the GMsFEM solution involves calculating the system residuals and Jacobians on the fine grid. We use empirical interpolation concepts to evaluate these residuals and Jacobians of the multiscale system with a computational cost which is proportional to the size of the coarse-scale problem rather than the fully-resolved fine scale one. The empirical interpolation method uses basis functions which are built by sampling the nonlinear function we want to approximate a limited number of times. The coefficients needed for this approximation are computed in the offline stage by inverting an inexpensive linear system. The proposed multiscale empirical interpolation techniques: (1) divide computing the nonlinear function into coarse regions; (2) evaluate contributions of nonlinear functions in each coarse region taking advantage of a reduced-order representation of the solution; and (3) introduce multiscale proper-orthogonal-decomposition techniques to find appropriate interpolation vectors. We demonstrate the effectiveness of the proposed methods on several nonlinear multiscale PDEs that are solved with Newton\\'s methods and fully-implicit time marching schemes. Our numerical results show that the proposed methods provide a robust framework for solving nonlinear multiscale PDEs on a coarse grid with bounded error and significant computational cost reduction.

  16. The modified signed likelihood statistic and saddlepoint approximations

    DEFF Research Database (Denmark)

    Jensen, Jens Ledet

    1992-01-01

    SUMMARY: For a number of tests in exponential families we show that the use of a normal approximation to the modified signed likelihood ratio statistic r * is equivalent to the use of a saddlepoint approximation. This is also true in a large deviation region where the signed likelihood ratio...... statistic r is of order √ n. © 1992 Biometrika Trust....

  17. Empirical Music Aesthetics

    DEFF Research Database (Denmark)

    Grund, Cynthia M.

    The toolbox for empirically exploring the ways that artistic endeavors convey and activate meaning on the part of performers and audiences continues to expand. Current work employing methods at the intersection of performance studies, philosophy, motion capture and neuroscience to better understand...... musical performance and reception is inspired by traditional approaches within aesthetics, but it also challenges some of the presuppositions inherent in them. As an example of such work I present a research project in empirical music aesthetics begun last year and of which I am a team member....

  18. Planck intermediate results: XVI. Profile likelihoods for cosmological parameters

    DEFF Research Database (Denmark)

    Bartlett, J.G.; Cardoso, J.-F.; Delabrouille, J.

    2014-01-01

    We explore the 2013 Planck likelihood function with a high-precision multi-dimensional minimizer (Minuit). This allows a refinement of the CDM best-fit solution with respect to previously-released results, and the construction of frequentist confidence intervals using profile likelihoods. The agr...

  19. Planck 2013 results. XV. CMB power spectra and likelihood

    DEFF Research Database (Denmark)

    Tauber, Jan; Bartlett, J.G.; Bucher, M.

    2014-01-01

    This paper presents the Planck 2013 likelihood, a complete statistical description of the two-point correlation function of the CMB temperature fluctuations that accounts for all known relevant uncertainties, both instrumental and astrophysical in nature. We use this likelihood to derive our best...

  20. The behavior of the likelihood ratio test for testing missingness

    OpenAIRE

    Hens, Niel; Aerts, Marc; Molenberghs, Geert; Thijs, Herbert

    2003-01-01

    To asses the sensitivity of conclusions to model choices in the context of selection models for non-random dropout, one can oppose the different missing mechanisms to each other; e.g. by the likelihood ratio tests. The finite sample behavior of the null distribution and the power of the likelihood ratio test is studied under a variety of missingness mechanisms. missing data; sensitivity analysis; likelihood ratio test; missing mechanisms

  1. Ego involvement increases doping likelihood.

    Science.gov (United States)

    Ring, Christopher; Kavussanu, Maria

    2018-08-01

    Achievement goal theory provides a framework to help understand how individuals behave in achievement contexts, such as sport. Evidence concerning the role of motivation in the decision to use banned performance enhancing substances (i.e., doping) is equivocal on this issue. The extant literature shows that dispositional goal orientation has been weakly and inconsistently associated with doping intention and use. It is possible that goal involvement, which describes the situational motivational state, is a stronger determinant of doping intention. Accordingly, the current study used an experimental design to examine the effects of goal involvement, manipulated using direct instructions and reflective writing, on doping likelihood in hypothetical situations in college athletes. The ego-involving goal increased doping likelihood compared to no goal and a task-involving goal. The present findings provide the first evidence that ego involvement can sway the decision to use doping to improve athletic performance.

  2. Experimental demonstration of the maximum likelihood-based chromatic dispersion estimator for coherent receivers

    DEFF Research Database (Denmark)

    Borkowski, Robert; Johannisson, Pontus; Wymeersch, Henk

    2014-01-01

    We perform an experimental investigation of a maximum likelihood-based (ML-based) algorithm for bulk chromatic dispersion estimation for digital coherent receivers operating in uncompensated optical networks. We demonstrate the robustness of the method at low optical signal-to-noise ratio (OSNR...

  3. Maximum likelihood approach for several stochastic volatility models

    International Nuclear Information System (INIS)

    Camprodon, Jordi; Perelló, Josep

    2012-01-01

    Volatility measures the amplitude of price fluctuations. Despite it being one of the most important quantities in finance, volatility is not directly observable. Here we apply a maximum likelihood method which assumes that price and volatility follow a two-dimensional diffusion process where volatility is the stochastic diffusion coefficient of the log-price dynamics. We apply this method to the simplest versions of the expOU, the OU and the Heston stochastic volatility models and we study their performance in terms of the log-price probability, the volatility probability, and its Mean First-Passage Time. The approach has some predictive power on the future returns amplitude by only knowing the current volatility. The assumed models do not consider long-range volatility autocorrelation and the asymmetric return-volatility cross-correlation but the method still yields very naturally these two important stylized facts. We apply the method to different market indices and with a good performance in all cases. (paper)

  4. Evaluation of Smoking Prevention Television Messages Based on the Elaboration Likelihood Model

    Science.gov (United States)

    Flynn, Brian S.; Worden, John K.; Bunn, Janice Yanushka; Connolly, Scott W.; Dorwaldt, Anne L.

    2011-01-01

    Progress in reducing youth smoking may depend on developing improved methods to communicate with higher risk youth. This study explored the potential of smoking prevention messages based on the Elaboration Likelihood Model (ELM) to address these needs. Structured evaluations of 12 smoking prevention messages based on three strategies derived from…

  5. Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context.

    Science.gov (United States)

    Martinez, Josue G; Carroll, Raymond J; Müller, Samuel; Sampson, Joshua N; Chatterjee, Nilanjan

    2011-11-01

    When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso.

  6. Empirical Phenomenology: A Qualitative Research Approach (The ...

    African Journals Online (AJOL)

    Empirical Phenomenology: A Qualitative Research Approach (The Cologne Seminars) ... and practical application of empirical phenomenology in social research. ... and considers its implications for qualitative methods such as interviewing ...

  7. What 'empirical turn in bioethics'?

    Science.gov (United States)

    Hurst, Samia

    2010-10-01

    Uncertainty as to how we should articulate empirical data and normative reasoning seems to underlie most difficulties regarding the 'empirical turn' in bioethics. This article examines three different ways in which we could understand 'empirical turn'. Using real facts in normative reasoning is trivial and would not represent a 'turn'. Becoming an empirical discipline through a shift to the social and neurosciences would be a turn away from normative thinking, which we should not take. Conducting empirical research to inform normative reasoning is the usual meaning given to the term 'empirical turn'. In this sense, however, the turn is incomplete. Bioethics has imported methodological tools from empirical disciplines, but too often it has not imported the standards to which researchers in these disciplines are held. Integrating empirical and normative approaches also represents true added difficulties. Addressing these issues from the standpoint of debates on the fact-value distinction can cloud very real methodological concerns by displacing the debate to a level of abstraction where they need not be apparent. Ideally, empirical research in bioethics should meet standards for empirical and normative validity similar to those used in the source disciplines for these methods, and articulate these aspects clearly and appropriately. More modestly, criteria to ensure that none of these standards are completely left aside would improve the quality of empirical bioethics research and partly clear the air of critiques addressing its theoretical justification, when its rigour in the particularly difficult context of interdisciplinarity is what should be at stake.

  8. A maximum likelihood framework for protein design

    Directory of Open Access Journals (Sweden)

    Philippe Hervé

    2006-06-01

    Full Text Available Abstract Background The aim of protein design is to predict amino-acid sequences compatible with a given target structure. Traditionally envisioned as a purely thermodynamic question, this problem can also be understood in a wider context, where additional constraints are captured by learning the sequence patterns displayed by natural proteins of known conformation. In this latter perspective, however, we still need a theoretical formalization of the question, leading to general and efficient learning methods, and allowing for the selection of fast and accurate objective functions quantifying sequence/structure compatibility. Results We propose a formulation of the protein design problem in terms of model-based statistical inference. Our framework uses the maximum likelihood principle to optimize the unknown parameters of a statistical potential, which we call an inverse potential to contrast with classical potentials used for structure prediction. We propose an implementation based on Markov chain Monte Carlo, in which the likelihood is maximized by gradient descent and is numerically estimated by thermodynamic integration. The fit of the models is evaluated by cross-validation. We apply this to a simple pairwise contact potential, supplemented with a solvent-accessibility term, and show that the resulting models have a better predictive power than currently available pairwise potentials. Furthermore, the model comparison method presented here allows one to measure the relative contribution of each component of the potential, and to choose the optimal number of accessibility classes, which turns out to be much higher than classically considered. Conclusion Altogether, this reformulation makes it possible to test a wide diversity of models, using different forms of potentials, or accounting for other factors than just the constraint of thermodynamic stability. Ultimately, such model-based statistical analyses may help to understand the forces

  9. Likelihood-ratio-based biometric verification

    NARCIS (Netherlands)

    Bazen, A.M.; Veldhuis, Raymond N.J.

    2002-01-01

    This paper presents results on optimal similarity measures for biometric verification based on fixed-length feature vectors. First, we show that the verification of a single user is equivalent to the detection problem, which implies that for single-user verification the likelihood ratio is optimal.

  10. Likelihood Ratio-Based Biometric Verification

    NARCIS (Netherlands)

    Bazen, A.M.; Veldhuis, Raymond N.J.

    The paper presents results on optimal similarity measures for biometric verification based on fixed-length feature vectors. First, we show that the verification of a single user is equivalent to the detection problem, which implies that, for single-user verification, the likelihood ratio is optimal.

  11. Maximum Likelihood Reconstruction for Magnetic Resonance Fingerprinting.

    Science.gov (United States)

    Zhao, Bo; Setsompop, Kawin; Ye, Huihui; Cauley, Stephen F; Wald, Lawrence L

    2016-08-01

    This paper introduces a statistical estimation framework for magnetic resonance (MR) fingerprinting, a recently proposed quantitative imaging paradigm. Within this framework, we present a maximum likelihood (ML) formalism to estimate multiple MR tissue parameter maps directly from highly undersampled, noisy k-space data. A novel algorithm, based on variable splitting, the alternating direction method of multipliers, and the variable projection method, is developed to solve the resulting optimization problem. Representative results from both simulations and in vivo experiments demonstrate that the proposed approach yields significantly improved accuracy in parameter estimation, compared to the conventional MR fingerprinting reconstruction. Moreover, the proposed framework provides new theoretical insights into the conventional approach. We show analytically that the conventional approach is an approximation to the ML reconstruction; more precisely, it is exactly equivalent to the first iteration of the proposed algorithm for the ML reconstruction, provided that a gridding reconstruction is used as an initialization.

  12. Subtracting and Fitting Histograms using Profile Likelihood

    CERN Document Server

    D'Almeida, F M L

    2008-01-01

    It is known that many interesting signals expected at LHC are of unknown shape and strongly contaminated by background events. These signals will be dif cult to detect during the rst years of LHC operation due to the initial low luminosity. In this work, one presents a method of subtracting histograms based on the pro le likelihood function when the background is previously estimated by Monte Carlo events and one has low statistics. Estimators for the signal in each bin of the histogram difference are calculated so as limits for the signals with 68.3% of Con dence Level in a low statistics case when one has a exponential background and a Gaussian signal. The method can also be used to t histograms when the signal shape is known. Our results show a good performance and avoid the problem of negative values when subtracting histograms.

  13. Planck 2013 results. XV. CMB power spectra and likelihood

    CERN Document Server

    Ade, P.A.R.; Armitage-Caplan, C.; Arnaud, M.; Ashdown, M.; Atrio-Barandela, F.; Aumont, J.; Baccigalupi, C.; Banday, A.J.; Barreiro, R.B.; Bartlett, J.G.; Battaner, E.; Benabed, K.; Benoit, A.; Benoit-Levy, A.; Bernard, J.P.; Bersanelli, M.; Bielewicz, P.; Bobin, J.; Bock, J.J.; Bonaldi, A.; Bonavera, L.; Bond, J.R.; Borrill, J.; Bouchet, F.R.; Boulanger, F.; Bridges, M.; Bucher, M.; Burigana, C.; Butler, R.C.; Calabrese, E.; Cardoso, J.F.; Catalano, A.; Challinor, A.; Chamballu, A.; Chiang, L.Y.; Chiang, H.C.; Christensen, P.R.; Church, S.; Clements, D.L.; Colombi, S.; Colombo, L.P.L.; Combet, C.; Couchot, F.; Coulais, A.; Crill, B.P.; Curto, A.; Cuttaia, F.; Danese, L.; Davies, R.D.; Davis, R.J.; de Bernardis, P.; de Rosa, A.; de Zotti, G.; Delabrouille, J.; Delouis, J.M.; Desert, F.X.; Dickinson, C.; Diego, J.M.; Dole, H.; Donzelli, S.; Dore, O.; Douspis, M.; Dunkley, J.; Dupac, X.; Efstathiou, G.; Elsner, F.; Ensslin, T.A.; Eriksen, H.K.; Finelli, F.; Forni, O.; Frailis, M.; Fraisse, A.A.; Franceschi, E.; Gaier, T.C.; Galeotta, S.; Galli, S.; Ganga, K.; Giard, M.; Giardino, G.; Giraud-Heraud, Y.; Gjerlow, E.; Gonzalez-Nuevo, J.; Gorski, K.M.; Gratton, S.; Gregorio, A.; Gruppuso, A.; Gudmundsson, J.E.; Hansen, F.K.; Hanson, D.; Harrison, D.; Helou, G.; Henrot-Versille, S.; Hernandez-Monteagudo, C.; Herranz, D.; Hildebrandt, S.R.; Hivon, E.; Hobson, M.; Holmes, W.A.; Hornstrup, A.; Hovest, W.; Huffenberger, K.M.; Hurier, G.; Jaffe, T.R.; Jaffe, A.H.; Jewell, J.; Jones, W.C.; Juvela, M.; Keihanen, E.; Keskitalo, R.; Kiiveri, K.; Kisner, T.S.; Kneissl, R.; Knoche, J.; Knox, L.; Kunz, M.; Kurki-Suonio, H.; Lagache, G.; Lahteenmaki, A.; Lamarre, J.M.; Lasenby, A.; Lattanzi, M.; Laureijs, R.J.; Lawrence, C.R.; Le Jeune, M.; Leach, S.; Leahy, J.P.; Leonardi, R.; Leon-Tavares, J.; Lesgourgues, J.; Liguori, M.; Lilje, P.B.; Lindholm, V.; Linden-Vornle, M.; Lopez-Caniego, M.; Lubin, P.M.; Macias-Perez, J.F.; Maffei, B.; Maino, D.; Mandolesi, N.; Marinucci, D.; Maris, M.; Marshall, D.J.; Martin, P.G.; Martinez-Gonzalez, E.; Masi, S.; Matarrese, S.; Matthai, F.; Mazzotta, P.; Meinhold, P.R.; Melchiorri, A.; Mendes, L.; Menegoni, E.; Mennella, A.; Migliaccio, M.; Millea, M.; Mitra, S.; Miville-Deschenes, M.A.; Molinari, D.; Moneti, A.; Montier, L.; Morgante, G.; Mortlock, D.; Moss, A.; Munshi, D.; Naselsky, P.; Nati, F.; Natoli, P.; Netterfield, C.B.; Norgaard-Nielsen, H.U.; Noviello, F.; Novikov, D.; Novikov, I.; O'Dwyer, I.J.; Orieux, F.; Osborne, S.; Oxborrow, C.A.; Paci, F.; Pagano, L.; Pajot, F.; Paladini, R.; Paoletti, D.; Partridge, B.; Pasian, F.; Patanchon, G.; Paykari, P.; Perdereau, O.; Perotto, L.; Perrotta, F.; Piacentini, F.; Piat, M.; Pierpaoli, E.; Pietrobon, D.; Plaszczynski, S.; Pointecouteau, E.; Polenta, G.; Ponthieu, N.; Popa, L.; Poutanen, T.; Pratt, G.W.; Prezeau, G.; Prunet, S.; Puget, J.L.; Rachen, J.P.; Rahlin, A.; Rebolo, R.; Reinecke, M.; Remazeilles, M.; Renault, C.; Ricciardi, S.; Riller, T.; Ringeval, C.; Ristorcelli, I.; Rocha, G.; Rosset, C.; Roudier, G.; Rowan-Robinson, M.; Rubino-Martin, J.A.; Rusholme, B.; Sandri, M.; Sanselme, L.; Santos, D.; Savini, G.; Scott, D.; Seiffert, M.D.; Shellard, E.P.S.; Spencer, L.D.; Starck, J.L.; Stolyarov, V.; Stompor, R.; Sudiwala, R.; Sureau, F.; Sutton, D.; Suur-Uski, A.S.; Sygnet, J.F.; Tauber, J.A.; Tavagnacco, D.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Tucci, M.; Tuovinen, J.; Turler, M.; Valenziano, L.; Valiviita, J.; Van Tent, B.; Varis, J.; Vielva, P.; Villa, F.; Vittorio, N.; Wade, L.A.; Wandelt, B.D.; Wehus, I.K.; White, M.; White, S.D.M.; Yvon, D.; Zacchei, A.; Zonca, A.

    2014-01-01

    We present the Planck likelihood, a complete statistical description of the two-point correlation function of the CMB temperature fluctuations. We use this likelihood to derive the Planck CMB power spectrum over three decades in l, covering 2 = 50, we employ a correlated Gaussian likelihood approximation based on angular cross-spectra derived from the 100, 143 and 217 GHz channels. We validate our likelihood through an extensive suite of consistency tests, and assess the impact of residual foreground and instrumental uncertainties on cosmological parameters. We find good internal agreement among the high-l cross-spectra with residuals of a few uK^2 at l <= 1000. We compare our results with foreground-cleaned CMB maps, and with cross-spectra derived from the 70 GHz Planck map, and find broad agreement in terms of spectrum residuals and cosmological parameters. The best-fit LCDM cosmology is in excellent agreement with preliminary Planck polarisation spectra. The standard LCDM cosmology is well constrained b...

  14. Incorporating Nuisance Parameters in Likelihoods for Multisource Spectra

    CERN Document Server

    Conway, J.S.

    2011-01-01

    We describe here the general mathematical approach to constructing likelihoods for fitting observed spectra in one or more dimensions with multiple sources, including the effects of systematic uncertainties represented as nuisance parameters, when the likelihood is to be maximized with respect to these parameters. We consider three types of nuisance parameters: simple multiplicative factors, source spectra "morphing" parameters, and parameters representing statistical uncertainties in the predicted source spectra.

  15. Robust Likelihoods for Inflationary Gravitational Waves from Maps of Cosmic Microwave Background Polarization

    Science.gov (United States)

    Switzer, Eric Ryan; Watts, Duncan J.

    2016-01-01

    The B-mode polarization of the cosmic microwave background provides a unique window into tensor perturbations from inflationary gravitational waves. Survey effects complicate the estimation and description of the power spectrum on the largest angular scales. The pixel-space likelihood yields parameter distributions without the power spectrum as an intermediate step, but it does not have the large suite of tests available to power spectral methods. Searches for primordial B-modes must rigorously reject and rule out contamination. Many forms of contamination vary or are uncorrelated across epochs, frequencies, surveys, or other data treatment subsets. The cross power and the power spectrum of the difference of subset maps provide approaches to reject and isolate excess variance. We develop an analogous joint pixel-space likelihood. Contamination not modeled in the likelihood produces parameter-dependent bias and complicates the interpretation of the difference map. We describe a null test that consistently weights the difference map. Excess variance should either be explicitly modeled in the covariance or be removed through reprocessing the data.

  16. Maximum likelihood window for time delay estimation

    International Nuclear Information System (INIS)

    Lee, Young Sup; Yoon, Dong Jin; Kim, Chi Yup

    2004-01-01

    Time delay estimation for the detection of leak location in underground pipelines is critically important. Because the exact leak location depends upon the precision of the time delay between sensor signals due to leak noise and the speed of elastic waves, the research on the estimation of time delay has been one of the key issues in leak lovating with the time arrival difference method. In this study, an optimal Maximum Likelihood window is considered to obtain a better estimation of the time delay. This method has been proved in experiments, which can provide much clearer and more precise peaks in cross-correlation functions of leak signals. The leak location error has been less than 1 % of the distance between sensors, for example the error was not greater than 3 m for 300 m long underground pipelines. Apart from the experiment, an intensive theoretical analysis in terms of signal processing has been described. The improved leak locating with the suggested method is due to the windowing effect in frequency domain, which offers a weighting in significant frequencies.

  17. Semi-empirical spectrophotometric (SESp) method for the indirect determination of the ratio of cationic micellar binding constants of counterions X⁻ and Br⁻(K(X)/K(Br)).

    Science.gov (United States)

    Khan, Mohammad Niyaz; Yusof, Nor Saadah Mohd; Razak, Norazizah Abdul

    2013-01-01

    The semi-empirical spectrophotometric (SESp) method, for the indirect determination of ion exchange constants (K(X)(Br)) of ion exchange processes occurring between counterions (X⁻ and Br⁻) at the cationic micellar surface, is described in this article. The method uses an anionic spectrophotometric probe molecule, N-(2-methoxyphenyl)phthalamate ion (1⁻), which measures the effects of varying concentrations of inert inorganic or organic salt (Na(v)X, v = 1, 2) on absorbance, (A(ob)) at 310 nm, of samples containing constant concentrations of 1⁻, NaOH and cationic micelles. The observed data fit satisfactorily to an empirical equation which gives the values of two empirical constants. These empirical constants lead to the determination of K(X)(Br) (= K(X)/K(Br) with K(X) and K(Br) representing cationic micellar binding constants of counterions X and Br⁻). This method gives values of K(X)(Br) for both moderately hydrophobic and hydrophilic X⁻. The values of K(X)(Br), obtained by using this method, are comparable with the corresponding values of K(X)(Br), obtained by the use of semi-empirical kinetic (SEK) method, for different moderately hydrophobic X. The values of K(X)(Br) for X = Cl⁻ and 2,6-Cl₂C6H₃CO₂⁻, obtained by the use of SESp and SEK methods, are similar to those obtained by the use of other different conventional methods.

  18. Maximum Likelihood Dynamic Factor Modeling for Arbitrary "N" and "T" Using SEM

    Science.gov (United States)

    Voelkle, Manuel C.; Oud, Johan H. L.; von Oertzen, Timo; Lindenberger, Ulman

    2012-01-01

    This article has 3 objectives that build on each other. First, we demonstrate how to obtain maximum likelihood estimates for dynamic factor models (the direct autoregressive factor score model) with arbitrary "T" and "N" by means of structural equation modeling (SEM) and compare the approach to existing methods. Second, we go beyond standard time…

  19. X-ray spectrum analysis of multi-component samples by a method of fundamental parameters using empirical ratios

    International Nuclear Information System (INIS)

    Karmanov, V.I.

    1986-01-01

    A type of the fundamental parameter method based on empirical relation of corrections for absorption and additional-excitation with absorbing characteristics of samples is suggested. The method is used for X-ray fluorescence analysis of multi-component samples of charges of welded electrodes. It is shown that application of the method is justified only for determination of titanium, calcium and silicon content in charges taking into account only corrections for absorption. Irn and manganese content can be calculated by the simple method of the external standard

  20. SENSITIVITY ANALYSIS IN FLEXIBLE PAVEMENT PERFORMANCE USING MECHANISTIC EMPIRICAL METHOD (CASE STUDY: CIREBON–LOSARI ROAD SEGMENT, WEST JAVA

    Directory of Open Access Journals (Sweden)

    E. Samad

    2012-02-01

    Full Text Available Cirebon – Losari flexible pavement which is located on the North Coast of Java, Indonesia, is in the severe damage condition caused by overloading vehicles passing the road. The need for developing improved pavement design and analysis methods is very necessary. The increment of loads and quality of material properties can be evaluated through Mechanistic-Empirical (M-E method. M-E software like KENLAYER has been developed to facilitate the transition from empirical to mechanistic design methods. From the KENLAYER analysis, it can be concluded that the effect of overloading to the pavement structure performance is difficult to minimize even though the first two layers have relatively high modulus of elasticity. The occurrence of 150%, 200%, and 250% overloading have a very significant effect in reducing 84%, 95%, and 98% of the pavement design life, respectively. For the purpose of increasing the pavement service life, it is more effective to manage the allowable load.

  1. How rational should bioethics be? The value of empirical approaches.

    Science.gov (United States)

    Alvarez, A A

    2001-10-01

    Rational justification of claims with empirical content calls for empirical and not only normative philosophical investigation. Empirical approaches to bioethics are epistemically valuable, i.e., such methods may be necessary in providing and verifying basic knowledge about cultural values and norms. Our assumptions in moral reasoning can be verified or corrected using these methods. Moral arguments can be initiated or adjudicated by data drawn from empirical investigation. One may argue that individualistic informed consent, for example, is not compatible with the Asian communitarian orientation. But this normative claim uses an empirical assumption that may be contrary to the fact that some Asians do value and argue for informed consent. Is it necessary and factual to neatly characterize some cultures as individualistic and some as communitarian? Empirical investigation can provide a reasonable way to inform such generalizations. In a multi-cultural context, such as in the Philippines, there is a need to investigate the nature of the local ethos before making any appeal to authenticity. Otherwise we may succumb to the same ethical imperialism we are trying hard to resist. Normative claims that involve empirical premises cannot be reasonable verified or evaluated without utilizing empirical methods along with philosophical reflection. The integration of empirical methods to the standard normative approach to moral reasoning should be reasonably guided by the epistemic demands of claims arising from cross-cultural discourse in bioethics.

  2. A comparison of likelihood ratio tests and Rao's score test for three separable covariance matrix structures.

    Science.gov (United States)

    Filipiak, Katarzyna; Klein, Daniel; Roy, Anuradha

    2017-01-01

    The problem of testing the separability of a covariance matrix against an unstructured variance-covariance matrix is studied in the context of multivariate repeated measures data using Rao's score test (RST). The RST statistic is developed with the first component of the separable structure as a first-order autoregressive (AR(1)) correlation matrix or an unstructured (UN) covariance matrix under the assumption of multivariate normality. It is shown that the distribution of the RST statistic under the null hypothesis of any separability does not depend on the true values of the mean or the unstructured components of the separable structure. A significant advantage of the RST is that it can be performed for small samples, even smaller than the dimension of the data, where the likelihood ratio test (LRT) cannot be used, and it outperforms the standard LRT in a number of contexts. Monte Carlo simulations are then used to study the comparative behavior of the null distribution of the RST statistic, as well as that of the LRT statistic, in terms of sample size considerations, and for the estimation of the empirical percentiles. Our findings are compared with existing results where the first component of the separable structure is a compound symmetry (CS) correlation matrix. It is also shown by simulations that the empirical null distribution of the RST statistic converges faster than the empirical null distribution of the LRT statistic to the limiting χ 2 distribution. The tests are implemented on a real dataset from medical studies. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Diagonal Likelihood Ratio Test for Equality of Mean Vectors in High-Dimensional Data

    KAUST Repository

    Hu, Zongliang

    2017-10-27

    We propose a likelihood ratio test framework for testing normal mean vectors in high-dimensional data under two common scenarios: the one-sample test and the two-sample test with equal covariance matrices. We derive the test statistics under the assumption that the covariance matrices follow a diagonal matrix structure. In comparison with the diagonal Hotelling\\'s tests, our proposed test statistics display some interesting characteristics. In particular, they are a summation of the log-transformed squared t-statistics rather than a direct summation of those components. More importantly, to derive the asymptotic normality of our test statistics under the null and local alternative hypotheses, we do not require the assumption that the covariance matrix follows a diagonal matrix structure. As a consequence, our proposed test methods are very flexible and can be widely applied in practice. Finally, simulation studies and a real data analysis are also conducted to demonstrate the advantages of our likelihood ratio test method.

  4. Diagonal Likelihood Ratio Test for Equality of Mean Vectors in High-Dimensional Data

    KAUST Repository

    Hu, Zongliang; Tong, Tiejun; Genton, Marc G.

    2017-01-01

    We propose a likelihood ratio test framework for testing normal mean vectors in high-dimensional data under two common scenarios: the one-sample test and the two-sample test with equal covariance matrices. We derive the test statistics under the assumption that the covariance matrices follow a diagonal matrix structure. In comparison with the diagonal Hotelling's tests, our proposed test statistics display some interesting characteristics. In particular, they are a summation of the log-transformed squared t-statistics rather than a direct summation of those components. More importantly, to derive the asymptotic normality of our test statistics under the null and local alternative hypotheses, we do not require the assumption that the covariance matrix follows a diagonal matrix structure. As a consequence, our proposed test methods are very flexible and can be widely applied in practice. Finally, simulation studies and a real data analysis are also conducted to demonstrate the advantages of our likelihood ratio test method.

  5. Empirical method to calculate Clinch River Breeder Reactor (CRBR) inlet plenum transient temperatures

    International Nuclear Information System (INIS)

    Howarth, W.L.

    1976-01-01

    Sodium flow enters the CRBR inlet plenum via three loops or inlets. An empirical equation was developed to calculate transient temperatures in the CRBR inlet plenum from known loop flows and temperatures. The constants in the empirical equation were derived from 1/4 scale Inlet Plenum Model tests using water as the test fluid. The sodium temperature distribution was simulated by an electrolyte. Step electrolyte transients at 100 percent model flow were used to calculate the equation constants. Step electrolyte runs at 50 percent and 10 percent flow confirmed that the constants were independent of flow. Also, a transient was tested which varied simultaneously flow rate and electrolyte. Agreement of the test results with the empirical equation results was good which verifies the empirical equation

  6. Robust Multi-Frame Adaptive Optics Image Restoration Algorithm Using Maximum Likelihood Estimation with Poisson Statistics

    Directory of Open Access Journals (Sweden)

    Dongming Li

    2017-04-01

    Full Text Available An adaptive optics (AO system provides real-time compensation for atmospheric turbulence. However, an AO image is usually of poor contrast because of the nature of the imaging process, meaning that the image contains information coming from both out-of-focus and in-focus planes of the object, which also brings about a loss in quality. In this paper, we present a robust multi-frame adaptive optics image restoration algorithm via maximum likelihood estimation. Our proposed algorithm uses a maximum likelihood method with image regularization as the basic principle, and constructs the joint log likelihood function for multi-frame AO images based on a Poisson distribution model. To begin with, a frame selection method based on image variance is applied to the observed multi-frame AO images to select images with better quality to improve the convergence of a blind deconvolution algorithm. Then, by combining the imaging conditions and the AO system properties, a point spread function estimation model is built. Finally, we develop our iterative solutions for AO image restoration addressing the joint deconvolution issue. We conduct a number of experiments to evaluate the performances of our proposed algorithm. Experimental results show that our algorithm produces accurate AO image restoration results and outperforms the current state-of-the-art blind deconvolution methods.

  7. The likelihood principle and its proof – a never-ending story…

    DEFF Research Database (Denmark)

    Jørgensen, Thomas Martini

    2015-01-01

    An ongoing controversy in philosophy of statistics is the so-called “likelihood principle” essentially stating that all evidence which is obtained from an experiment about an unknown quantity θ is contained in the likelihood function of θ. Common classical statistical methodology, such as the use...... of significance tests, and confidence intervals, depends on the experimental procedure and unrealized events and thus violates the likelihood principle. The likelihood principle was identified by that name and proved in a famous paper by Allan Birnbaum in 1962. However, ever since both the principle itself...... as well as the proof has been highly debated. This presentation will illustrate the debate of both the principle and its proof, from 1962 and up to today. An often-used experiment to illustrate the controversy between classical interpretation and evidential confirmation based on the likelihood principle...

  8. The performance of selected semi-empirical and DFT methods in studying C60 fullerene derivatives

    Science.gov (United States)

    Sikorska, Celina; Puzyn, Tomasz

    2015-11-01

    The capability of reproducing the open circuit voltages (V oc) of 15 representative C60 fullerene derivatives was tested using the selected quantum mechanical methods (B3LYP, PM6, and PM7) together with the two one-electron basis sets. Certain theoretical treatments (e.g. PM6) were found to be satisfactory for preliminary estimates of the open circuit voltages (V oc), whereas the use of the B3LYP/6-31G(d) approach has been proven to assure highly accurate results. We also examined the structural similarity of 19 fullerene derivatives by employing principle component analysis (PCA). In order to express the structural features of the studied compounds we used molecular descriptors calculated with semi-empirical (PM6 and PM7) and density functional (B3LYP/6-31G(d)) methods separately. In performing PCA, we noticed that semi-empirical methods (i.e. PM6 and PM7) seem satisfactory for molecules, in which one can distinguish the aromatic and the aliphatic parts in the cyclopropane ring of PCBM (phenyl-C61-buteric acid methyl ester) and they significantly overestimate the energy of the highest occupied molecular orbital (E HOMO). The use of the B3LYP functional, however, is recommended for studying methanofullerenes, which closely resemble the structure of PCBM, and for their modifications.

  9. The performance of selected semi-empirical and DFT methods in studying C60 fullerene derivatives

    International Nuclear Information System (INIS)

    Sikorska, Celina; Puzyn, Tomasz

    2015-01-01

    The capability of reproducing the open circuit voltages (V oc ) of 15 representative C 60 fullerene derivatives was tested using the selected quantum mechanical methods (B3LYP, PM6, and PM7) together with the two one-electron basis sets. Certain theoretical treatments (e.g. PM6) were found to be satisfactory for preliminary estimates of the open circuit voltages (V oc ), whereas the use of the B3LYP/6-31G(d) approach has been proven to assure highly accurate results. We also examined the structural similarity of 19 fullerene derivatives by employing principle component analysis (PCA). In order to express the structural features of the studied compounds we used molecular descriptors calculated with semi-empirical (PM6 and PM7) and density functional (B3LYP/6-31G(d)) methods separately. In performing PCA, we noticed that semi-empirical methods (i.e. PM6 and PM7) seem satisfactory for molecules, in which one can distinguish the aromatic and the aliphatic parts in the cyclopropane ring of PCBM (phenyl-C 61 -buteric acid methyl ester) and they significantly overestimate the energy of the highest occupied molecular orbital (E HOMO ). The use of the B3LYP functional, however, is recommended for studying methanofullerenes, which closely resemble the structure of PCBM, and for their modifications. (paper)

  10. The performance of selected semi-empirical and DFT methods in studying C₆₀ fullerene derivatives.

    Science.gov (United States)

    Sikorska, Celina; Puzyn, Tomasz

    2015-11-13

    The capability of reproducing the open circuit voltages (V(oc)) of 15 representative C60 fullerene derivatives was tested using the selected quantum mechanical methods (B3LYP, PM6, and PM7) together with the two one-electron basis sets. Certain theoretical treatments (e.g. PM6) were found to be satisfactory for preliminary estimates of the open circuit voltages (V(oc)), whereas the use of the B3LYP/6-31G(d) approach has been proven to assure highly accurate results. We also examined the structural similarity of 19 fullerene derivatives by employing principle component analysis (PCA). In order to express the structural features of the studied compounds we used molecular descriptors calculated with semi-empirical (PM6 and PM7) and density functional (B3LYP/6-31G(d)) methods separately. In performing PCA, we noticed that semi-empirical methods (i.e. PM6 and PM7) seem satisfactory for molecules, in which one can distinguish the aromatic and the aliphatic parts in the cyclopropane ring of PCBM (phenyl-C61-buteric acid methyl ester) and they significantly overestimate the energy of the highest occupied molecular orbital (E(HOMO)). The use of the B3LYP functional, however, is recommended for studying methanofullerenes, which closely resemble the structure of PCBM, and for their modifications.

  11. On the performance of social network and likelihood-based expert weighting schemes

    International Nuclear Information System (INIS)

    Cooke, Roger M.; ElSaadany, Susie; Huang Xinzheng

    2008-01-01

    Using expert judgment data from the TU Delft's expert judgment database, we compare the performance of different weighting schemes, namely equal weighting, performance-based weighting from the classical model [Cooke RM. Experts in uncertainty. Oxford: Oxford University Press; 1991.], social network (SN) weighting and likelihood weighting. The picture that emerges with regard to SN weights is rather mixed. SN theory does not provide an alternative to performance-based combination of expert judgments, since the statistical accuracy of the SN decision maker is sometimes unacceptably low. On the other hand, it does outperform equal weighting in the majority of cases. The results here, though not overwhelmingly positive, do nonetheless motivate further research into social interaction methods for nominating and weighting experts. Indeed, a full expert judgment study with performance measurement requires an investment in time and effort, with a view to securing external validation. If high confidence in a comparable level of validation can be obtained by less intensive methods, this would be very welcome, and would facilitate the application of structured expert judgment in situations where the resources for a full study are not available. Likelihood weights are just as resource intensive as performance-based weights, and the evidence presented here suggests that they are inferior to performance-based weights with regard to those scoring variables which are optimized in performance weights (calibration and information). Perhaps surprisingly, they are also inferior with regard to likelihood. Their use is further discouraged by the fact that they constitute a strongly improper scoring rule

  12. Effect of Box-Cox transformation on power of Haseman-Elston and maximum-likelihood variance components tests to detect quantitative trait Loci.

    Science.gov (United States)

    Etzel, C J; Shete, S; Beasley, T M; Fernandez, J R; Allison, D B; Amos, C I

    2003-01-01

    Non-normality of the phenotypic distribution can affect power to detect quantitative trait loci in sib pair studies. Previously, we observed that Winsorizing the sib pair phenotypes increased the power of quantitative trait locus (QTL) detection for both Haseman-Elston (HE) least-squares tests [Hum Hered 2002;53:59-67] and maximum likelihood-based variance components (MLVC) analysis [Behav Genet (in press)]. Winsorizing the phenotypes led to a slight increase in type 1 error in H-E tests and a slight decrease in type I error for MLVC analysis. Herein, we considered transforming the sib pair phenotypes using the Box-Cox family of transformations. Data were simulated for normal and non-normal (skewed and kurtic) distributions. Phenotypic values were replaced by Box-Cox transformed values. Twenty thousand replications were performed for three H-E tests of linkage and the likelihood ratio test (LRT), the Wald test and other robust versions based on the MLVC method. We calculated the relative nominal inflation rate as the ratio of observed empirical type 1 error divided by the set alpha level (5, 1 and 0.1% alpha levels). MLVC tests applied to non-normal data had inflated type I errors (rate ratio greater than 1.0), which were controlled best by Box-Cox transformation and to a lesser degree by Winsorizing. For example, for non-transformed, skewed phenotypes (derived from a chi2 distribution with 2 degrees of freedom), the rates of empirical type 1 error with respect to set alpha level=0.01 were 0.80, 4.35 and 7.33 for the original H-E test, LRT and Wald test, respectively. For the same alpha level=0.01, these rates were 1.12, 3.095 and 4.088 after Winsorizing and 0.723, 1.195 and 1.905 after Box-Cox transformation. Winsorizing reduced inflated error rates for the leptokurtic distribution (derived from a Laplace distribution with mean 0 and variance 8). Further, power (adjusted for empirical type 1 error) at the 0.01 alpha level ranged from 4.7 to 17.3% across all tests

  13. Microarray background correction: maximum likelihood estimation for the normal-exponential convolution

    DEFF Research Database (Denmark)

    Silver, Jeremy D; Ritchie, Matthew E; Smyth, Gordon K

    2009-01-01

    exponentially distributed, representing background noise and signal, respectively. Using a saddle-point approximation, Ritchie and others (2007) found normexp to be the best background correction method for 2-color microarray data. This article develops the normexp method further by improving the estimation...... is developed for exact maximum likelihood estimation (MLE) using high-quality optimization software and using the saddle-point estimates as starting values. "MLE" is shown to outperform heuristic estimators proposed by other authors, both in terms of estimation accuracy and in terms of performance on real data...

  14. L.U.St: a tool for approximated maximum likelihood supertree reconstruction.

    Science.gov (United States)

    Akanni, Wasiu A; Creevey, Christopher J; Wilkinson, Mark; Pisani, Davide

    2014-06-12

    Supertrees combine disparate, partially overlapping trees to generate a synthesis that provides a high level perspective that cannot be attained from the inspection of individual phylogenies. Supertrees can be seen as meta-analytical tools that can be used to make inferences based on results of previous scientific studies. Their meta-analytical application has increased in popularity since it was realised that the power of statistical tests for the study of evolutionary trends critically depends on the use of taxon-dense phylogenies. Further to that, supertrees have found applications in phylogenomics where they are used to combine gene trees and recover species phylogenies based on genome-scale data sets. Here, we present the L.U.St package, a python tool for approximate maximum likelihood supertree inference and illustrate its application using a genomic data set for the placental mammals. L.U.St allows the calculation of the approximate likelihood of a supertree, given a set of input trees, performs heuristic searches to look for the supertree of highest likelihood, and performs statistical tests of two or more supertrees. To this end, L.U.St implements a winning sites test allowing ranking of a collection of a-priori selected hypotheses, given as a collection of input supertree topologies. It also outputs a file of input-tree-wise likelihood scores that can be used as input to CONSEL for calculation of standard tests of two trees (e.g. Kishino-Hasegawa, Shimidoara-Hasegawa and Approximately Unbiased tests). This is the first fully parametric implementation of a supertree method, it has clearly understood properties, and provides several advantages over currently available supertree approaches. It is easy to implement and works on any platform that has python installed. bitBucket page - https://afro-juju@bitbucket.org/afro-juju/l.u.st.git. Davide.Pisani@bristol.ac.uk.

  15. Performance of penalized maximum likelihood in estimation of genetic covariances matrices

    Directory of Open Access Journals (Sweden)

    Meyer Karin

    2011-11-01

    Full Text Available Abstract Background Estimation of genetic covariance matrices for multivariate problems comprising more than a few traits is inherently problematic, since sampling variation increases dramatically with the number of traits. This paper investigates the efficacy of regularized estimation of covariance components in a maximum likelihood framework, imposing a penalty on the likelihood designed to reduce sampling variation. In particular, penalties that "borrow strength" from the phenotypic covariance matrix are considered. Methods An extensive simulation study was carried out to investigate the reduction in average 'loss', i.e. the deviation in estimated matrices from the population values, and the accompanying bias for a range of parameter values and sample sizes. A number of penalties are examined, penalizing either the canonical eigenvalues or the genetic covariance or correlation matrices. In addition, several strategies to determine the amount of penalization to be applied, i.e. to estimate the appropriate tuning factor, are explored. Results It is shown that substantial reductions in loss for estimates of genetic covariance can be achieved for small to moderate sample sizes. While no penalty performed best overall, penalizing the variance among the estimated canonical eigenvalues on the logarithmic scale or shrinking the genetic towards the phenotypic correlation matrix appeared most advantageous. Estimating the tuning factor using cross-validation resulted in a loss reduction 10 to 15% less than that obtained if population values were known. Applying a mild penalty, chosen so that the deviation in likelihood from the maximum was non-significant, performed as well if not better than cross-validation and can be recommended as a pragmatic strategy. Conclusions Penalized maximum likelihood estimation provides the means to 'make the most' of limited and precious data and facilitates more stable estimation for multi-dimensional analyses. It should

  16. Employing a Monte Carlo algorithm in Newton-type methods for restricted maximum likelihood estimation of genetic parameters.

    Directory of Open Access Journals (Sweden)

    Kaarina Matilainen

    Full Text Available Estimation of variance components by Monte Carlo (MC expectation maximization (EM restricted maximum likelihood (REML is computationally efficient for large data sets and complex linear mixed effects models. However, efficiency may be lost due to the need for a large number of iterations of the EM algorithm. To decrease the computing time we explored the use of faster converging Newton-type algorithms within MC REML implementations. The implemented algorithms were: MC Newton-Raphson (NR, where the information matrix was generated via sampling; MC average information(AI, where the information was computed as an average of observed and expected information; and MC Broyden's method, where the zero of the gradient was searched using a quasi-Newton-type algorithm. Performance of these algorithms was evaluated using simulated data. The final estimates were in good agreement with corresponding analytical ones. MC NR REML and MC AI REML enhanced convergence compared to MC EM REML and gave standard errors for the estimates as a by-product. MC NR REML required a larger number of MC samples, while each MC AI REML iteration demanded extra solving of mixed model equations by the number of parameters to be estimated. MC Broyden's method required the largest number of MC samples with our small data and did not give standard errors for the parameters directly. We studied the performance of three different convergence criteria for the MC AI REML algorithm. Our results indicate the importance of defining a suitable convergence criterion and critical value in order to obtain an efficient Newton-type method utilizing a MC algorithm. Overall, use of a MC algorithm with Newton-type methods proved feasible and the results encourage testing of these methods with different kinds of large-scale problem settings.

  17. A simulation study of likelihood inference procedures in rayleigh distribution with censored data

    International Nuclear Information System (INIS)

    Baklizi, S. A.; Baker, H. M.

    2001-01-01

    Inference procedures based on the likelihood function are considered for the one parameter Rayleigh distribution with type1 and type 2 censored data. Using simulation techniques, the finite sample performances of the maximum likelihood estimator and the large sample likelihood interval estimation procedures based on the Wald, the Rao, and the likelihood ratio statistics are investigated. It appears that the maximum likelihood estimator is unbiased. The approximate variance estimates obtained from the asymptotic normal distribution of the maximum likelihood estimator are accurate under type 2 censored data while they tend to be smaller than the actual variances when considering type1 censored data of small size. It appears also that interval estimation based on the Wald and Rao statistics need much more sample size than interval estimation based on the likelihood ratio statistic to attain reasonable accuracy. (authors). 15 refs., 4 tabs

  18. Multi-level restricted maximum likelihood covariance estimation and kriging for large non-gridded spatial datasets

    KAUST Repository

    Castrillon, Julio; Genton, Marc G.; Yokota, Rio

    2015-01-01

    We develop a multi-level restricted Gaussian maximum likelihood method for estimating the covariance function parameters and computing the best unbiased predictor. Our approach produces a new set of multi-level contrasts where the deterministic

  19. A New Statistical Method to Determine the Degree of Validity of Health Economic Model Outcomes against Empirical Data.

    Science.gov (United States)

    Corro Ramos, Isaac; van Voorn, George A K; Vemer, Pepijn; Feenstra, Talitha L; Al, Maiwenn J

    2017-09-01

    The validation of health economic (HE) model outcomes against empirical data is of key importance. Although statistical testing seems applicable, guidelines for the validation of HE models lack guidance on statistical validation, and actual validation efforts often present subjective judgment of graphs and point estimates. To discuss the applicability of existing validation techniques and to present a new method for quantifying the degrees of validity statistically, which is useful for decision makers. A new Bayesian method is proposed to determine how well HE model outcomes compare with empirical data. Validity is based on a pre-established accuracy interval in which the model outcomes should fall. The method uses the outcomes of a probabilistic sensitivity analysis and results in a posterior distribution around the probability that HE model outcomes can be regarded as valid. We use a published diabetes model (Modelling Integrated Care for Diabetes based on Observational data) to validate the outcome "number of patients who are on dialysis or with end-stage renal disease." Results indicate that a high probability of a valid outcome is associated with relatively wide accuracy intervals. In particular, 25% deviation from the observed outcome implied approximately 60% expected validity. Current practice in HE model validation can be improved by using an alternative method based on assessing whether the model outcomes fit to empirical data at a predefined level of accuracy. This method has the advantage of assessing both model bias and parameter uncertainty and resulting in a quantitative measure of the degree of validity that penalizes models predicting the mean of an outcome correctly but with overly wide credible intervals. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

  20. Narrow band interference cancelation in OFDM: Astructured maximum likelihood approach

    KAUST Repository

    Sohail, Muhammad Sadiq

    2012-06-01

    This paper presents a maximum likelihood (ML) approach to mitigate the effect of narrow band interference (NBI) in a zero padded orthogonal frequency division multiplexing (ZP-OFDM) system. The NBI is assumed to be time variant and asynchronous with the frequency grid of the ZP-OFDM system. The proposed structure based technique uses the fact that the NBI signal is sparse as compared to the ZP-OFDM signal in the frequency domain. The structure is also useful in reducing the computational complexity of the proposed method. The paper also presents a data aided approach for improved NBI estimation. The suitability of the proposed method is demonstrated through simulations. © 2012 IEEE.

  1. Likelihood inference for unions of interacting discs

    DEFF Research Database (Denmark)

    Møller, Jesper; Helisova, K.

    2010-01-01

    This is probably the first paper which discusses likelihood inference for a random set using a germ-grain model, where the individual grains are unobservable, edge effects occur and other complications appear. We consider the case where the grains form a disc process modelled by a marked point...... process, where the germs are the centres and the marks are the associated radii of the discs. We propose to use a recent parametric class of interacting disc process models, where the minimal sufficient statistic depends on various geometric properties of the random set, and the density is specified......-based maximum likelihood inference and the effect of specifying different reference Poisson models....

  2. Sediment yield estimation in mountain catchments of the Camastra reservoir, southern Italy: a comparison among different empirical methods

    Science.gov (United States)

    Lazzari, Maurizio; Danese, Maria; Gioia, Dario; Piccarreta, Marco

    2013-04-01

    Sedimentary budget estimation is an important topic for both scientific and social community, because it is crucial to understand both dynamics of orogenic belts and many practical problems, such as soil conservation and sediment accumulation in reservoir. Estimations of sediment yield or denudation rates in southern-central Italy are generally obtained by simple empirical relationships based on statistical regression between geomorphic parameters of the drainage network and the measured suspended sediment yield at the outlet of several drainage basins or through the use of models based on sediment delivery ratio or on soil loss equations. In this work, we perform a study of catchment dynamics and an estimation of sedimentary yield for several mountain catchments of the central-western sector of the Basilicata region, southern Italy. Sediment yield estimation has been obtained through both an indirect estimation of suspended sediment yield based on the Tu index (mean annual suspension sediment yield, Ciccacci et al., 1980) and the application of the Rusle (Renard et al., 1997) and the USPED (Mitasova et al., 1996) empirical methods. The preliminary results indicate a reliable difference between the RUSLE and USPED methods and the estimation based on the Tu index; a critical data analysis of results has been carried out considering also the present-day spatial distribution of erosion, transport and depositional processes in relation to the maps obtained from the application of those different empirical methods. The studied catchments drain an artificial reservoir (i.e. the Camastra dam), where a detailed evaluation of the amount of historical sediment storage has been collected. Sediment yield estimation obtained by means of the empirical methods have been compared and checked with historical data of sediment accumulation measured in the artificial reservoir of the Camastra dam. The validation of such estimations of sediment yield at the scale of large catchments

  3. Likelihood Inference of Nonlinear Models Based on a Class of Flexible Skewed Distributions

    Directory of Open Access Journals (Sweden)

    Xuedong Chen

    2014-01-01

    Full Text Available This paper deals with the issue of the likelihood inference for nonlinear models with a flexible skew-t-normal (FSTN distribution, which is proposed within a general framework of flexible skew-symmetric (FSS distributions by combining with skew-t-normal (STN distribution. In comparison with the common skewed distributions such as skew normal (SN, and skew-t (ST as well as scale mixtures of skew normal (SMSN, the FSTN distribution can accommodate more flexibility and robustness in the presence of skewed, heavy-tailed, especially multimodal outcomes. However, for this distribution, a usual approach of maximum likelihood estimates based on EM algorithm becomes unavailable and an alternative way is to return to the original Newton-Raphson type method. In order to improve the estimation as well as the way for confidence estimation and hypothesis test for the parameters of interest, a modified Newton-Raphson iterative algorithm is presented in this paper, based on profile likelihood for nonlinear regression models with FSTN distribution, and, then, the confidence interval and hypothesis test are also developed. Furthermore, a real example and simulation are conducted to demonstrate the usefulness and the superiority of our approach.

  4. Modelling maximum likelihood estimation of availability

    International Nuclear Information System (INIS)

    Waller, R.A.; Tietjen, G.L.; Rock, G.W.

    1975-01-01

    Suppose the performance of a nuclear powered electrical generating power plant is continuously monitored to record the sequence of failure and repairs during sustained operation. The purpose of this study is to assess one method of estimating the performance of the power plant when the measure of performance is availability. That is, we determine the probability that the plant is operational at time t. To study the availability of a power plant, we first assume statistical models for the variables, X and Y, which denote the time-to-failure and the time-to-repair variables, respectively. Once those statistical models are specified, the availability, A(t), can be expressed as a function of some or all of their parameters. Usually those parameters are unknown in practice and so A(t) is unknown. This paper discusses the maximum likelihood estimator of A(t) when the time-to-failure model for X is an exponential density with parameter, lambda, and the time-to-repair model for Y is an exponential density with parameter, theta. Under the assumption of exponential models for X and Y, it follows that the instantaneous availability at time t is A(t)=lambda/(lambda+theta)+theta/(lambda+theta)exp[-[(1/lambda)+(1/theta)]t] with t>0. Also, the steady-state availability is A(infinity)=lambda/(lambda+theta). We use the observations from n failure-repair cycles of the power plant, say X 1 , X 2 , ..., Xsub(n), Y 1 , Y 2 , ..., Ysub(n) to present the maximum likelihood estimators of A(t) and A(infinity). The exact sampling distributions for those estimators and some statistical properties are discussed before a simulation model is used to determine 95% simulation intervals for A(t). The methodology is applied to two examples which approximate the operating history of two nuclear power plants. (author)

  5. MEGA5: Molecular Evolutionary Genetics Analysis Using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods

    Science.gov (United States)

    Tamura, Koichiro; Peterson, Daniel; Peterson, Nicholas; Stecher, Glen; Nei, Masatoshi; Kumar, Sudhir

    2011-01-01

    Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net. PMID:21546353

  6. Likelihood ratio model for classification of forensic evidence

    Energy Technology Data Exchange (ETDEWEB)

    Zadora, G., E-mail: gzadora@ies.krakow.pl [Institute of Forensic Research, Westerplatte 9, 31-033 Krakow (Poland); Neocleous, T., E-mail: tereza@stats.gla.ac.uk [University of Glasgow, Department of Statistics, 15 University Gardens, Glasgow G12 8QW (United Kingdom)

    2009-05-29

    One of the problems of analysis of forensic evidence such as glass fragments, is the determination of their use-type category, e.g. does a glass fragment originate from an unknown window or container? Very small glass fragments arise during various accidents and criminal offences, and could be carried on the clothes, shoes and hair of participants. It is therefore necessary to obtain information on their physicochemical composition in order to solve the classification problem. Scanning Electron Microscopy coupled with an Energy Dispersive X-ray Spectrometer and the Glass Refractive Index Measurement method are routinely used in many forensic institutes for the investigation of glass. A natural form of glass evidence evaluation for forensic purposes is the likelihood ratio-LR = p(E|H{sub 1})/p(E|H{sub 2}). The main aim of this paper was to study the performance of LR models for glass object classification which considered one or two sources of data variability, i.e. between-glass-object variability and(or) within-glass-object variability. Within the proposed model a multivariate kernel density approach was adopted for modelling the between-object distribution and a multivariate normal distribution was adopted for modelling within-object distributions. Moreover, a graphical method of estimating the dependence structure was employed to reduce the highly multivariate problem to several lower-dimensional problems. The performed analysis showed that the best likelihood model was the one which allows to include information about between and within-object variability, and with variables derived from elemental compositions measured by SEM-EDX, and refractive values determined before (RI{sub b}) and after (RI{sub a}) the annealing process, in the form of dRI = log{sub 10}|RI{sub a} - RI{sub b}|. This model gave better results than the model with only between-object variability considered. In addition, when dRI and variables derived from elemental compositions were used, this

  7. Likelihood ratio model for classification of forensic evidence

    International Nuclear Information System (INIS)

    Zadora, G.; Neocleous, T.

    2009-01-01

    One of the problems of analysis of forensic evidence such as glass fragments, is the determination of their use-type category, e.g. does a glass fragment originate from an unknown window or container? Very small glass fragments arise during various accidents and criminal offences, and could be carried on the clothes, shoes and hair of participants. It is therefore necessary to obtain information on their physicochemical composition in order to solve the classification problem. Scanning Electron Microscopy coupled with an Energy Dispersive X-ray Spectrometer and the Glass Refractive Index Measurement method are routinely used in many forensic institutes for the investigation of glass. A natural form of glass evidence evaluation for forensic purposes is the likelihood ratio-LR = p(E|H 1 )/p(E|H 2 ). The main aim of this paper was to study the performance of LR models for glass object classification which considered one or two sources of data variability, i.e. between-glass-object variability and(or) within-glass-object variability. Within the proposed model a multivariate kernel density approach was adopted for modelling the between-object distribution and a multivariate normal distribution was adopted for modelling within-object distributions. Moreover, a graphical method of estimating the dependence structure was employed to reduce the highly multivariate problem to several lower-dimensional problems. The performed analysis showed that the best likelihood model was the one which allows to include information about between and within-object variability, and with variables derived from elemental compositions measured by SEM-EDX, and refractive values determined before (RI b ) and after (RI a ) the annealing process, in the form of dRI = log 10 |RI a - RI b |. This model gave better results than the model with only between-object variability considered. In addition, when dRI and variables derived from elemental compositions were used, this model outperformed two other

  8. An Accurate Method for Inferring Relatedness in Large Datasets of Unphased Genotypes via an Embedded Likelihood-Ratio Test

    KAUST Repository

    Rodriguez, Jesse M.

    2013-01-01

    Studies that map disease genes rely on accurate annotations that indicate whether individuals in the studied cohorts are related to each other or not. For example, in genome-wide association studies, the cohort members are assumed to be unrelated to one another. Investigators can correct for individuals in a cohort with previously-unknown shared familial descent by detecting genomic segments that are shared between them, which are considered to be identical by descent (IBD). Alternatively, elevated frequencies of IBD segments near a particular locus among affected individuals can be indicative of a disease-associated gene. As genotyping studies grow to use increasingly large sample sizes and meta-analyses begin to include many data sets, accurate and efficient detection of hidden relatedness becomes a challenge. To enable disease-mapping studies of increasingly large cohorts, a fast and accurate method to detect IBD segments is required. We present PARENTE, a novel method for detecting related pairs of individuals and shared haplotypic segments within these pairs. PARENTE is a computationally-efficient method based on an embedded likelihood ratio test. As demonstrated by the results of our simulations, our method exhibits better accuracy than the current state of the art, and can be used for the analysis of large genotyped cohorts. PARENTE\\'s higher accuracy becomes even more significant in more challenging scenarios, such as detecting shorter IBD segments or when an extremely low false-positive rate is required. PARENTE is publicly and freely available at http://parente.stanford.edu/. © 2013 Springer-Verlag.

  9. On the likelihood function of Gaussian max-stable processes

    KAUST Repository

    Genton, M. G.; Ma, Y.; Sang, H.

    2011-01-01

    We derive a closed form expression for the likelihood function of a Gaussian max-stable process indexed by ℝd at p≤d+1 sites, d≥1. We demonstrate the gain in efficiency in the maximum composite likelihood estimators of the covariance matrix from p=2 to p=3 sites in ℝ2 by means of a Monte Carlo simulation study. © 2011 Biometrika Trust.

  10. On the likelihood function of Gaussian max-stable processes

    KAUST Repository

    Genton, M. G.

    2011-05-24

    We derive a closed form expression for the likelihood function of a Gaussian max-stable process indexed by ℝd at p≤d+1 sites, d≥1. We demonstrate the gain in efficiency in the maximum composite likelihood estimators of the covariance matrix from p=2 to p=3 sites in ℝ2 by means of a Monte Carlo simulation study. © 2011 Biometrika Trust.

  11. Sampling variability in forensic likelihood-ratio computation: A simulation study

    NARCIS (Netherlands)

    Ali, Tauseef; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.; Meuwly, Didier

    2015-01-01

    Recently, in the forensic biometric community, there is a growing interest to compute a metric called “likelihood- ratio‿ when a pair of biometric specimens is compared using a biometric recognition system. Generally, a biomet- ric recognition system outputs a score and therefore a likelihood-ratio

  12. A comparison of entropy balance and probability weighting methods to generalize observational cohorts to a population: a simulation and empirical example.

    Science.gov (United States)

    Harvey, Raymond A; Hayden, Jennifer D; Kamble, Pravin S; Bouchard, Jonathan R; Huang, Joanna C

    2017-04-01

    We compared methods to control bias and confounding in observational studies including inverse probability weighting (IPW) and stabilized IPW (sIPW). These methods often require iteration and post-calibration to achieve covariate balance. In comparison, entropy balance (EB) optimizes covariate balance a priori by calibrating weights using the target's moments as constraints. We measured covariate balance empirically and by simulation by using absolute standardized mean difference (ASMD), absolute bias (AB), and root mean square error (RMSE), investigating two scenarios: the size of the observed (exposed) cohort exceeds the target (unexposed) cohort and vice versa. The empirical application weighted a commercial health plan cohort to a nationally representative National Health and Nutrition Examination Survey target on the same covariates and compared average total health care cost estimates across methods. Entropy balance alone achieved balance (ASMD ≤ 0.10) on all covariates in simulation and empirically. In simulation scenario I, EB achieved the lowest AB and RMSE (13.64, 31.19) compared with IPW (263.05, 263.99) and sIPW (319.91, 320.71). In scenario II, EB outperformed IPW and sIPW with smaller AB and RMSE. In scenarios I and II, EB achieved the lowest mean estimate difference from the simulated population outcome ($490.05, $487.62) compared with IPW and sIPW, respectively. Empirically, only EB differed from the unweighted mean cost indicating IPW, and sIPW weighting was ineffective. Entropy balance demonstrated the bias-variance tradeoff achieving higher estimate accuracy, yet lower estimate precision, compared with IPW methods. EB weighting required no post-processing and effectively mitigated observed bias and confounding. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  13. Formula I(1 and I(2: Race Tracks for Likelihood Maximization Algorithms of I(1 and I(2 Cointegrated VAR Models

    Directory of Open Access Journals (Sweden)

    Jurgen A. Doornik

    2017-11-01

    Full Text Available This paper provides some test cases, called circuits, for the evaluation of Gaussian likelihood maximization algorithms of the cointegrated vector autoregressive model. Both I(1 and I(2 models are considered. The performance of algorithms is compared first in terms of effectiveness, defined as the ability to find the overall maximum. The next step is to compare their efficiency and reliability across experiments. The aim of the paper is to commence a collective learning project by the profession on the actual properties of algorithms for cointegrated vector autoregressive model estimation, in order to improve their quality and, as a consequence, also the reliability of empirical research.

  14. Maximum likelihood-based analysis of photon arrival trajectories in single-molecule FRET

    Energy Technology Data Exchange (ETDEWEB)

    Waligorska, Marta [Adam Mickiewicz University, Faculty of Chemistry, Grunwaldzka 6, 60-780 Poznan (Poland); Molski, Andrzej, E-mail: amolski@amu.edu.pl [Adam Mickiewicz University, Faculty of Chemistry, Grunwaldzka 6, 60-780 Poznan (Poland)

    2012-07-25

    Highlights: Black-Right-Pointing-Pointer We study model selection and parameter recovery from single-molecule FRET experiments. Black-Right-Pointing-Pointer We examine the maximum likelihood-based analysis of two-color photon trajectories. Black-Right-Pointing-Pointer The number of observed photons determines the performance of the method. Black-Right-Pointing-Pointer For long trajectories, one can extract mean dwell times that are comparable to inter-photon times. -- Abstract: When two fluorophores (donor and acceptor) are attached to an immobilized biomolecule, anti-correlated fluctuations of the donor and acceptor fluorescence caused by Foerster resonance energy transfer (FRET) report on the conformational kinetics of the molecule. Here we assess the maximum likelihood-based analysis of donor and acceptor photon arrival trajectories as a method for extracting the conformational kinetics. Using computer generated data we quantify the accuracy and precision of parameter estimates and the efficiency of the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) in selecting the true kinetic model. We find that the number of observed photons is the key parameter determining parameter estimation and model selection. For long trajectories, one can extract mean dwell times that are comparable to inter-photon times.

  15. Quantifying the Strength of General Factors in Psychopathology: A Comparison of CFA with Maximum Likelihood Estimation, BSEM, and ESEM/EFA Bifactor Approaches.

    Science.gov (United States)

    Murray, Aja Louise; Booth, Tom; Eisner, Manuel; Obsuth, Ingrid; Ribeaud, Denis

    2018-05-22

    Whether or not importance should be placed on an all-encompassing general factor of psychopathology (or p factor) in classifying, researching, diagnosing, and treating psychiatric disorders depends (among other issues) on the extent to which comorbidity is symptom-general rather than staying largely within the confines of narrower transdiagnostic factors such as internalizing and externalizing. In this study, we compared three methods of estimating p factor strength. We compared omega hierarchical and explained common variance calculated from confirmatory factor analysis (CFA) bifactor models with maximum likelihood (ML) estimation, from exploratory structural equation modeling/exploratory factor analysis models with a bifactor rotation, and from Bayesian structural equation modeling (BSEM) bifactor models. Our simulation results suggested that BSEM with small variance priors on secondary loadings might be the preferred option. However, CFA with ML also performed well provided secondary loadings were modeled. We provide two empirical examples of applying the three methodologies using a normative sample of youth (z-proso, n = 1,286) and a university counseling sample (n = 359).

  16. A Maximum-Likelihood Method to Correct for Allelic Dropout in Microsatellite Data with No Replicate Genotypes

    Science.gov (United States)

    Wang, Chaolong; Schroeder, Kari B.; Rosenberg, Noah A.

    2012-01-01

    Allelic dropout is a commonly observed source of missing data in microsatellite genotypes, in which one or both allelic copies at a locus fail to be amplified by the polymerase chain reaction. Especially for samples with poor DNA quality, this problem causes a downward bias in estimates of observed heterozygosity and an upward bias in estimates of inbreeding, owing to mistaken classifications of heterozygotes as homozygotes when one of the two copies drops out. One general approach for avoiding allelic dropout involves repeated genotyping of homozygous loci to minimize the effects of experimental error. Existing computational alternatives often require replicate genotyping as well. These approaches, however, are costly and are suitable only when enough DNA is available for repeated genotyping. In this study, we propose a maximum-likelihood approach together with an expectation-maximization algorithm to jointly estimate allelic dropout rates and allele frequencies when only one set of nonreplicated genotypes is available. Our method considers estimates of allelic dropout caused by both sample-specific factors and locus-specific factors, and it allows for deviation from Hardy–Weinberg equilibrium owing to inbreeding. Using the estimated parameters, we correct the bias in the estimation of observed heterozygosity through the use of multiple imputations of alleles in cases where dropout might have occurred. With simulated data, we show that our method can (1) effectively reproduce patterns of missing data and heterozygosity observed in real data; (2) correctly estimate model parameters, including sample-specific dropout rates, locus-specific dropout rates, and the inbreeding coefficient; and (3) successfully correct the downward bias in estimating the observed heterozygosity. We find that our method is fairly robust to violations of model assumptions caused by population structure and by genotyping errors from sources other than allelic dropout. Because the data sets

  17. A sensitivity analysis of centrifugal compressors' empirical models

    International Nuclear Information System (INIS)

    Yoon, Sung Ho; Baek, Je Hyun

    2001-01-01

    The mean-line method using empirical models is the most practical method of predicting off-design performance. To gain insight into the empirical models, the influence of empirical models on the performance prediction results is investigated. We found that, in the two-zone model, the secondary flow mass fraction has a considerable effect at high mass flow-rates on the performance prediction curves. In the TEIS model, the first element changes the slope of the performance curves as well as the stable operating range. The second element makes the performance curves move up and down as it increases or decreases. It is also discovered that the slip factor affects pressure ratio, but it has little effect on efficiency. Finally, this study reveals that the skin friction coefficient has significant effect on both the pressure ratio curve and the efficiency curve. These results show the limitations of the present empirical models, and more reasonable empirical models are reeded

  18. Maximum likelihood estimation of phase-type distributions

    DEFF Research Database (Denmark)

    Esparza, Luz Judith R

    for both univariate and multivariate cases. Methods like the EM algorithm and Markov chain Monte Carlo are applied for this purpose. Furthermore, this thesis provides explicit formulae for computing the Fisher information matrix for discrete and continuous phase-type distributions, which is needed to find......This work is concerned with the statistical inference of phase-type distributions and the analysis of distributions with rational Laplace transform, known as matrix-exponential distributions. The thesis is focused on the estimation of the maximum likelihood parameters of phase-type distributions...... confidence regions for their estimated parameters. Finally, a new general class of distributions, called bilateral matrix-exponential distributions, is defined. These distributions have the entire real line as domain and can be used, for instance, for modelling. In addition, this class of distributions...

  19. Some features and applications of an empirical approach to the treatment of measurement data (DoD-method)

    International Nuclear Information System (INIS)

    Beyrich, W.; Golly, W.; Spannagel, G.

    1981-01-01

    An empirical method of data evaluation is described which allows the derivation of meaningful estimates of the variances of data groups even if they comprise extreme values (outliers). It can be applied to problems usually treated by variance analysis and seems to be suitable to investigate and describe the state of the art of the various analytical methods applied in international safeguards. Some examples are given to illustrate this procedure; they are based on data of the SALE program

  20. A short proof that phylogenetic tree reconstruction by maximum likelihood is hard.

    Science.gov (United States)

    Roch, Sebastien

    2006-01-01

    Maximum likelihood is one of the most widely used techniques to infer evolutionary histories. Although it is thought to be intractable, a proof of its hardness has been lacking. Here, we give a short proof that computing the maximum likelihood tree is NP-hard by exploiting a connection between likelihood and parsimony observed by Tuffley and Steel.

  1. A Short Proof that Phylogenetic Tree Reconstruction by Maximum Likelihood is Hard

    OpenAIRE

    Roch, S.

    2005-01-01

    Maximum likelihood is one of the most widely used techniques to infer evolutionary histories. Although it is thought to be intractable, a proof of its hardness has been lacking. Here, we give a short proof that computing the maximum likelihood tree is NP-hard by exploiting a connection between likelihood and parsimony observed by Tuffley and Steel.

  2. PWR surveillance based on correspondence between empirical models and physical

    International Nuclear Information System (INIS)

    Zwingelstein, G.; Upadhyaya, B.R.; Kerlin, T.W.

    1976-01-01

    An on line surveillance method based on the correspondence between empirical models and physicals models is proposed for pressurized water reactors. Two types of empirical models are considered as well as the mathematical models defining the correspondence between the physical and empirical parameters. The efficiency of this method is illustrated for the surveillance of the Doppler coefficient for Oconee I (an 886 MWe PWR) [fr

  3. Simplified likelihood for the re-interpretation of public CMS results

    CERN Document Server

    The CMS Collaboration

    2017-01-01

    In this note, a procedure for the construction of simplified likelihoods for the re-interpretation of the results of CMS searches for new physics is presented. The procedure relies on the use of a reduced set of information on the background models used in these searches which can readily be provided by the CMS collaboration. A toy example is used to demonstrate the procedure and its accuracy in reproducing the full likelihood for setting limits in models for physics beyond the standard model. Finally, two representative searches from the CMS collaboration are used to demonstrate the validity of the simplified likelihood approach under realistic conditions.

  4. A comparison of maximum likelihood and other estimators of eigenvalues from several correlated Monte Carlo samples

    International Nuclear Information System (INIS)

    Beer, M.

    1980-01-01

    The maximum likelihood method for the multivariate normal distribution is applied to the case of several individual eigenvalues. Correlated Monte Carlo estimates of the eigenvalue are assumed to follow this prescription and aspects of the assumption are examined. Monte Carlo cell calculations using the SAM-CE and VIM codes for the TRX-1 and TRX-2 benchmark reactors, and SAM-CE full core results are analyzed with this method. Variance reductions of a few percent to a factor of 2 are obtained from maximum likelihood estimation as compared with the simple average and the minimum variance individual eigenvalue. The numerical results verify that the use of sample variances and correlation coefficients in place of the corresponding population statistics still leads to nearly minimum variance estimation for a sufficient number of histories and aggregates

  5. Likelihood inference of non-constant diversification rates with incomplete taxon sampling.

    Science.gov (United States)

    Höhna, Sebastian

    2014-01-01

    Large-scale phylogenies provide a valuable source to study background diversification rates and investigate if the rates have changed over time. Unfortunately most large-scale, dated phylogenies are sparsely sampled (fewer than 5% of the described species) and taxon sampling is not uniform. Instead, taxa are frequently sampled to obtain at least one representative per subgroup (e.g. family) and thus to maximize diversity (diversified sampling). So far, such complications have been ignored, potentially biasing the conclusions that have been reached. In this study I derive the likelihood of a birth-death process with non-constant (time-dependent) diversification rates and diversified taxon sampling. Using simulations I test if the true parameters and the sampling method can be recovered when the trees are small or medium sized (fewer than 200 taxa). The results show that the diversification rates can be inferred and the estimates are unbiased for large trees but are biased for small trees (fewer than 50 taxa). Furthermore, model selection by means of Akaike's Information Criterion favors the true model if the true rates differ sufficiently from alternative models (e.g. the birth-death model is recovered if the extinction rate is large and compared to a pure-birth model). Finally, I applied six different diversification rate models--ranging from a constant-rate pure birth process to a decreasing speciation rate birth-death process but excluding any rate shift models--on three large-scale empirical phylogenies (ants, mammals and snakes with respectively 149, 164 and 41 sampled species). All three phylogenies were constructed by diversified taxon sampling, as stated by the authors. However only the snake phylogeny supported diversified taxon sampling. Moreover, a parametric bootstrap test revealed that none of the tested models provided a good fit to the observed data. The model assumptions, such as homogeneous rates across species or no rate shifts, appear to be

  6. Moral Identity Predicts Doping Likelihood via Moral Disengagement and Anticipated Guilt.

    Science.gov (United States)

    Kavussanu, Maria; Ring, Christopher

    2017-08-01

    In this study, we integrated elements of social cognitive theory of moral thought and action and the social cognitive model of moral identity to better understand doping likelihood in athletes. Participants (N = 398) recruited from a variety of team sports completed measures of moral identity, moral disengagement, anticipated guilt, and doping likelihood. Moral identity predicted doping likelihood indirectly via moral disengagement and anticipated guilt. Anticipated guilt about potential doping mediated the relationship between moral disengagement and doping likelihood. Our findings provide novel evidence to suggest that athletes, who feel that being a moral person is central to their self-concept, are less likely to use banned substances due to their lower tendency to morally disengage and the more intense feelings of guilt they expect to experience for using banned substances.

  7. Predictors of Self-Reported Likelihood of Working with Older Adults

    Science.gov (United States)

    Eshbaugh, Elaine M.; Gross, Patricia E.; Satrom, Tatum

    2010-01-01

    This study examined the self-reported likelihood of working with older adults in a future career among 237 college undergraduates at a midsized Midwestern university. Although aging anxiety was not significantly related to likelihood of working with older adults, those students who had a greater level of death anxiety were less likely than other…

  8. Merging expert and empirical data for rare event frequency estimation: Pool homogenisation for empirical Bayes models

    International Nuclear Information System (INIS)

    Quigley, John; Hardman, Gavin; Bedford, Tim; Walls, Lesley

    2011-01-01

    Empirical Bayes provides one approach to estimating the frequency of rare events as a weighted average of the frequencies of an event and a pool of events. The pool will draw upon, for example, events with similar precursors. The higher the degree of homogeneity of the pool, then the Empirical Bayes estimator will be more accurate. We propose and evaluate a new method using homogenisation factors under the assumption that events are generated from a Homogeneous Poisson Process. The homogenisation factors are scaling constants, which can be elicited through structured expert judgement and used to align the frequencies of different events, hence homogenising the pool. The estimation error relative to the homogeneity of the pool is examined theoretically indicating that reduced error is associated with larger pool homogeneity. The effects of misspecified expert assessments of the homogenisation factors are examined theoretically and through simulation experiments. Our results show that the proposed Empirical Bayes method using homogenisation factors is robust under different degrees of misspecification.

  9. Anticipating cognitive effort: roles of perceived error-likelihood and time demands.

    Science.gov (United States)

    Dunn, Timothy L; Inzlicht, Michael; Risko, Evan F

    2017-11-13

    Why are some actions evaluated as effortful? In the present set of experiments we address this question by examining individuals' perception of effort when faced with a trade-off between two putative cognitive costs: how much time a task takes vs. how error-prone it is. Specifically, we were interested in whether individuals anticipate engaging in a small amount of hard work (i.e., low time requirement, but high error-likelihood) vs. a large amount of easy work (i.e., high time requirement, but low error-likelihood) as being more effortful. In between-subject designs, Experiments 1 through 3 demonstrated that individuals anticipate options that are high in perceived error-likelihood (yet less time consuming) as more effortful than options that are perceived to be more time consuming (yet low in error-likelihood). Further, when asked to evaluate which of the two tasks was (a) more effortful, (b) more error-prone, and (c) more time consuming, effort-based and error-based choices closely tracked one another, but this was not the case for time-based choices. Utilizing a within-subject design, Experiment 4 demonstrated overall similar pattern of judgments as Experiments 1 through 3. However, both judgments of error-likelihood and time demand similarly predicted effort judgments. Results are discussed within the context of extant accounts of cognitive control, with considerations of how error-likelihood and time demands may independently and conjunctively factor into judgments of cognitive effort.

  10. A hybrid filtering method based on a novel empirical mode decomposition for friction signals

    International Nuclear Information System (INIS)

    Li, Chengwei; Zhan, Liwei

    2015-01-01

    During a measurement, the measured signal usually contains noise. To remove the noise and preserve the important feature of the signal, we introduce a hybrid filtering method that uses a new intrinsic mode function (NIMF) and a modified Hausdorff distance. The NIMF is defined as the difference between the noisy signal and each intrinsic mode function (IMF), which is obtained by empirical mode decomposition (EMD), ensemble EMD, complementary ensemble EMD, or complete ensemble EMD with adaptive noise (CEEMDAN). The relevant mode selecting is based on the similarity between the first NIMF and the rest of the NIMFs. With this filtering method, the EMD and improved versions are used to filter the simulation and friction signals. The friction signal between an airplane tire and the runaway is recorded during a simulated airplane touchdown and features spikes of various amplitudes and noise. The filtering effectiveness of the four hybrid filtering methods are compared and discussed. The results show that the filtering method based on CEEMDAN outperforms other signal filtering methods. (paper)

  11. A theory of timing in scintillation counters based on maximum likelihood estimation

    International Nuclear Information System (INIS)

    Tomitani, Takehiro

    1982-01-01

    A theory of timing in scintillation counters based on the maximum likelihood estimation is presented. An optimum filter that minimizes the variance of timing is described. A simple formula to estimate the variance of timing is presented as a function of photoelectron number, scintillation decay constant and the single electron transit time spread in the photomultiplier. The present method was compared with the theory by E. Gatti and V. Svelto. The proposed method was applied to two simple models and rough estimations of potential time resolution of several scintillators are given. The proposed method is applicable to the timing in Cerenkov counters and semiconductor detectors as well. (author)

  12. Risk Presentation Using the Three Dimensions of Likelihood, Severity, and Level of Control

    Science.gov (United States)

    Watson, Clifford

    2010-01-01

    Traditional hazard analysis techniques utilize a two-dimensional representation of the results determined by relative likelihood and severity of the residual risk. These matrices present a quick-look at the Likelihood (Y-axis) and Severity (X-axis) of the probable outcome of a hazardous event. A three-dimensional method, described herein, utilizes the traditional X and Y axes, while adding a new, third dimension, shown as the Z-axis, and referred to as the Level of Control. The elements of the Z-axis are modifications of the Hazard Elimination and Control steps (also known as the Hazard Reduction Precedence Sequence). These steps are: 1. Eliminate risk through design. 2. Substitute less risky materials for more hazardous materials. 3. Install safety devices. 4. Install caution and warning devices. 5. Develop administrative controls (to include special procedures and training.) 6. Provide protective clothing and equipment. When added to the two-dimensional models, the level of control adds a visual representation of the risk associated with the hazardous condition, creating a tall-pole for the leastwell-controlled failure while establishing the relative likelihood and severity of all causes and effects for an identified hazard. Computer modeling of the analytical results, using spreadsheets and three-dimensional charting gives a visual confirmation of the relationship between causes and their controls.

  13. A biclustering algorithm for binary matrices based on penalized Bernoulli likelihood

    KAUST Repository

    Lee, Seokho

    2013-01-31

    We propose a new biclustering method for binary data matrices using the maximum penalized Bernoulli likelihood estimation. Our method applies a multi-layer model defined on the logits of the success probabilities, where each layer represents a simple bicluster structure and the combination of multiple layers is able to reveal complicated, multiple biclusters. The method allows for non-pure biclusters, and can simultaneously identify the 1-prevalent blocks and 0-prevalent blocks. A computationally efficient algorithm is developed and guidelines are provided for specifying the tuning parameters, including initial values of model parameters, the number of layers, and the penalty parameters. Missing-data imputation can be handled in the EM framework. The method is tested using synthetic and real datasets and shows good performance. © 2013 Springer Science+Business Media New York.

  14. Empirical Research In Engineering Design

    DEFF Research Database (Denmark)

    Ahmed, Saeema

    2007-01-01

    Increasingly engineering design research involves the use of empirical studies that are conducted within an industrial environment [Ahmed, 2001; Court 1995; Hales 1987]. Research into the use of information by designers or understanding how engineers build up experience are examples of research...... of research issues. This paper describes case studies of empirical research carried out within industry in engineering design focusing upon information, knowledge and experience in engineering design. The paper describes the research methods employed, their suitability for the particular research aims...

  15. Bacterial clonal diagnostics as a tool for evidence-based empiric antibiotic selection.

    Science.gov (United States)

    Tchesnokova, Veronika; Avagyan, Hovhannes; Rechkina, Elena; Chan, Diana; Muradova, Mariya; Haile, Helen Ghirmai; Radey, Matthew; Weissman, Scott; Riddell, Kim; Scholes, Delia; Johnson, James R; Sokurenko, Evgeni V

    2017-01-01

    Despite the known clonal distribution of antibiotic resistance in many bacteria, empiric (pre-culture) antibiotic selection still relies heavily on species-level cumulative antibiograms, resulting in overuse of broad-spectrum agents and excessive antibiotic/pathogen mismatch. Urinary tract infections (UTIs), which account for a large share of antibiotic use, are caused predominantly by Escherichia coli, a highly clonal pathogen. In an observational clinical cohort study of urgent care patients with suspected UTI, we assessed the potential for E. coli clonal-level antibiograms to improve empiric antibiotic selection. A novel PCR-based clonotyping assay was applied to fresh urine samples to rapidly detect E. coli and the urine strain's clonotype. Based on a database of clonotype-specific antibiograms, the acceptability of various antibiotics for empiric therapy was inferred using a 20%, 10%, and 30% allowed resistance threshold. The test's performance characteristics and possible effects on prescribing were assessed. The rapid test identified E. coli clonotypes directly in patients' urine within 25-35 minutes, with high specificity and sensitivity compared to culture. Antibiotic selection based on a clonotype-specific antibiogram could reduce the relative likelihood of antibiotic/pathogen mismatch by ≥ 60%. Compared to observed prescribing patterns, clonal diagnostics-guided antibiotic selection could safely double the use of trimethoprim/sulfamethoxazole and minimize fluoroquinolone use. In summary, a rapid clonotyping test showed promise for improving empiric antibiotic prescribing for E. coli UTI, including reversing preferential use of fluoroquinolones over trimethoprim/sulfamethoxazole. The clonal diagnostics approach merges epidemiologic surveillance, antimicrobial stewardship, and molecular diagnostics to bring evidence-based medicine directly to the point of care.

  16. Maximum likelihood reconstruction in fully 3D PET via the SAGE algorithm

    International Nuclear Information System (INIS)

    Ollinger, J.M.; Goggin, A.S.

    1996-01-01

    The SAGE and ordered subsets algorithms have been proposed as fast methods to compute penalized maximum likelihood estimates in PET. We have implemented both for use in fully 3D PET and completed a preliminary evaluation. The technique used to compute the transition matrix is fully described. The evaluation suggests that the ordered subsets algorithm converges much faster than SAGE, but that it stops short of the optimal solution

  17. Empirical method to measure stochasticity and multifractality in nonlinear time series

    Science.gov (United States)

    Lin, Chih-Hao; Chang, Chia-Seng; Li, Sai-Ping

    2013-12-01

    An empirical algorithm is used here to study the stochastic and multifractal nature of nonlinear time series. A parameter can be defined to quantitatively measure the deviation of the time series from a Wiener process so that the stochasticity of different time series can be compared. The local volatility of the time series under study can be constructed using this algorithm, and the multifractal structure of the time series can be analyzed by using this local volatility. As an example, we employ this method to analyze financial time series from different stock markets. The result shows that while developed markets evolve very much like an Ito process, the emergent markets are far from efficient. Differences about the multifractal structures and leverage effects between developed and emergent markets are discussed. The algorithm used here can be applied in a similar fashion to study time series of other complex systems.

  18. A Maximum Likelihood Approach to Determine Sensor Radiometric Response Coefficients for NPP VIIRS Reflective Solar Bands

    Science.gov (United States)

    Lei, Ning; Chiang, Kwo-Fu; Oudrari, Hassan; Xiong, Xiaoxiong

    2011-01-01

    Optical sensors aboard Earth orbiting satellites such as the next generation Visible/Infrared Imager/Radiometer Suite (VIIRS) assume that the sensors radiometric response in the Reflective Solar Bands (RSB) is described by a quadratic polynomial, in relating the aperture spectral radiance to the sensor Digital Number (DN) readout. For VIIRS Flight Unit 1, the coefficients are to be determined before launch by an attenuation method, although the linear coefficient will be further determined on-orbit through observing the Solar Diffuser. In determining the quadratic polynomial coefficients by the attenuation method, a Maximum Likelihood approach is applied in carrying out the least-squares procedure. Crucial to the Maximum Likelihood least-squares procedure is the computation of the weight. The weight not only has a contribution from the noise of the sensor s digital count, with an important contribution from digitization error, but also is affected heavily by the mathematical expression used to predict the value of the dependent variable, because both the independent and the dependent variables contain random noise. In addition, model errors have a major impact on the uncertainties of the coefficients. The Maximum Likelihood approach demonstrates the inadequacy of the attenuation method model with a quadratic polynomial for the retrieved spectral radiance. We show that using the inadequate model dramatically increases the uncertainties of the coefficients. We compute the coefficient values and their uncertainties, considering both measurement and model errors.

  19. Profile-likelihood Confidence Intervals in Item Response Theory Models.

    Science.gov (United States)

    Chalmers, R Philip; Pek, Jolynn; Liu, Yang

    2017-01-01

    Confidence intervals (CIs) are fundamental inferential devices which quantify the sampling variability of parameter estimates. In item response theory, CIs have been primarily obtained from large-sample Wald-type approaches based on standard error estimates, derived from the observed or expected information matrix, after parameters have been estimated via maximum likelihood. An alternative approach to constructing CIs is to quantify sampling variability directly from the likelihood function with a technique known as profile-likelihood confidence intervals (PL CIs). In this article, we introduce PL CIs for item response theory models, compare PL CIs to classical large-sample Wald-type CIs, and demonstrate important distinctions among these CIs. CIs are then constructed for parameters directly estimated in the specified model and for transformed parameters which are often obtained post-estimation. Monte Carlo simulation results suggest that PL CIs perform consistently better than Wald-type CIs for both non-transformed and transformed parameters.

  20. Supplementary Material for: High-Order Composite Likelihood Inference for Max-Stable Distributions and Processes

    KAUST Repository

    Castruccio, Stefano; Huser, Raphaë l; Genton, Marc G.

    2016-01-01

    In multivariate or spatial extremes, inference for max-stable processes observed at a large collection of points is a very challenging problem and current approaches typically rely on less expensive composite likelihoods constructed from small subsets of data. In this work, we explore the limits of modern state-of-the-art computational facilities to perform full likelihood inference and to efficiently evaluate high-order composite likelihoods. With extensive simulations, we assess the loss of information of composite likelihood estimators with respect to a full likelihood approach for some widely used multivariate or spatial extreme models, we discuss how to choose composite likelihood truncation to improve the efficiency, and we also provide recommendations for practitioners. This article has supplementary material online.

  1. Exclusion probabilities and likelihood ratios with applications to mixtures.

    Science.gov (United States)

    Slooten, Klaas-Jan; Egeland, Thore

    2016-01-01

    The statistical evidence obtained from mixed DNA profiles can be summarised in several ways in forensic casework including the likelihood ratio (LR) and the Random Man Not Excluded (RMNE) probability. The literature has seen a discussion of the advantages and disadvantages of likelihood ratios and exclusion probabilities, and part of our aim is to bring some clarification to this debate. In a previous paper, we proved that there is a general mathematical relationship between these statistics: RMNE can be expressed as a certain average of the LR, implying that the expected value of the LR, when applied to an actual contributor to the mixture, is at least equal to the inverse of the RMNE. While the mentioned paper presented applications for kinship problems, the current paper demonstrates the relevance for mixture cases, and for this purpose, we prove some new general properties. We also demonstrate how to use the distribution of the likelihood ratio for donors of a mixture, to obtain estimates for exceedance probabilities of the LR for non-donors, of which the RMNE is a special case corresponding to L R>0. In order to derive these results, we need to view the likelihood ratio as a random variable. In this paper, we describe how such a randomization can be achieved. The RMNE is usually invoked only for mixtures without dropout. In mixtures, artefacts like dropout and drop-in are commonly encountered and we address this situation too, illustrating our results with a basic but widely implemented model, a so-called binary model. The precise definitions, modelling and interpretation of the required concepts of dropout and drop-in are not entirely obvious, and we attempt to clarify them here in a general likelihood framework for a binary model.

  2. Simple simulation of diffusion bridges with application to likelihood inference for diffusions

    DEFF Research Database (Denmark)

    Bladt, Mogens; Sørensen, Michael

    2014-01-01

    the accuracy and efficiency of the approximate method and compare it to exact simulation methods. In the study, our method provides a very good approximation to the distribution of a diffusion bridge for bridges that are likely to occur in applications to statistical inference. To illustrate the usefulness......With a view to statistical inference for discretely observed diffusion models, we propose simple methods of simulating diffusion bridges, approximately and exactly. Diffusion bridge simulation plays a fundamental role in likelihood and Bayesian inference for diffusion processes. First a simple......-dimensional diffusions and is applicable to all one-dimensional diffusion processes with finite speed-measure. One advantage of the new approach is that simple simulation methods like the Milstein scheme can be applied to bridge simulation. Another advantage over previous bridge simulation methods is that the proposed...

  3. Teaching Empirical Software Engineering Using Expert Teams

    DEFF Research Database (Denmark)

    Kuhrmann, Marco

    2017-01-01

    Empirical software engineering aims at making software engineering claims measurable, i.e., to analyze and understand phenomena in software engineering and to evaluate software engineering approaches and solutions. Due to the involvement of humans and the multitude of fields for which software...... is crucial, software engineering is considered hard to teach. Yet, empirical software engineering increases this difficulty by adding the scientific method as extra dimension. In this paper, we present a Master-level course on empirical software engineering in which different empirical instruments...... an extra specific expertise that they offer as service to other teams, thus, fostering cross-team collaboration. The paper outlines the general course setup, topics addressed, and it provides initial lessons learned....

  4. Evaluation and comparison of estimation methods for failure rates and probabilities

    Energy Technology Data Exchange (ETDEWEB)

    Vaurio, Jussi K. [Fortum Power and Heat Oy, P.O. Box 23, 07901 Loviisa (Finland)]. E-mail: jussi.vaurio@fortum.com; Jaenkaelae, Kalle E. [Fortum Nuclear Services, P.O. Box 10, 00048 Fortum (Finland)

    2006-02-01

    An updated parametric robust empirical Bayes (PREB) estimation methodology is presented as an alternative to several two-stage Bayesian methods used to assimilate failure data from multiple units or plants. PREB is based on prior-moment matching and avoids multi-dimensional numerical integrations. The PREB method is presented for failure-truncated and time-truncated data. Erlangian and Poisson likelihoods with gamma prior are used for failure rate estimation, and Binomial data with beta prior are used for failure probability per demand estimation. Combined models and assessment uncertainties are accounted for. One objective is to compare several methods with numerical examples and show that PREB works as well if not better than the alternative more complex methods, especially in demanding problems of small samples, identical data and zero failures. False claims and misconceptions are straightened out, and practical applications in risk studies are presented.

  5. Sampling of systematic errors to estimate likelihood weights in nuclear data uncertainty propagation

    International Nuclear Information System (INIS)

    Helgesson, P.; Sjöstrand, H.; Koning, A.J.; Rydén, J.; Rochman, D.; Alhassan, E.; Pomp, S.

    2016-01-01

    In methodologies for nuclear data (ND) uncertainty assessment and propagation based on random sampling, likelihood weights can be used to infer experimental information into the distributions for the ND. As the included number of correlated experimental points grows large, the computational time for the matrix inversion involved in obtaining the likelihood can become a practical problem. There are also other problems related to the conventional computation of the likelihood, e.g., the assumption that all experimental uncertainties are Gaussian. In this study, a way to estimate the likelihood which avoids matrix inversion is investigated; instead, the experimental correlations are included by sampling of systematic errors. It is shown that the model underlying the sampling methodology (using univariate normal distributions for random and systematic errors) implies a multivariate Gaussian for the experimental points (i.e., the conventional model). It is also shown that the likelihood estimates obtained through sampling of systematic errors approach the likelihood obtained with matrix inversion as the sample size for the systematic errors grows large. In studied practical cases, it is seen that the estimates for the likelihood weights converge impractically slowly with the sample size, compared to matrix inversion. The computational time is estimated to be greater than for matrix inversion in cases with more experimental points, too. Hence, the sampling of systematic errors has little potential to compete with matrix inversion in cases where the latter is applicable. Nevertheless, the underlying model and the likelihood estimates can be easier to intuitively interpret than the conventional model and the likelihood function involving the inverted covariance matrix. Therefore, this work can both have pedagogical value and be used to help motivating the conventional assumption of a multivariate Gaussian for experimental data. The sampling of systematic errors could also

  6. Empirical comparison of four baseline covariate adjustment methods in analysis of continuous outcomes in randomized controlled trials

    Directory of Open Access Journals (Sweden)

    Zhang S

    2014-07-01

    Full Text Available Shiyuan Zhang,1 James Paul,2 Manyat Nantha-Aree,2 Norman Buckley,2 Uswa Shahzad,2 Ji Cheng,2 Justin DeBeer,5 Mitchell Winemaker,5 David Wismer,5 Dinshaw Punthakee,5 Victoria Avram,5 Lehana Thabane1–41Department of Clinical Epidemiology and Biostatistics, 2Department of Anesthesia, McMaster University, Hamilton, ON, Canada; 3Biostatistics Unit/Centre for Evaluation of Medicines, St Joseph's Healthcare - Hamilton, Hamilton, ON, Canada; 4Population Health Research Institute, Hamilton Health Science/McMaster University, 5Department of Surgery, Division of Orthopaedics, McMaster University, Hamilton, ON, CanadaBackground: Although seemingly straightforward, the statistical comparison of a continuous variable in a randomized controlled trial that has both a pre- and posttreatment score presents an interesting challenge for trialists. We present here empirical application of four statistical methods (posttreatment scores with analysis of variance, analysis of covariance, change in scores, and percent change in scores, using data from a randomized controlled trial of postoperative pain in patients following total joint arthroplasty (the Morphine COnsumption in Joint Replacement Patients, With and Without GaBapentin Treatment, a RandomIzed ControlLEd Study [MOBILE] trials.Methods: Analysis of covariance (ANCOVA was used to adjust for baseline measures and to provide an unbiased estimate of the mean group difference of the 1-year postoperative knee flexion scores in knee arthroplasty patients. Robustness tests were done by comparing ANCOVA with three comparative methods: the posttreatment scores, change in scores, and percentage change from baseline.Results: All four methods showed similar direction of effect; however, ANCOVA (-3.9; 95% confidence interval [CI]: -9.5, 1.6; P=0.15 and the posttreatment score (-4.3; 95% CI: -9.8, 1.2; P=0.12 method provided the highest precision of estimate compared with the change score (-3.0; 95% CI: -9.9, 3.8; P=0

  7. Likelihood ratio decisions in memory: three implied regularities.

    Science.gov (United States)

    Glanzer, Murray; Hilford, Andrew; Maloney, Laurence T

    2009-06-01

    We analyze four general signal detection models for recognition memory that differ in their distributional assumptions. Our analyses show that a basic assumption of signal detection theory, the likelihood ratio decision axis, implies three regularities in recognition memory: (1) the mirror effect, (2) the variance effect, and (3) the z-ROC length effect. For each model, we present the equations that produce the three regularities and show, in computed examples, how they do so. We then show that the regularities appear in data from a range of recognition studies. The analyses and data in our study support the following generalization: Individuals make efficient recognition decisions on the basis of likelihood ratios.

  8. Asymptotic Likelihood Distribution for Correlated & Constrained Systems

    CERN Document Server

    Agarwal, Ujjwal

    2016-01-01

    It describes my work as summer student at CERN. The report discusses the asymptotic distribution of the likelihood ratio for total no. of parameters being h and 2 out of these being are constrained and correlated.

  9. Comparison of likelihood testing procedures for parallel systems with covariances

    International Nuclear Information System (INIS)

    Ayman Baklizi; Isa Daud; Noor Akma Ibrahim

    1998-01-01

    In this paper we considered investigating and comparing the behavior of the likelihood ratio, the Rao's and the Wald's statistics for testing hypotheses on the parameters of the simple linear regression model based on parallel systems with covariances. These statistics are asymptotically equivalent (Barndorff-Nielsen and Cox, 1994). However, their relative performances in finite samples are generally known. A Monte Carlo experiment is conducted to stimulate the sizes and the powers of these statistics for complete samples and in the presence of time censoring. Comparisons of the statistics are made according to the attainment of assumed size of the test and their powers at various points in the parameter space. The results show that the likelihood ratio statistics appears to have the best performance in terms of the attainment of the assumed size of the test. Power comparisons show that the Rao statistic has some advantage over the Wald statistic in almost all of the space of alternatives while likelihood ratio statistic occupies either the first or the last position in term of power. Overall, the likelihood ratio statistic appears to be more appropriate to the model under study, especially for small sample sizes

  10. Joint maximum-likelihood magnitudes of presumed underground nuclear test explosions

    Science.gov (United States)

    Peacock, Sheila; Douglas, Alan; Bowers, David

    2017-08-01

    Body-wave magnitudes (mb) of 606 seismic disturbances caused by presumed underground nuclear test explosions at specific test sites between 1964 and 1996 have been derived from station amplitudes collected by the International Seismological Centre (ISC), by a joint inversion for mb and station-specific magnitude corrections. A maximum-likelihood method was used to reduce the upward bias of network mean magnitudes caused by data censoring, where arrivals at stations that do not report arrivals are assumed to be hidden by the ambient noise at the time. Threshold noise levels at each station were derived from the ISC amplitudes using the method of Kelly and Lacoss, which fits to the observed magnitude-frequency distribution a Gutenberg-Richter exponential decay truncated at low magnitudes by an error function representing the low-magnitude threshold of the station. The joint maximum-likelihood inversion is applied to arrivals from the sites: Semipalatinsk (Kazakhstan) and Novaya Zemlya, former Soviet Union; Singer (Lop Nor), China; Mururoa and Fangataufa, French Polynesia; and Nevada, USA. At sites where eight or more arrivals could be used to derive magnitudes and station terms for 25 or more explosions (Nevada, Semipalatinsk and Mururoa), the resulting magnitudes and station terms were fixed and a second inversion carried out to derive magnitudes for additional explosions with three or more arrivals. 93 more magnitudes were thus derived. During processing for station thresholds, many stations were rejected for sparsity of data, obvious errors in reported amplitude, or great departure of the reported amplitude-frequency distribution from the expected left-truncated exponential decay. Abrupt changes in monthly mean amplitude at a station apparently coincide with changes in recording equipment and/or analysis method at the station.

  11. Levels of reduction in van Manen's phenomenological hermeneutic method: an empirical example.

    Science.gov (United States)

    Heinonen, Kristiina

    2015-05-01

    To describe reduction as a method using van Manen's phenomenological hermeneutic research approach. Reduction involves several levels that can be distinguished for their methodological usefulness. Researchers can use reduction in different ways and dimensions for their methodological needs. A study of Finnish multiple-birth families in which open interviews (n=38) were conducted with public health nurses, family care workers and parents of twins. A systematic literature and knowledge review showed there were no articles on multiple-birth families that used van Manen's method. Discussion The phenomena of the 'lifeworlds' of multiple-birth families consist of three core essential themes as told by parents: 'a state of constant vigilance', 'ensuring that they can continue to cope' and 'opportunities to share with other people'. Reduction provides the opportunity to carry out in-depth phenomenological hermeneutic research and understand people's lives. It helps to keep research stages separate but also enables a consolidated view. Social care and healthcare professionals have to hear parents' voices better to comprehensively understand their situation; they need further tools and training to be able to empower parents of twins. This paper adds an empirical example to the discussion of phenomenology, hermeneutic study and reduction as a method. It opens up reduction for researchers to exploit.

  12. Estimation of flashover voltage probability of overhead line insulators under industrial pollution, based on maximum likelihood method

    International Nuclear Information System (INIS)

    Arab, M.N.; Ayaz, M.

    2004-01-01

    The performance of transmission line insulator is greatly affected by dust, fumes from industrial areas and saline deposit near the coast. Such pollutants in the presence of moisture form a coating on the surface of the insulator, which in turn allows the passage of leakage current. This leakage builds up to a point where flashover develops. The flashover is often followed by permanent failure of insulation resulting in prolong outages. With the increase in system voltage owing to the greater demand of electrical energy over the past few decades, the importance of flashover due to pollution has received special attention. The objective of the present work was to study the performance of overhead line insulators in the presence of contaminants such as induced salts. A detailed review of the literature and the mechanisms of insulator flashover due to the pollution are presented. Experimental investigations on the behavior of overhead line insulators under industrial salt contamination are carried out. A special fog chamber was designed in which the contamination testing of insulators was carried out. Flashover behavior under various degrees of contamination of insulators with the most common industrial fume components such as Nitrate and Sulphate compounds was studied. Substituting the normal distribution parameter in the probability distribution function based on maximum likelihood develops a statistical method. The method gives a high accuracy in the estimation of the 50% flashover voltage, which is then used to evaluate the critical flashover index at various contamination levels. The critical flashover index is a valuable parameter in insulation design for numerous applications. (author)

  13. Empirical research through design

    NARCIS (Netherlands)

    Keyson, D.V.; Bruns, M.

    2009-01-01

    This paper describes the empirical research through design method (ERDM), which differs from current approaches to research through design by enforcing the need for the designer, after a series of pilot prototype based studies, to a-priori develop a number of testable interaction design hypothesis

  14. The Location-Scale Mixture Exponential Power Distribution: A Bayesian and Maximum Likelihood Approach

    OpenAIRE

    Rahnamaei, Z.; Nematollahi, N.; Farnoosh, R.

    2012-01-01

    We introduce an alternative skew-slash distribution by using the scale mixture of the exponential power distribution. We derive the properties of this distribution and estimate its parameter by Maximum Likelihood and Bayesian methods. By a simulation study we compute the mentioned estimators and their mean square errors, and we provide an example on real data to demonstrate the modeling strength of the new distribution.

  15. How users adopt healthcare information: An empirical study of an online Q&A community.

    Science.gov (United States)

    Jin, Jiahua; Yan, Xiangbin; Li, Yijun; Li, Yumei

    2016-02-01

    The emergence of social media technology has led to the creation of many online healthcare communities, where patients can easily share and look for healthcare-related information from peers who have experienced a similar problem. However, with increased user-generated content, there is a need to constantly analyse which content should be trusted as one sifts through enormous amounts of healthcare information. This study aims to explore patients' healthcare information seeking behavior in online communities. Based on dual-process theory and the knowledge adoption model, we proposed a healthcare information adoption model for online communities. This model highlights that information quality, emotional support, and source credibility are antecedent variables of adoption likelihood of healthcare information, and competition among repliers and involvement of recipients moderate the relationship between the antecedent variables and adoption likelihood. Empirical data were collected from the healthcare module of China's biggest Q&A community-Baidu Knows. Text mining techniques were adopted to calculate the information quality and emotional support contained in each reply text. A binary logistics regression model and hierarchical regression approach were employed to test the proposed conceptual model. Information quality, emotional support, and source credibility have significant and positive impact on healthcare information adoption likelihood, and among these factors, information quality has the biggest impact on a patient's adoption decision. In addition, competition among repliers and involvement of recipients were tested as moderating effects between these antecedent factors and the adoption likelihood. Results indicate competition among repliers positively moderates the relationship between source credibility and adoption likelihood, and recipients' involvement positively moderates the relationship between information quality, source credibility, and adoption

  16. EbayesThresh: R Programs for Empirical Bayes Thresholding

    Directory of Open Access Journals (Sweden)

    Iain Johnstone

    2005-04-01

    Full Text Available Suppose that a sequence of unknown parameters is observed sub ject to independent Gaussian noise. The EbayesThresh package in the S language implements a class of Empirical Bayes thresholding methods that can take advantage of possible sparsity in the sequence, to improve the quality of estimation. The prior for each parameter in the sequence is a mixture of an atom of probability at zero and a heavy-tailed density. Within the package, this can be either a Laplace (double exponential density or else a mixture of normal distributions with tail behavior similar to the Cauchy distribution. The mixing weight, or sparsity parameter, is chosen automatically by marginal maximum likelihood. If estimation is carried out using the posterior median, this is a random thresholding procedure; the estimation can also be carried out using other thresholding rules with the same threshold, and the package provides the posterior mean, and hard and soft thresholding, as additional options. This paper reviews the method, and gives details (far beyond those previously published of the calculations needed for implementing the procedures. It explains and motivates both the general methodology, and the use of the EbayesThresh package, through simulated and real data examples. When estimating the wavelet transform of an unknown function, it is appropriate to apply the method level by level to the transform of the observed data. The package can carry out these calculations for wavelet transforms obtained using various packages in R and S-PLUS. Details, including a motivating example, are presented, and the application of the method to image estimation is also explored. The final topic considered is the estimation of a single sequence that may become progressively sparser along the sequence. An iterated least squares isotone regression method allows for the choice of a threshold that depends monotonically on the order in which the observations are made. An alternative

  17. Geochemical wolframite fingerprinting - the likelihood ratio approach for laser ablation ICP-MS data.

    Science.gov (United States)

    Martyna, Agnieszka; Gäbler, Hans-Eike; Bahr, Andreas; Zadora, Grzegorz

    2018-05-01

    Wolframite has been specified as a 'conflict mineral' by a U.S. Government Act, which obliges companies that use these minerals to report their origin. Minerals originating from conflict regions in the Democratic Republic of the Congo shall be excluded from the market as their illegal mining, trading, and taxation are supposed to fuel ongoing violent conflicts. The German Federal Institute for Geosciences and Natural Resources (BGR) developed a geochemical fingerprinting method for wolframite based on laser ablation inductively coupled plasma-mass spectrometry. Concentrations of 46 elements in about 5300 wolframite grains from 64 mines were determined. The issue of verifying the declared origins of the wolframite samples may be framed as a forensic problem by considering two contrasting hypotheses: the examined sample and a sample collected from the declared mine originate from the same mine (H 1 ), and the two samples come from different mines (H 2 ). The solution is found using the likelihood ratio (LR) theory. On account of the multidimensionality, the lack of normal distribution of data within each sample, and the huge within-sample dispersion in relation to the dispersion between samples, the classic LR models had to be modified. Robust principal component analysis and linear discriminant analysis were used to characterize samples. The similarity of two samples was expressed by Kolmogorov-Smirnov distances, which were interpreted in view of H 1 and H 2 hypotheses within the LR framework. The performance of the models, controlled by the levels of incorrect responses and the empirical cross entropy, demonstrated that the proposed LR models are successful in verifying the authenticity of the wolframite samples. Graphical abstract Geochemical wolframite fingerprinting.

  18. The Socratic Method: Empirical Assessment of a Psychology Capstone Course

    Science.gov (United States)

    Burns, Lawrence R.; Stephenson, Paul L.; Bellamy, Katy

    2016-01-01

    Although students make some epistemological progress during college, most graduate without developing meaning-making strategies that reflect an understanding that knowledge is socially constructed. Using a pre-test-post-test design and a within-subjects 2 × 2 mixed-design ANOVA, this study reports on empirical findings which support the Socratic…

  19. Efficient Bit-to-Symbol Likelihood Mappings

    Science.gov (United States)

    Moision, Bruce E.; Nakashima, Michael A.

    2010-01-01

    This innovation is an efficient algorithm designed to perform bit-to-symbol and symbol-to-bit likelihood mappings that represent a significant portion of the complexity of an error-correction code decoder for high-order constellations. Recent implementation of the algorithm in hardware has yielded an 8- percent reduction in overall area relative to the prior design.

  20. Interface of the polarizable continuum model of solvation with semi-empirical methods in the GAMESS program.

    Directory of Open Access Journals (Sweden)

    Casper Steinmann

    Full Text Available An interface between semi-empirical methods and the polarized continuum model (PCM of solvation successfully implemented into GAMESS following the approach by Chudinov et al (Chem. Phys. 1992, 160, 41. The interface includes energy gradients and is parallelized. For large molecules such as ubiquitin a reasonable speedup (up to a factor of six is observed for up to 16 cores. The SCF convergence is greatly improved by PCM for proteins compared to the gas phase.

  1. Communicating likelihoods and probabilities in forecasts of volcanic eruptions

    Science.gov (United States)

    Doyle, Emma E. H.; McClure, John; Johnston, David M.; Paton, Douglas

    2014-02-01

    The issuing of forecasts and warnings of natural hazard events, such as volcanic eruptions, earthquake aftershock sequences and extreme weather often involves the use of probabilistic terms, particularly when communicated by scientific advisory groups to key decision-makers, who can differ greatly in relative expertise and function in the decision making process. Recipients may also differ in their perception of relative importance of political and economic influences on interpretation. Consequently, the interpretation of these probabilistic terms can vary greatly due to the framing of the statements, and whether verbal or numerical terms are used. We present a review from the psychology literature on how the framing of information influences communication of these probability terms. It is also unclear as to how people rate their perception of an event's likelihood throughout a time frame when a forecast time window is stated. Previous research has identified that, when presented with a 10-year time window forecast, participants viewed the likelihood of an event occurring ‘today’ as being of less than that in year 10. Here we show that this skew in perception also occurs for short-term time windows (under one week) that are of most relevance for emergency warnings. In addition, unlike the long-time window statements, the use of the phrasing “within the next…” instead of “in the next…” does not mitigate this skew, nor do we observe significant differences between the perceived likelihoods of scientists and non-scientists. This finding suggests that effects occurring due to the shorter time window may be ‘masking’ any differences in perception due to wording or career background observed for long-time window forecasts. These results have implications for scientific advice, warning forecasts, emergency management decision-making, and public information as any skew in perceived event likelihood towards the end of a forecast time window may result in

  2. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree

    Directory of Open Access Journals (Sweden)

    Kodner Robin B

    2010-10-01

    Full Text Available Abstract Background Likelihood-based phylogenetic inference is generally considered to be the most reliable classification method for unknown sequences. However, traditional likelihood-based phylogenetic methods cannot be applied to large volumes of short reads from next-generation sequencing due to computational complexity issues and lack of phylogenetic signal. "Phylogenetic placement," where a reference tree is fixed and the unknown query sequences are placed onto the tree via a reference alignment, is a way to bring the inferential power offered by likelihood-based approaches to large data sets. Results This paper introduces pplacer, a software package for phylogenetic placement and subsequent visualization. The algorithm can place twenty thousand short reads on a reference tree of one thousand taxa per hour per processor, has essentially linear time and memory complexity in the number of reference taxa, and is easy to run in parallel. Pplacer features calculation of the posterior probability of a placement on an edge, which is a statistically rigorous way of quantifying uncertainty on an edge-by-edge basis. It also can inform the user of the positional uncertainty for query sequences by calculating expected distance between placement locations, which is crucial in the estimation of uncertainty with a well-sampled reference tree. The software provides visualizations using branch thickness and color to represent number of placements and their uncertainty. A simulation study using reads generated from 631 COG alignments shows a high level of accuracy for phylogenetic placement over a wide range of alignment diversity, and the power of edge uncertainty estimates to measure placement confidence. Conclusions Pplacer enables efficient phylogenetic placement and subsequent visualization, making likelihood-based phylogenetics methodology practical for large collections of reads; it is freely available as source code, binaries, and a web service.

  3. Straight line fitting and predictions: On a marginal likelihood approach to linear regression and errors-in-variables models

    Science.gov (United States)

    Christiansen, Bo

    2015-04-01

    Linear regression methods are without doubt the most used approaches to describe and predict data in the physical sciences. They are often good first order approximations and they are in general easier to apply and interpret than more advanced methods. However, even the properties of univariate regression can lead to debate over the appropriateness of various models as witnessed by the recent discussion about climate reconstruction methods. Before linear regression is applied important choices have to be made regarding the origins of the noise terms and regarding which of the two variables under consideration that should be treated as the independent variable. These decisions are often not easy to make but they may have a considerable impact on the results. We seek to give a unified probabilistic - Bayesian with flat priors - treatment of univariate linear regression and prediction by taking, as starting point, the general errors-in-variables model (Christiansen, J. Clim., 27, 2014-2031, 2014). Other versions of linear regression can be obtained as limits of this model. We derive the likelihood of the model parameters and predictands of the general errors-in-variables model by marginalizing over the nuisance parameters. The resulting likelihood is relatively simple and easy to analyze and calculate. The well known unidentifiability of the errors-in-variables model is manifested as the absence of a well-defined maximum in the likelihood. However, this does not mean that probabilistic inference can not be made; the marginal likelihoods of model parameters and the predictands have, in general, well-defined maxima. We also include a probabilistic version of classical calibration and show how it is related to the errors-in-variables model. The results are illustrated by an example from the coupling between the lower stratosphere and the troposphere in the Northern Hemisphere winter.

  4. Bacterial clonal diagnostics as a tool for evidence-based empiric antibiotic selection.

    Directory of Open Access Journals (Sweden)

    Veronika Tchesnokova

    Full Text Available Despite the known clonal distribution of antibiotic resistance in many bacteria, empiric (pre-culture antibiotic selection still relies heavily on species-level cumulative antibiograms, resulting in overuse of broad-spectrum agents and excessive antibiotic/pathogen mismatch. Urinary tract infections (UTIs, which account for a large share of antibiotic use, are caused predominantly by Escherichia coli, a highly clonal pathogen. In an observational clinical cohort study of urgent care patients with suspected UTI, we assessed the potential for E. coli clonal-level antibiograms to improve empiric antibiotic selection. A novel PCR-based clonotyping assay was applied to fresh urine samples to rapidly detect E. coli and the urine strain's clonotype. Based on a database of clonotype-specific antibiograms, the acceptability of various antibiotics for empiric therapy was inferred using a 20%, 10%, and 30% allowed resistance threshold. The test's performance characteristics and possible effects on prescribing were assessed. The rapid test identified E. coli clonotypes directly in patients' urine within 25-35 minutes, with high specificity and sensitivity compared to culture. Antibiotic selection based on a clonotype-specific antibiogram could reduce the relative likelihood of antibiotic/pathogen mismatch by ≥ 60%. Compared to observed prescribing patterns, clonal diagnostics-guided antibiotic selection could safely double the use of trimethoprim/sulfamethoxazole and minimize fluoroquinolone use. In summary, a rapid clonotyping test showed promise for improving empiric antibiotic prescribing for E. coli UTI, including reversing preferential use of fluoroquinolones over trimethoprim/sulfamethoxazole. The clonal diagnostics approach merges epidemiologic surveillance, antimicrobial stewardship, and molecular diagnostics to bring evidence-based medicine directly to the point of care.

  5. Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings

    Science.gov (United States)

    Wang, Dong; Zhao, Yang; Yi, Cai; Tsui, Kwok-Leung; Lin, Jianhui

    2018-02-01

    Rolling element bearings are widely used in various industrial machines, such as electric motors, generators, pumps, gearboxes, railway axles, turbines, and helicopter transmissions. Fault diagnosis of rolling element bearings is beneficial to preventing any unexpected accident and reducing economic loss. In the past years, many bearing fault detection methods have been developed. Recently, a new adaptive signal processing method called empirical wavelet transform attracts much attention from readers and engineers and its applications to bearing fault diagnosis have been reported. The main problem of empirical wavelet transform is that Fourier segments required in empirical wavelet transform are strongly dependent on the local maxima of the amplitudes of the Fourier spectrum of a signal, which connotes that Fourier segments are not always reliable and effective if the Fourier spectrum of the signal is complicated and overwhelmed by heavy noises and other strong vibration components. In this paper, sparsity guided empirical wavelet transform is proposed to automatically establish Fourier segments required in empirical wavelet transform for fault diagnosis of rolling element bearings. Industrial bearing fault signals caused by single and multiple railway axle bearing defects are used to verify the effectiveness of the proposed sparsity guided empirical wavelet transform. Results show that the proposed method can automatically discover Fourier segments required in empirical wavelet transform and reveal single and multiple railway axle bearing defects. Besides, some comparisons with three popular signal processing methods including ensemble empirical mode decomposition, the fast kurtogram and the fast spectral correlation are conducted to highlight the superiority of the proposed method.

  6. Brief Communication: Likelihood of societal preparedness for global change: trend detection

    Directory of Open Access Journals (Sweden)

    R. M. Vogel

    2013-07-01

    Full Text Available Anthropogenic influences on earth system processes are now pervasive, resulting in trends in river discharge, pollution levels, ocean levels, precipitation, temperature, wind, landslides, bird and plant populations and a myriad of other important natural hazards relating to earth system state variables. Thousands of trend detection studies have been published which report the statistical significance of observed trends. Unfortunately, such studies only concentrate on the null hypothesis of "no trend". Little or no attention is given to the power of such statistical trend tests, which would quantify the likelihood that we might ignore a trend if it really existed. The probability of missing the trend, if it exists, known as the type II error, informs us about the likelihood of whether or not society is prepared to accommodate and respond to such trends. We describe how the power or probability of detecting a trend if it exists, depends critically on our ability to develop improved multivariate deterministic and statistical methods for predicting future trends in earth system processes. Several other research and policy implications for improving our understanding of trend detection and our societal response to those trends are discussed.

  7. Susceptibility, likelihood to be diagnosed, worry and fear for contracting Lyme disease.

    Science.gov (United States)

    Fogel, Joshua; Chawla, Gurasees S

    Risk perception and psychological concerns are relevant for understanding how people view Lyme disease. This study investigates the four separate outcomes of susceptibility, likelihood to be diagnosed, worry, and fear for contracting Lyme disease. University students (n=713) were surveyed about demographics, perceived health, Lyme disease knowledge, Lyme disease preventive behaviors, Lyme disease history, and Lyme disease miscellaneous variables. We found that women were associated with increased susceptibility and fear. Asian/Asian-American race/ethnicity was associated with increased worry and fear. Perceived good health was associated with increased likelihood to be diagnosed, worry, and fear. Correct knowledge was associated with increased susceptibility and likelihood to be diagnosed. Those who typically spend a lot of time outdoors were associated with increased susceptibility, likelihood to be diagnosed, worry, and fear. In conclusion, healthcare providers and public health campaigns should address susceptibility, likelihood to be diagnosed, worry, and fear about Lyme disease, and should particularly target women and Asians/Asian-Americans to address any possible misconceptions and/or offer effective coping strategies. Copyright © 2016 King Saud Bin Abdulaziz University for Health Sciences. Published by Elsevier Ltd. All rights reserved.

  8. An Iterative Maximum a Posteriori Estimation of Proficiency Level to Detect Multiple Local Likelihood Maxima

    Science.gov (United States)

    Magis, David; Raiche, Gilles

    2010-01-01

    In this article the authors focus on the issue of the nonuniqueness of the maximum likelihood (ML) estimator of proficiency level in item response theory (with special attention to logistic models). The usual maximum a posteriori (MAP) method offers a good alternative within that framework; however, this article highlights some drawbacks of its…

  9. The Location-Scale Mixture Exponential Power Distribution: A Bayesian and Maximum Likelihood Approach

    Directory of Open Access Journals (Sweden)

    Z. Rahnamaei

    2012-01-01

    Full Text Available We introduce an alternative skew-slash distribution by using the scale mixture of the exponential power distribution. We derive the properties of this distribution and estimate its parameter by Maximum Likelihood and Bayesian methods. By a simulation study we compute the mentioned estimators and their mean square errors, and we provide an example on real data to demonstrate the modeling strength of the new distribution.

  10. The effect of empirical potential functions on modeling of amorphous carbon using molecular dynamics method

    International Nuclear Information System (INIS)

    Li, Longqiu; Xu, Ming; Song, Wenping; Ovcharenko, Andrey; Zhang, Guangyu; Jia, Ding

    2013-01-01

    Empirical potentials have a strong effect on the hybridization and structure of amorphous carbon and are of great importance in molecular dynamics (MD) simulations. In this work, amorphous carbon at densities ranging from 2.0 to 3.2 g/cm 3 was modeled by a liquid quenching method using Tersoff, 2nd REBO, and ReaxFF empirical potentials. The hybridization, structure and radial distribution function G(r) of carbon atoms were analyzed as a function of the three potentials mentioned above. The ReaxFF potential is capable to model the change of the structure of amorphous carbon and MD results are in a good agreement with experimental results and density function theory (DFT) at low density of 2.6 g/cm 3 and below. The 2nd REBO potential can be used when amorphous carbon has a very low density of 2.4 g/cm 3 and below. Considering the computational efficiency, the Tersoff potential is recommended to model amorphous carbon at a high density of 2.6 g/cm 3 and above. In addition, the influence of the quenching time on the hybridization content obtained with the three potentials is discussed.

  11. Steady state likelihood ratio sensitivity analysis for stiff kinetic Monte Carlo simulations.

    Science.gov (United States)

    Núñez, M; Vlachos, D G

    2015-01-28

    Kinetic Monte Carlo simulation is an integral tool in the study of complex physical phenomena present in applications ranging from heterogeneous catalysis to biological systems to crystal growth and atmospheric sciences. Sensitivity analysis is useful for identifying important parameters and rate-determining steps, but the finite-difference application of sensitivity analysis is computationally demanding. Techniques based on the likelihood ratio method reduce the computational cost of sensitivity analysis by obtaining all gradient information in a single run. However, we show that disparity in time scales of microscopic events, which is ubiquitous in real systems, introduces drastic statistical noise into derivative estimates for parameters affecting the fast events. In this work, the steady-state likelihood ratio sensitivity analysis is extended to singularly perturbed systems by invoking partial equilibration for fast reactions, that is, by working on the fast and slow manifolds of the chemistry. Derivatives on each time scale are computed independently and combined to the desired sensitivity coefficients to considerably reduce the noise in derivative estimates for stiff systems. The approach is demonstrated in an analytically solvable linear system.

  12. Maximum-likelihood estimation of recent shared ancestry (ERSA).

    Science.gov (United States)

    Huff, Chad D; Witherspoon, David J; Simonson, Tatum S; Xing, Jinchuan; Watkins, W Scott; Zhang, Yuhua; Tuohy, Therese M; Neklason, Deborah W; Burt, Randall W; Guthery, Stephen L; Woodward, Scott R; Jorde, Lynn B

    2011-05-01

    Accurate estimation of recent shared ancestry is important for genetics, evolution, medicine, conservation biology, and forensics. Established methods estimate kinship accurately for first-degree through third-degree relatives. We demonstrate that chromosomal segments shared by two individuals due to identity by descent (IBD) provide much additional information about shared ancestry. We developed a maximum-likelihood method for the estimation of recent shared ancestry (ERSA) from the number and lengths of IBD segments derived from high-density SNP or whole-genome sequence data. We used ERSA to estimate relationships from SNP genotypes in 169 individuals from three large, well-defined human pedigrees. ERSA is accurate to within one degree of relationship for 97% of first-degree through fifth-degree relatives and 80% of sixth-degree and seventh-degree relatives. We demonstrate that ERSA's statistical power approaches the maximum theoretical limit imposed by the fact that distant relatives frequently share no DNA through a common ancestor. ERSA greatly expands the range of relationships that can be estimated from genetic data and is implemented in a freely available software package.

  13. Dimension-independent likelihood-informed MCMC

    KAUST Repository

    Cui, Tiangang

    2015-10-08

    Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters that represent the discretization of an underlying function. This work introduces a family of Markov chain Monte Carlo (MCMC) samplers that can adapt to the particular structure of a posterior distribution over functions. Two distinct lines of research intersect in the methods developed here. First, we introduce a general class of operator-weighted proposal distributions that are well defined on function space, such that the performance of the resulting MCMC samplers is independent of the discretization of the function. Second, by exploiting local Hessian information and any associated low-dimensional structure in the change from prior to posterior distributions, we develop an inhomogeneous discretization scheme for the Langevin stochastic differential equation that yields operator-weighted proposals adapted to the non-Gaussian structure of the posterior. The resulting dimension-independent and likelihood-informed (DILI) MCMC samplers may be useful for a large class of high-dimensional problems where the target probability measure has a density with respect to a Gaussian reference measure. Two nonlinear inverse problems are used to demonstrate the efficiency of these DILI samplers: an elliptic PDE coefficient inverse problem and path reconstruction in a conditioned diffusion.

  14. Dimension-independent likelihood-informed MCMC

    KAUST Repository

    Cui, Tiangang; Law, Kody; Marzouk, Youssef M.

    2015-01-01

    Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters that represent the discretization of an underlying function. This work introduces a family of Markov chain Monte Carlo (MCMC) samplers that can adapt to the particular structure of a posterior distribution over functions. Two distinct lines of research intersect in the methods developed here. First, we introduce a general class of operator-weighted proposal distributions that are well defined on function space, such that the performance of the resulting MCMC samplers is independent of the discretization of the function. Second, by exploiting local Hessian information and any associated low-dimensional structure in the change from prior to posterior distributions, we develop an inhomogeneous discretization scheme for the Langevin stochastic differential equation that yields operator-weighted proposals adapted to the non-Gaussian structure of the posterior. The resulting dimension-independent and likelihood-informed (DILI) MCMC samplers may be useful for a large class of high-dimensional problems where the target probability measure has a density with respect to a Gaussian reference measure. Two nonlinear inverse problems are used to demonstrate the efficiency of these DILI samplers: an elliptic PDE coefficient inverse problem and path reconstruction in a conditioned diffusion.

  15. The impact of organizational factors on-business adoption: An empirical analysis

    Directory of Open Access Journals (Sweden)

    Marta García-Moreno

    2018-06-01

    Full Text Available Purpose: Provide empirical validation of the model developed by García Moreno et al. (2016 on the factors influencing the adoption of e-business in firms. Design/methodology/approach: Consideration is given to the method for measuring each one of the variables included in the model. Use has been made of the e-Business Watch database, which contains measures for the theoretical model’s three categories: firm, technology, and environment. Multinomial logistic regression models have been provided. Findings: The variables included have revealed significant statistical relationships for the model in question, although the intensity of the relationships differs. the variables related to the environment also reveal statistically significant relationships, whereby the attitude of trading partners appears to have a relevant and growing impact on e-business adoption. Research limitations/implications: Data come from just one database: the e-Business Watch database/enriched data from alternative databases could be included. Practical implications: The infrastructure of information and communications technologies (ICTs is confirmed to be a determining factor in e-business development. Nevertheless, the effect of competitor rivalry has a more erratic influence that is encapsulated in a significant relationship in intermediate models, with a sharper increase in the likelihood of being in the category of customer-focused firms, and less internally focused. Social implications: The human capital linked to ICTs is a driving force behind the adoption of these practices. Albeit with a more moderate effect, note should also be taken of the capacity for entering into relationships with third parties within the scope of ICTs, with significant effects that become more robust as they are tested in models that seek to explain the probability of recording higher levels of e-business adoption. Originality/value: The article presents a first empirical analysis to

  16. Probabilistic Requirements (Partial) Verification Methods Best Practices Improvement. Variables Acceptance Sampling Calculators: Empirical Testing. Volume 2

    Science.gov (United States)

    Johnson, Kenneth L.; White, K. Preston, Jr.

    2012-01-01

    The NASA Engineering and Safety Center was requested to improve on the Best Practices document produced for the NESC assessment, Verification of Probabilistic Requirements for the Constellation Program, by giving a recommended procedure for using acceptance sampling by variables techniques as an alternative to the potentially resource-intensive acceptance sampling by attributes method given in the document. In this paper, the results of empirical tests intended to assess the accuracy of acceptance sampling plan calculators implemented for six variable distributions are presented.

  17. Comparison of least-squares vs. maximum likelihood estimation for standard spectrum technique of β−γ coincidence spectrum analysis

    International Nuclear Information System (INIS)

    Lowrey, Justin D.; Biegalski, Steven R.F.

    2012-01-01

    The spectrum deconvolution analysis tool (SDAT) software code was written and tested at The University of Texas at Austin utilizing the standard spectrum technique to determine activity levels of Xe-131m, Xe-133m, Xe-133, and Xe-135 in β–γ coincidence spectra. SDAT was originally written to utilize the method of least-squares to calculate the activity of each radionuclide component in the spectrum. Recently, maximum likelihood estimation was also incorporated into the SDAT tool. This is a robust statistical technique to determine the parameters that maximize the Poisson distribution likelihood function of the sample data. In this case it is used to parameterize the activity level of each of the radioxenon components in the spectra. A new test dataset was constructed utilizing Xe-131m placed on a Xe-133 background to compare the robustness of the least-squares and maximum likelihood estimation methods for low counting statistics data. The Xe-131m spectra were collected independently from the Xe-133 spectra and added to generate the spectra in the test dataset. The true independent counts of Xe-131m and Xe-133 are known, as they were calculated before the spectra were added together. Spectra with both high and low counting statistics are analyzed. Studies are also performed by analyzing only the 30 keV X-ray region of the β–γ coincidence spectra. Results show that maximum likelihood estimation slightly outperforms least-squares for low counting statistics data.

  18. The emotion of compassion and the likelihood of its expression in nursing practice.

    Science.gov (United States)

    Newham, Roger Alan

    2017-07-01

    Philosophical and empirical work on the nature of the emotions is extensive, and there are many theories of emotions. However, all agree that emotions are not knee jerk reactions to stimuli and are open to rational assessment or warrant. This paper's focus is on the condition or conditions for compassion as an emotion and the likelihood that it or they can be met in nursing practice. Thus, it is attempting to keep, as far as possible, compassion as an emotion separate from both moral norms and professional norms. This is because empirical or causal conditions that can make experiencing and acting out of compassion difficult seem especially relevant in nursing practice. I consider how theories of emotion in general and of compassion in particular are somewhat contested, but all recent accounts agree that emotions are not totally immune to reason. Then, using accounts of constitutive conditions of the emotion of compassion, I will show how they are often likely to be quite fragile or unstable in practice and particularly so within much nursing practice. In addition, some of the conditions for compassion will be shown to be problematic for nursing practice. It is difficult to keep ideas of compassion separate from morality, and this connection is noticeable in the claims made of compassion for nursing and so I will briefly highlight one such connection that of the need for normative theory to give an account of the value that emotions such as compassion presume and that compassionate motivation is separate from moral motivation and may conflict with it. The fragility or instability of the emotion of compassion in practice has implications for both what can be expected and what should be expected of compassion; at least if what is wanted is a realist rather than idealist account of "should." © 2016 John Wiley & Sons Ltd.

  19. Nearly Efficient Likelihood Ratio Tests for Seasonal Unit Roots

    DEFF Research Database (Denmark)

    Jansson, Michael; Nielsen, Morten Ørregaard

    In an important generalization of zero frequency autore- gressive unit root tests, Hylleberg, Engle, Granger, and Yoo (1990) developed regression-based tests for unit roots at the seasonal frequencies in quarterly time series. We develop likelihood ratio tests for seasonal unit roots and show...... that these tests are "nearly efficient" in the sense of Elliott, Rothenberg, and Stock (1996), i.e. that their local asymptotic power functions are indistinguishable from the Gaussian power envelope. Currently available nearly efficient testing procedures for seasonal unit roots are regression-based and require...... the choice of a GLS detrending parameter, which our likelihood ratio tests do not....

  20. PIRPLE: a penalized-likelihood framework for incorporation of prior images in CT reconstruction

    International Nuclear Information System (INIS)

    Stayman, J Webster; Dang, Hao; Ding, Yifu; Siewerdsen, Jeffrey H

    2013-01-01

    Over the course of diagnosis and treatment, it is common for a number of imaging studies to be acquired. Such imaging sequences can provide substantial patient-specific prior knowledge about the anatomy that can be incorporated into a prior-image-based tomographic reconstruction for improved image quality and better dose utilization. We present a general methodology using a model-based reconstruction approach including formulations of the measurement noise that also integrates prior images. This penalized-likelihood technique adopts a sparsity enforcing penalty that incorporates prior information yet allows for change between the current reconstruction and the prior image. Moreover, since prior images are generally not registered with the current image volume, we present a modified model-based approach that seeks a joint registration of the prior image in addition to the reconstruction of projection data. We demonstrate that the combined prior-image- and model-based technique outperforms methods that ignore the prior data or lack a noise model. Moreover, we demonstrate the importance of registration for prior-image-based reconstruction methods and show that the prior-image-registered penalized-likelihood estimation (PIRPLE) approach can maintain a high level of image quality in the presence of noisy and undersampled projection data. (paper)

  1. Transition States from Empirical Force Fields

    DEFF Research Database (Denmark)

    Jensen, Frank; Norrby, Per-Ola

    2003-01-01

    This is an overview of the use of empirical force fields in the study of reaction mechanisms. EVB-type methods (including RFF and MCMM) produce full reaction surfaces by mixing, in the simplest case, known force fields describing reactants and products. The SEAM method instead locates approximate...

  2. Surface Passivation in Empirical Tight Binding

    OpenAIRE

    He, Yu; Tan, Yaohua; Jiang, Zhengping; Povolotskyi, Michael; Klimeck, Gerhard; Kubis, Tillmann

    2015-01-01

    Empirical Tight Binding (TB) methods are widely used in atomistic device simulations. Existing TB methods to passivate dangling bonds fall into two categories: 1) Method that explicitly includes passivation atoms is limited to passivation with atoms and small molecules only. 2) Method that implicitly incorporates passivation does not distinguish passivation atom types. This work introduces an implicit passivation method that is applicable to any passivation scenario with appropriate parameter...

  3. DREAM3: network inference using dynamic context likelihood of relatedness and the inferelator.

    Directory of Open Access Journals (Sweden)

    Aviv Madar

    2010-03-01

    Full Text Available Many current works aiming to learn regulatory networks from systems biology data must balance model complexity with respect to data availability and quality. Methods that learn regulatory associations based on unit-less metrics, such as Mutual Information, are attractive in that they scale well and reduce the number of free parameters (model complexity per interaction to a minimum. In contrast, methods for learning regulatory networks based on explicit dynamical models are more complex and scale less gracefully, but are attractive as they may allow direct prediction of transcriptional dynamics and resolve the directionality of many regulatory interactions.We aim to investigate whether scalable information based methods (like the Context Likelihood of Relatedness method and more explicit dynamical models (like Inferelator 1.0 prove synergistic when combined. We test a pipeline where a novel modification of the Context Likelihood of Relatedness (mixed-CLR, modified to use time series data is first used to define likely regulatory interactions and then Inferelator 1.0 is used for final model selection and to build an explicit dynamical model.Our method ranked 2nd out of 22 in the DREAM3 100-gene in silico networks challenge. Mixed-CLR and Inferelator 1.0 are complementary, demonstrating a large performance gain relative to any single tested method, with precision being especially high at low recall values. Partitioning the provided data set into four groups (knock-down, knock-out, time-series, and combined revealed that using comprehensive knock-out data alone provides optimal performance. Inferelator 1.0 proved particularly powerful at resolving the directionality of regulatory interactions, i.e. "who regulates who" (approximately of identified true positives were correctly resolved. Performance drops for high in-degree genes, i.e. as the number of regulators per target gene increases, but not with out-degree, i.e. performance is not affected by

  4. Using empirical Bayes predictors from generalized linear mixed models to test and visualize associations among longitudinal outcomes.

    Science.gov (United States)

    Mikulich-Gilbertson, Susan K; Wagner, Brandie D; Grunwald, Gary K; Riggs, Paula D; Zerbe, Gary O

    2018-01-01

    Medical research is often designed to investigate changes in a collection of response variables that are measured repeatedly on the same subjects. The multivariate generalized linear mixed model (MGLMM) can be used to evaluate random coefficient associations (e.g. simple correlations, partial regression coefficients) among outcomes that may be non-normal and differently distributed by specifying a multivariate normal distribution for their random effects and then evaluating the latent relationship between them. Empirical Bayes predictors are readily available for each subject from any mixed model and are observable and hence, plotable. Here, we evaluate whether second-stage association analyses of empirical Bayes predictors from a MGLMM, provide a good approximation and visual representation of these latent association analyses using medical examples and simulations. Additionally, we compare these results with association analyses of empirical Bayes predictors generated from separate mixed models for each outcome, a procedure that could circumvent computational problems that arise when the dimension of the joint covariance matrix of random effects is large and prohibits estimation of latent associations. As has been shown in other analytic contexts, the p-values for all second-stage coefficients that were determined by naively assuming normality of empirical Bayes predictors provide a good approximation to p-values determined via permutation analysis. Analyzing outcomes that are interrelated with separate models in the first stage and then associating the resulting empirical Bayes predictors in a second stage results in different mean and covariance parameter estimates from the maximum likelihood estimates generated by a MGLMM. The potential for erroneous inference from using results from these separate models increases as the magnitude of the association among the outcomes increases. Thus if computable, scatterplots of the conditionally independent empirical Bayes

  5. Clarification of the use of chi-square and likelihood functions in fits to histograms

    International Nuclear Information System (INIS)

    Baker, S.; Cousins, R.D.

    1984-01-01

    We consider the problem of fitting curves to histograms in which the data obey multinomial or Poisson statistics. Techniques commonly used by physicists are examined in light of standard results found in the statistics literature. We review the relationship between multinomial and Poisson distributions, and clarify a sufficient condition for equality of the area under the fitted curve and the number of events on the histogram. Following the statisticians, we use the likelihood ratio test to construct a general Z 2 statistic, Zsub(lambda) 2 , which yields parameter and error estimates identical to those of the method of maximum likelihood. The Zsub(lambda) 2 statistic is further useful for testing goodness-of-fit since the value of its minimum asymptotically obeys a classical chi-square distribution. One should be aware, however, of the potential for statistical bias, especially when the number of events is small. (orig.)

  6. Empirical methods for the estimation of Southern Ocean CO2: support vector and random forest regression

    CSIR Research Space (South Africa)

    Gregor, Luke

    2017-12-01

    Full Text Available understanding with spatially integrated air–sea flux estimates (Fay and McKinley, 2014). Conversely, ocean biogeochemical process models are good tools for mechanis- tic understanding, but fail to represent the seasonality of CO2 fluxes in the Southern Ocean... of including coordinate variables as proxies of 1pCO2 in the empirical methods. In the inter- comparison study by Rödenbeck et al. (2015) proxies typi- cally include, but are not limited to, sea surface temperature (SST), chlorophyll a (Chl a), mixed layer...

  7. Neutron spectra unfolding with maximum entropy and maximum likelihood

    International Nuclear Information System (INIS)

    Itoh, Shikoh; Tsunoda, Toshiharu

    1989-01-01

    A new unfolding theory has been established on the basis of the maximum entropy principle and the maximum likelihood method. This theory correctly embodies the Poisson statistics of neutron detection, and always brings a positive solution over the whole energy range. Moreover, the theory unifies both problems of overdetermined and of underdetermined. For the latter, the ambiguity in assigning a prior probability, i.e. the initial guess in the Bayesian sense, has become extinct by virtue of the principle. An approximate expression of the covariance matrix for the resultant spectra is also presented. An efficient algorithm to solve the nonlinear system, which appears in the present study, has been established. Results of computer simulation showed the effectiveness of the present theory. (author)

  8. Factors influencing the stay-exit intention of small livestock farmers: empirical evidence from southern Chile

    Directory of Open Access Journals (Sweden)

    Luis M. Carter-Leal

    2018-04-01

    Full Text Available This study analyses the factors driving the stay-exit intention of small livestock farmers located in southern Chile. Technical, economic, and social characteristics from 212 farmers were included in this study. Through an empirical probit model we identified the variables that should be considered when developing rural policies aimed at increasing the likelihood to stay in farming. The results showed that 12 out of the 30 parameters were significant (p<0.10, with an extremely good fit of the model (McFadden pseudo-R2 = 0.25, Count R2 = 75.9%. Particularly, ‘female farmer’, ‘positive expectation about future farming life’, ‘capacity of farm income to cover the expenses of the whole family’, ‘mixed production’, ‘participation in an association’, and ‘distance to the nearest city’ were positively associated with the stay intention. Moreover, our study also indicates that ‘existence of a defined retirement age’, ‘existence of a defined sale price for the farm’, ‘a mixed farm focused on livestock production’, ‘the possibility to make own decisions’, ‘age squared’, and the ‘number of people living at the farm’ were negatively associated with the stay intention. Our empirical findings suggest that farmer characteristics (gender, family size, the farming system (multi-activity production, efficiency, and social aspects of the rural society (associations, protection of agricultural products are also important aspects that should be considered by rural development policies aimed at improving the likelihood of staying, in addition to the technical characteristics of the farming which have been traditionally addressed in developing countries.

  9. Supervisor Autonomy and Considerate Leadership Style are Associated with Supervisors’ Likelihood to Accommodate Back Injured Workers

    Science.gov (United States)

    McGuire, Connor; Kristman, Vicki L; Williams-Whitt, Kelly; Reguly, Paula; Shaw, William; Soklaridis, Sophie

    2015-01-01

    PURPOSE To determine the association between supervisors’ leadership style and autonomy and supervisors’ likelihood of supporting job accommodations for back-injured workers. METHODS A cross-sectional study of supervisors from Canadian and US employers was conducted using a web-based, self-report questionnaire that included a case vignette of a back-injured worker. Autonomy and two dimensions of leadership style (considerate and initiating structure) were included as exposures. The outcome, supervisors’ likeliness to support job accommodation, was measured with the Job Accommodation Scale. We conducted univariate analyses of all variables and bivariate analyses of the JAS score with each exposure and potential confounding factor. We used multivariable generalized linear models to control for confounding factors. RESULTS A total of 796 supervisors participated. Considerate leadership style (β= .012; 95% CI: .009–.016) and autonomy (β= .066; 95% CI: .025–.11) were positively associated with supervisors’ likelihood to accommodate after adjusting for appropriate confounding factors. An initiating structure leadership style was not significantly associated with supervisors’ likelihood to accommodate (β = .0018; 95% CI: −.0026–.0061) after adjusting for appropriate confounders. CONCLUSIONS Autonomy and a considerate leadership style were positively associated with supervisors’ likelihood to accommodate a back-injured worker. Providing supervisors with more autonomy over decisions of accommodation and developing their considerate leadership style may aid in increasing work accommodation for back-injured workers and preventing prolonged work disability. PMID:25595332

  10. Parallelization of maximum likelihood fits with OpenMP and CUDA

    CERN Document Server

    Jarp, S; Leduc, J; Nowak, A; Pantaleo, F

    2011-01-01

    Data analyses based on maximum likelihood fits are commonly used in the high energy physics community for fitting statistical models to data samples. This technique requires the numerical minimization of the negative log-likelihood function. MINUIT is the most common package used for this purpose in the high energy physics community. The main algorithm in this package, MIGRAD, searches the minimum by using the gradient information. The procedure requires several evaluations of the function, depending on the number of free parameters and their initial values. The whole procedure can be very CPU-time consuming in case of complex functions, with several free parameters, many independent variables and large data samples. Therefore, it becomes particularly important to speed-up the evaluation of the negative log-likelihood function. In this paper we present an algorithm and its implementation which benefits from data vectorization and parallelization (based on OpenMP) and which was also ported to Graphics Processi...

  11. A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain-computer interface

    Science.gov (United States)

    Chen, Yi-Feng; Atal, Kiran; Xie, Sheng-Quan; Liu, Quan

    2017-08-01

    Objective. Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain-computer interface (BCI) applications. Approach. Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts. This paper presents an improved method to detect the frequency component associated with SSVEP using multivariate empirical mode decomposition (MEMD) and CCA (MEMD-CCA). EEG signals from nine healthy volunteers were recorded to evaluate the performance of the proposed method for SSVEP recognition. Main results. We compared our method with CCA and temporally local multivariate synchronization index (TMSI). The results suggest that the MEMD-CCA achieved significantly higher accuracy in contrast to standard CCA and TMSI. It gave the improvements of 1.34%, 3.11%, 3.33%, 10.45%, 15.78%, 18.45%, 15.00% and 14.22% on average over CCA at time windows from 0.5 s to 5 s and 0.55%, 1.56%, 7.78%, 14.67%, 13.67%, 7.33% and 7.78% over TMSI from 0.75 s to 5 s. The method outperformed the filter-based decomposition (FB), empirical mode decomposition (EMD) and wavelet decomposition (WT) based CCA for SSVEP recognition. Significance. The results demonstrate the ability of our proposed MEMD-CCA to improve the performance of SSVEP-based BCI.

  12. Likelihood inference of non-constant diversification rates with incomplete taxon sampling.

    Directory of Open Access Journals (Sweden)

    Sebastian Höhna

    Full Text Available Large-scale phylogenies provide a valuable source to study background diversification rates and investigate if the rates have changed over time. Unfortunately most large-scale, dated phylogenies are sparsely sampled (fewer than 5% of the described species and taxon sampling is not uniform. Instead, taxa are frequently sampled to obtain at least one representative per subgroup (e.g. family and thus to maximize diversity (diversified sampling. So far, such complications have been ignored, potentially biasing the conclusions that have been reached. In this study I derive the likelihood of a birth-death process with non-constant (time-dependent diversification rates and diversified taxon sampling. Using simulations I test if the true parameters and the sampling method can be recovered when the trees are small or medium sized (fewer than 200 taxa. The results show that the diversification rates can be inferred and the estimates are unbiased for large trees but are biased for small trees (fewer than 50 taxa. Furthermore, model selection by means of Akaike's Information Criterion favors the true model if the true rates differ sufficiently from alternative models (e.g. the birth-death model is recovered if the extinction rate is large and compared to a pure-birth model. Finally, I applied six different diversification rate models--ranging from a constant-rate pure birth process to a decreasing speciation rate birth-death process but excluding any rate shift models--on three large-scale empirical phylogenies (ants, mammals and snakes with respectively 149, 164 and 41 sampled species. All three phylogenies were constructed by diversified taxon sampling, as stated by the authors. However only the snake phylogeny supported diversified taxon sampling. Moreover, a parametric bootstrap test revealed that none of the tested models provided a good fit to the observed data. The model assumptions, such as homogeneous rates across species or no rate shifts, appear

  13. Highly comparative time-series analysis: the empirical structure of time series and their methods.

    Science.gov (United States)

    Fulcher, Ben D; Little, Max A; Jones, Nick S

    2013-06-06

    The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

  14. Evaluating score- and feature-based likelihood ratio models for multivariate continuous data: applied to forensic MDMA comparison

    NARCIS (Netherlands)

    Bolck, A.; Ni, H.; Lopatka, M.

    2015-01-01

    Likelihood ratio (LR) models are moving into the forefront of forensic evidence evaluation as these methods are adopted by a diverse range of application areas in forensic science. We examine the fundamentally different results that can be achieved when feature- and score-based methodologies are

  15. Unbinned likelihood maximisation framework for neutrino clustering in Python

    Energy Technology Data Exchange (ETDEWEB)

    Coenders, Stefan [Technische Universitaet Muenchen, Boltzmannstr. 2, 85748 Garching (Germany)

    2016-07-01

    Albeit having detected an astrophysical neutrino flux with IceCube, sources of astrophysical neutrinos remain hidden up to now. A detection of a neutrino point source is a smoking gun for hadronic processes and acceleration of cosmic rays. The search for neutrino sources has many degrees of freedom, for example steady versus transient, point-like versus extended sources, et cetera. Here, we introduce a Python framework designed for unbinned likelihood maximisations as used in searches for neutrino point sources by IceCube. Implementing source scenarios in a modular way, likelihood searches on various kinds can be implemented in a user-friendly way, without sacrificing speed and memory management.

  16. Risk Assessment Using the Three Dimensions of Probability (Likelihood), Severity, and Level of Control

    Science.gov (United States)

    Watson, Clifford C.

    2011-01-01

    Traditional hazard analysis techniques utilize a two-dimensional representation of the results determined by relative likelihood and severity of the residual risk. These matrices present a quick-look at the Likelihood (Y-axis) and Severity (X-axis) of the probable outcome of a hazardous event. A three-dimensional method, described herein, utilizes the traditional X and Y axes, while adding a new, third dimension, shown as the Z-axis, and referred to as the Level of Control. The elements of the Z-axis are modifications of the Hazard Elimination and Control steps (also known as the Hazard Reduction Precedence Sequence). These steps are: 1. Eliminate risk through design. 2. Substitute less risky materials for more hazardous materials. 3. Install safety devices. 4. Install caution and warning devices. 5. Develop administrative controls (to include special procedures and training.) 6. Provide protective clothing and equipment. When added to the two-dimensional models, the level of control adds a visual representation of the risk associated with the hazardous condition, creating a tall-pole for the least-well-controlled failure while establishing the relative likelihood and severity of all causes and effects for an identified hazard. Computer modeling of the analytical results, using spreadsheets and three-dimensional charting gives a visual confirmation of the relationship between causes and their controls.

  17. Dimension-Independent Likelihood-Informed MCMC

    KAUST Repository

    Cui, Tiangang; Law, Kody; Marzouk, Youssef

    2015-01-01

    Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters, which in principle can be described as functions. By exploiting low-dimensional structure in the change from prior to posterior [distributions], we introduce a suite of MCMC samplers that can adapt to the complex structure of the posterior distribution, yet are well-defined on function space. Posterior sampling in nonlinear inverse problems arising from various partial di erential equations and also a stochastic differential equation are used to demonstrate the e ciency of these dimension-independent likelihood-informed samplers.

  18. Dimension-Independent Likelihood-Informed MCMC

    KAUST Repository

    Cui, Tiangang

    2015-01-07

    Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters, which in principle can be described as functions. By exploiting low-dimensional structure in the change from prior to posterior [distributions], we introduce a suite of MCMC samplers that can adapt to the complex structure of the posterior distribution, yet are well-defined on function space. Posterior sampling in nonlinear inverse problems arising from various partial di erential equations and also a stochastic differential equation are used to demonstrate the e ciency of these dimension-independent likelihood-informed samplers.

  19. Nearly Efficient Likelihood Ratio Tests of the Unit Root Hypothesis

    DEFF Research Database (Denmark)

    Jansson, Michael; Nielsen, Morten Ørregaard

    Seemingly absent from the arsenal of currently available "nearly efficient" testing procedures for the unit root hypothesis, i.e. tests whose local asymptotic power functions are indistinguishable from the Gaussian power envelope, is a test admitting a (quasi-)likelihood ratio interpretation. We...... show that the likelihood ratio unit root test derived in a Gaussian AR(1) model with standard normal innovations is nearly efficient in that model. Moreover, these desirable properties carry over to more complicated models allowing for serially correlated and/or non-Gaussian innovations....

  20. Empirical psychology, common sense, and Kant's empirical markers for moral responsibility.

    Science.gov (United States)

    Frierson, Patrick

    2008-12-01

    This paper explains the empirical markers by which Kant thinks that one can identify moral responsibility. After explaining the problem of discerning such markers within a Kantian framework I briefly explain Kant's empirical psychology. I then argue that Kant's empirical markers for moral responsibility--linked to higher faculties of cognition--are not sufficient conditions for moral responsibility, primarily because they are empirical characteristics subject to natural laws. Next. I argue that these markers are not necessary conditions of moral responsibility. Given Kant's transcendental idealism, even an entity that lacks these markers could be free and morally responsible, although as a matter of fact Kant thinks that none are. Given that they are neither necessary nor sufficient conditions, I discuss the status of Kant's claim that higher faculties are empirical markers of moral responsibility. Drawing on connections between Kant's ethical theory and 'common rational cognition' (4:393), I suggest that Kant's theory of empirical markers can be traced to ordinary common sense beliefs about responsibility. This suggestion helps explain both why empirical markers are important and what the limits of empirical psychology are within Kant's account of moral responsibility.

  1. Performance and sensitivity analysis of the generalized likelihood ratio method for failure detection. M.S. Thesis

    Science.gov (United States)

    Bueno, R. A.

    1977-01-01

    Results of the generalized likelihood ratio (GLR) technique for the detection of failures in aircraft application are presented, and its relationship to the properties of the Kalman-Bucy filter is examined. Under the assumption that the system is perfectly modeled, the detectability and distinguishability of four failure types are investigated by means of analysis and simulations. Detection of failures is found satisfactory, but problems in identifying correctly the mode of a failure may arise. These issues are closely examined as well as the sensitivity of GLR to modeling errors. The advantages and disadvantages of this technique are discussed, and various modifications are suggested to reduce its limitations in performance and computational complexity.

  2. Bayesian Inference using Neural Net Likelihood Models for Protein Secondary Structure Prediction

    Directory of Open Access Journals (Sweden)

    Seong-Gon Kim

    2011-06-01

    Full Text Available Several techniques such as Neural Networks, Genetic Algorithms, Decision Trees and other statistical or heuristic methods have been used to approach the complex non-linear task of predicting Alpha-helicies, Beta-sheets and Turns of a proteins secondary structure in the past. This project introduces a new machine learning method by using an offline trained Multilayered Perceptrons (MLP as the likelihood models within a Bayesian Inference framework to predict secondary structures proteins. Varying window sizes are used to extract neighboring amino acid information and passed back and forth between the Neural Net models and the Bayesian Inference process until there is a convergence of the posterior secondary structure probability.

  3. Analytic confidence level calculations using the likelihood ratio and fourier transform

    International Nuclear Information System (INIS)

    Hu Hongbo; Nielsen, J.

    2000-01-01

    The interpretation of new particle search results involves a confidence level calculation on either the discovery hypothesis or the background-only ('null') hypothesis. A typical approach uses toy Monte Carlo experiments to build an expected experiment estimator distribution against which an observed experiment's estimator may be compared. In this note, a new approach is presented which calculates analytically the experiment estimator distribution via a Fourier transform, using the likelihood ratio as an ordering estimator. The analytic approach enjoys an enormous speed advantage over the toy Monte Carlo method, making it possible to quickly and precisely calculate confidence level results

  4. Evaluating healthcare priority setting at the meso level: A thematic review of empirical literature

    Science.gov (United States)

    Waithaka, Dennis; Tsofa, Benjamin; Barasa, Edwine

    2018-01-01

    Background: Decentralization of health systems has made sub-national/regional healthcare systems the backbone of healthcare delivery. These regions are tasked with the difficult responsibility of determining healthcare priorities and resource allocation amidst scarce resources. We aimed to review empirical literature that evaluated priority setting practice at the meso (sub-national) level of health systems. Methods: We systematically searched PubMed, ScienceDirect and Google scholar databases and supplemented these with manual searching for relevant studies, based on the reference list of selected papers. We only included empirical studies that described and evaluated, or those that only evaluated priority setting practice at the meso-level. A total of 16 papers were identified from LMICs and HICs. We analyzed data from the selected papers by thematic review. Results: Few studies used systematic priority setting processes, and all but one were from HICs. Both formal and informal criteria are used in priority-setting, however, informal criteria appear to be more perverse in LMICs compared to HICs. The priority setting process at the meso-level is a top-down approach with minimal involvement of the community. Accountability for reasonableness was the most common evaluative framework as it was used in 12 of the 16 studies. Efficiency, reallocation of resources and options for service delivery redesign were the most common outcome measures used to evaluate priority setting. Limitations: Our study was limited by the fact that there are very few empirical studies that have evaluated priority setting at the meso-level and there is likelihood that we did not capture all the studies. Conclusions: Improving priority setting practices at the meso level is crucial to strengthening health systems. This can be achieved through incorporating and adapting systematic priority setting processes and frameworks to the context where used, and making considerations of both process

  5. Truncation artifact suppression in cone-beam radionuclide transmission CT using maximum likelihood techniques: evaluation with human subjects

    International Nuclear Information System (INIS)

    Manglos, S.H.

    1992-01-01

    Transverse image truncation can be a serious problem for human imaging using cone-beam transmission CT (CB-CT) implemented on a conventional rotating gamma camera. This paper presents a reconstruction method to reduce or eliminate the artifacts resulting from the truncation. The method uses a previously published transmission maximum likelihood EM algorithm, adapted to the cone-beam geometry. The reconstruction method is evaluated qualitatively using three human subjects of various dimensions and various degrees of truncation. (author)

  6. Maximum Likelihood PSD Estimation for Speech Enhancement in Reverberation and Noise

    DEFF Research Database (Denmark)

    Kuklasinski, Adam; Doclo, Simon; Jensen, Søren Holdt

    2016-01-01

    In this contribution we focus on the problem of power spectral density (PSD) estimation from multiple microphone signals in reverberant and noisy environments. The PSD estimation method proposed in this paper is based on the maximum likelihood (ML) methodology. In particular, we derive a novel ML...... instrumental measures and is shown to be higher than when the competing estimator is used. Moreover, we perform a speech intelligibility test where we demonstrate that both the proposed and the competing PSD estimators lead to similar intelligibility improvements......., it is shown numerically that the mean squared estimation error achieved by the proposed method is near the limit set by the corresponding Cram´er-Rao lower bound. The speech dereverberation performance of a multi-channel Wiener filter (MWF) based on the proposed PSD estimators is measured using several...

  7. The likelihood ratio as a random variable for linked markers in kinship analysis.

    Science.gov (United States)

    Egeland, Thore; Slooten, Klaas

    2016-11-01

    The likelihood ratio is the fundamental quantity that summarizes the evidence in forensic cases. Therefore, it is important to understand the theoretical properties of this statistic. This paper is the last in a series of three, and the first to study linked markers. We show that for all non-inbred pairwise kinship comparisons, the expected likelihood ratio in favor of a type of relatedness depends on the allele frequencies only via the number of alleles, also for linked markers, and also if the true relationship is another one than is tested for by the likelihood ratio. Exact expressions for the expectation and variance are derived for all these cases. Furthermore, we show that the expected likelihood ratio is a non-increasing function if the recombination rate increases between 0 and 0.5 when the actual relationship is the one investigated by the LR. Besides being of theoretical interest, exact expressions such as obtained here can be used for software validation as they allow to verify the correctness up to arbitrary precision. The paper also presents results and advice of practical importance. For example, we argue that the logarithm of the likelihood ratio behaves in a fundamentally different way than the likelihood ratio itself in terms of expectation and variance, in agreement with its interpretation as weight of evidence. Equipped with the results presented and freely available software, one may check calculations and software and also do power calculations.

  8. Modified generalized method of moments for a robust estimation of polytomous logistic model

    Directory of Open Access Journals (Sweden)

    Xiaoshan Wang

    2014-07-01

    Full Text Available The maximum likelihood estimation (MLE method, typically used for polytomous logistic regression, is prone to bias due to both misclassification in outcome and contamination in the design matrix. Hence, robust estimators are needed. In this study, we propose such a method for nominal response data with continuous covariates. A generalized method of weighted moments (GMWM approach is developed for dealing with contaminated polytomous response data. In this approach, distances are calculated based on individual sample moments. And Huber weights are applied to those observations with large distances. Mellow-type weights are also used to downplay leverage points. We describe theoretical properties of the proposed approach. Simulations suggest that the GMWM performs very well in correcting contamination-caused biases. An empirical application of the GMWM estimator on data from a survey demonstrates its usefulness.

  9. Protein-Ligand Empirical Interaction Components for Virtual Screening.

    Science.gov (United States)

    Yan, Yuna; Wang, Weijun; Sun, Zhaoxi; Zhang, John Z H; Ji, Changge

    2017-08-28

    A major shortcoming of empirical scoring functions is that they often fail to predict binding affinity properly. Removing false positives of docking results is one of the most challenging works in structure-based virtual screening. Postdocking filters, making use of all kinds of experimental structure and activity information, may help in solving the issue. We describe a new method based on detailed protein-ligand interaction decomposition and machine learning. Protein-ligand empirical interaction components (PLEIC) are used as descriptors for support vector machine learning to develop a classification model (PLEIC-SVM) to discriminate false positives from true positives. Experimentally derived activity information is used for model training. An extensive benchmark study on 36 diverse data sets from the DUD-E database has been performed to evaluate the performance of the new method. The results show that the new method performs much better than standard empirical scoring functions in structure-based virtual screening. The trained PLEIC-SVM model is able to capture important interaction patterns between ligand and protein residues for one specific target, which is helpful in discarding false positives in postdocking filtering.

  10. The empirical potential of live streaming beyond cognitive psychology

    Directory of Open Access Journals (Sweden)

    Alexander Nicolai Wendt

    2017-03-01

    Full Text Available Empirical methods of self-description, think aloud protocols and introspection have been extensively criticized or neglected in behaviorist and cognitivist psychology. Their methodological value has been fundamentally questioned since there apparently is no suficient proof for their validity. However, the major arguments against self-description can be critically reviewed by theoretical psychology. This way, these methods’ empirical value can be redeemed. Furthermore, self-descriptive methods can be updated by the use of contemporary media technology. In order to support the promising perspectives for future empirical research in the field of cognitive psychology, Live Streaming is proposed as a viable data source. Introducing this new paradigm, this paper presents some of the formal constituents and accessible contents of Live Streaming, and relates them to established forms of empirical research. By its structure and established usage, Live Streaming bears remarkable resemblances to the traditional methods of self-description, yet it also adds fruitful new features of use. On the basis of its qualities, the possible benefits that appear to be feasible in comparison with the traditional methods of self-description are elaborated, such as Live Streaming’s ecological validity. Ultimately, controversial theoretical concepts, such as those in phenomenology and cultural-historical psychology, are adopted to sketch further potential benefits of the utility of Live Streaming in current psychology debates.

  11. Imagination perspective affects ratings of the likelihood of occurrence of autobiographical memories.

    Science.gov (United States)

    Marsh, Benjamin U; Pezdek, Kathy; Lam, Shirley T

    2014-07-01

    Two experiments tested and confirmed the hypothesis that when the phenomenological characteristics of imagined events are more similar to those of related autobiographical memories, the imagined event is more likely to be considered to have occurred. At Time 1 and 2-weeks later, individuals rated the likelihood of occurrence for 20 life events. In Experiment 1, 1-week after Time 1, individuals imagined 3 childhood events from a first-person or third-person perspective. There was a no-imagination control. An increase in likelihood ratings from Time 1 to Time 2 resulted when imagination was from the third-person but not first-person perspective. In Experiment 2, childhood and recent events were imagined from a third- or first-person perspective. A significant interaction resulted. For childhood events, likelihood change scores were greater for third-person than first-person perspective; for recent adult events, likelihood change scores were greater for first-person than third-person perspective, although this latter trend was not significant. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. Use of deterministic sampling for exploring likelihoods in linkage analysis for quantitative traits.

    NARCIS (Netherlands)

    Mackinnon, M.J.; Beek, van der S.; Kinghorn, B.P.

    1996-01-01

    Deterministic sampling was used to numerically evaluate the expected log-likelihood surfaces of QTL-marker linkage models in large pedigrees with simple structures. By calculating the expected values of likelihoods, questions of power of experimental designs, bias in parameter estimates, approximate

  13. Tests of Parameters Instability: Theoretical Study and Empirical Applications on Two Types of Models (ARMA Model and Market Model

    Directory of Open Access Journals (Sweden)

    Sahbi FARHANI

    2012-01-01

    Full Text Available This paper considers tests of parameters instability and structural change with known, unknown or multiple breakpoints. The results apply to a wide class of parametric models that are suitable for estimation by strong rules for detecting the number of breaks in a time series. For that, we use Chow, CUSUM, CUSUM of squares, Wald, likelihood ratio and Lagrange multiplier tests. Each test implicitly uses an estimate of a change point. We conclude with an empirical analysis on two different models (ARMA model and simple linear regression model.

  14. Caching and interpolated likelihoods: accelerating cosmological Monte Carlo Markov chains

    Energy Technology Data Exchange (ETDEWEB)

    Bouland, Adam; Easther, Richard; Rosenfeld, Katherine, E-mail: adam.bouland@aya.yale.edu, E-mail: richard.easther@yale.edu, E-mail: krosenfeld@cfa.harvard.edu [Department of Physics, Yale University, New Haven CT 06520 (United States)

    2011-05-01

    We describe a novel approach to accelerating Monte Carlo Markov Chains. Our focus is cosmological parameter estimation, but the algorithm is applicable to any problem for which the likelihood surface is a smooth function of the free parameters and computationally expensive to evaluate. We generate a high-order interpolating polynomial for the log-likelihood using the first points gathered by the Markov chains as a training set. This polynomial then accurately computes the majority of the likelihoods needed in the latter parts of the chains. We implement a simple version of this algorithm as a patch (InterpMC) to CosmoMC and show that it accelerates parameter estimatation by a factor of between two and four for well-converged chains. The current code is primarily intended as a ''proof of concept'', and we argue that there is considerable room for further performance gains. Unlike other approaches to accelerating parameter fits, we make no use of precomputed training sets or special choices of variables, and InterpMC is almost entirely transparent to the user.

  15. Caching and interpolated likelihoods: accelerating cosmological Monte Carlo Markov chains

    International Nuclear Information System (INIS)

    Bouland, Adam; Easther, Richard; Rosenfeld, Katherine

    2011-01-01

    We describe a novel approach to accelerating Monte Carlo Markov Chains. Our focus is cosmological parameter estimation, but the algorithm is applicable to any problem for which the likelihood surface is a smooth function of the free parameters and computationally expensive to evaluate. We generate a high-order interpolating polynomial for the log-likelihood using the first points gathered by the Markov chains as a training set. This polynomial then accurately computes the majority of the likelihoods needed in the latter parts of the chains. We implement a simple version of this algorithm as a patch (InterpMC) to CosmoMC and show that it accelerates parameter estimatation by a factor of between two and four for well-converged chains. The current code is primarily intended as a ''proof of concept'', and we argue that there is considerable room for further performance gains. Unlike other approaches to accelerating parameter fits, we make no use of precomputed training sets or special choices of variables, and InterpMC is almost entirely transparent to the user

  16. Likelihood estimators for multivariate extremes

    KAUST Repository

    Huser, Raphaë l; Davison, Anthony C.; Genton, Marc G.

    2015-01-01

    The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.

  17. Likelihood estimators for multivariate extremes

    KAUST Repository

    Huser, Raphaël

    2015-11-17

    The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.

  18. Quantifying uncertainty, variability and likelihood for ordinary differential equation models

    LENUS (Irish Health Repository)

    Weisse, Andrea Y

    2010-10-28

    Abstract Background In many applications, ordinary differential equation (ODE) models are subject to uncertainty or variability in initial conditions and parameters. Both, uncertainty and variability can be quantified in terms of a probability density function on the state and parameter space. Results The partial differential equation that describes the evolution of this probability density function has a form that is particularly amenable to application of the well-known method of characteristics. The value of the density at some point in time is directly accessible by the solution of the original ODE extended by a single extra dimension (for the value of the density). This leads to simple methods for studying uncertainty, variability and likelihood, with significant advantages over more traditional Monte Carlo and related approaches especially when studying regions with low probability. Conclusions While such approaches based on the method of characteristics are common practice in other disciplines, their advantages for the study of biological systems have so far remained unrecognized. Several examples illustrate performance and accuracy of the approach and its limitations.

  19. Intermolecular interactions in the condensed phase: Evaluation of semi-empirical quantum mechanical methods.

    Science.gov (United States)

    Christensen, Anders S; Kromann, Jimmy C; Jensen, Jan H; Cui, Qiang

    2017-10-28

    To facilitate further development of approximate quantum mechanical methods for condensed phase applications, we present a new benchmark dataset of intermolecular interaction energies in the solution phase for a set of 15 dimers, each containing one charged monomer. The reference interaction energy in solution is computed via a thermodynamic cycle that integrates dimer binding energy in the gas phase at the coupled cluster level and solute-solvent interaction with density functional theory; the estimated uncertainty of such calculated interaction energy is ±1.5 kcal/mol. The dataset is used to benchmark the performance of a set of semi-empirical quantum mechanical (SQM) methods that include DFTB3-D3, DFTB3/CPE-D3, OM2-D3, PM6-D3, PM6-D3H+, and PM7 as well as the HF-3c method. We find that while all tested SQM methods tend to underestimate binding energies in the gas phase with a root-mean-squared error (RMSE) of 2-5 kcal/mol, they overestimate binding energies in the solution phase with an RMSE of 3-4 kcal/mol, with the exception of DFTB3/CPE-D3 and OM2-D3, for which the systematic deviation is less pronounced. In addition, we find that HF-3c systematically overestimates binding energies in both gas and solution phases. As most approximate QM methods are parametrized and evaluated using data measured or calculated in the gas phase, the dataset represents an important first step toward calibrating QM based methods for application in the condensed phase where polarization and exchange repulsion need to be treated in a balanced fashion.

  20. Intermolecular interactions in the condensed phase: Evaluation of semi-empirical quantum mechanical methods

    Science.gov (United States)

    Christensen, Anders S.; Kromann, Jimmy C.; Jensen, Jan H.; Cui, Qiang

    2017-10-01

    To facilitate further development of approximate quantum mechanical methods for condensed phase applications, we present a new benchmark dataset of intermolecular interaction energies in the solution phase for a set of 15 dimers, each containing one charged monomer. The reference interaction energy in solution is computed via a thermodynamic cycle that integrates dimer binding energy in the gas phase at the coupled cluster level and solute-solvent interaction with density functional theory; the estimated uncertainty of such calculated interaction energy is ±1.5 kcal/mol. The dataset is used to benchmark the performance of a set of semi-empirical quantum mechanical (SQM) methods that include DFTB3-D3, DFTB3/CPE-D3, OM2-D3, PM6-D3, PM6-D3H+, and PM7 as well as the HF-3c method. We find that while all tested SQM methods tend to underestimate binding energies in the gas phase with a root-mean-squared error (RMSE) of 2-5 kcal/mol, they overestimate binding energies in the solution phase with an RMSE of 3-4 kcal/mol, with the exception of DFTB3/CPE-D3 and OM2-D3, for which the systematic deviation is less pronounced. In addition, we find that HF-3c systematically overestimates binding energies in both gas and solution phases. As most approximate QM methods are parametrized and evaluated using data measured or calculated in the gas phase, the dataset represents an important first step toward calibrating QM based methods for application in the condensed phase where polarization and exchange repulsion need to be treated in a balanced fashion.

  1. CO2 capture in amine solutions: modelling and simulations with non-empirical methods

    Science.gov (United States)

    Andreoni, Wanda; Pietrucci, Fabio

    2016-12-01

    Absorption in aqueous amine solutions is the most advanced technology for the capture of CO2, although suffering from drawbacks that do not allow exploitation on large scale. The search for optimum solvents has been pursued with empirical methods and has also motivated a number of computational approaches over the last decade. However, a deeper level of understanding of the relevant chemical reactions in solution is required so as to contribute to this effort. We present here a brief critical overview of the most recent applications of computer simulations using ab initio methods. Comparison of their outcome shows a strong dependence on the structural models employed to represent the molecular systems in solution and on the strategy used to simulate the reactions. In particular, the results of very recent ab initio molecular dynamics augmented with metadynamics are summarized, showing the crucial role of water, which has been so far strongly underestimated both in the calculations and in the interpretation of experimental data. Indications are given for advances in computational approaches that are necessary if meant to contribute to the rational design of new solvents.

  2. CO2 capture in amine solutions: modelling and simulations with non-empirical methods

    International Nuclear Information System (INIS)

    Andreoni, Wanda; Pietrucci, Fabio

    2016-01-01

    Absorption in aqueous amine solutions is the most advanced technology for the capture of CO 2 , although suffering from drawbacks that do not allow exploitation on large scale. The search for optimum solvents has been pursued with empirical methods and has also motivated a number of computational approaches over the last decade. However, a deeper level of understanding of the relevant chemical reactions in solution is required so as to contribute to this effort. We present here a brief critical overview of the most recent applications of computer simulations using ab initio methods. Comparison of their outcome shows a strong dependence on the structural models employed to represent the molecular systems in solution and on the strategy used to simulate the reactions. In particular, the results of very recent ab initio molecular dynamics augmented with metadynamics are summarized, showing the crucial role of water, which has been so far strongly underestimated both in the calculations and in the interpretation of experimental data. Indications are given for advances in computational approaches that are necessary if meant to contribute to the rational design of new solvents. (topical review)

  3. Cases in which ancestral maximum likelihood will be confusingly misleading.

    Science.gov (United States)

    Handelman, Tomer; Chor, Benny

    2017-05-07

    Ancestral maximum likelihood (AML) is a phylogenetic tree reconstruction criteria that "lies between" maximum parsimony (MP) and maximum likelihood (ML). ML has long been known to be statistically consistent. On the other hand, Felsenstein (1978) showed that MP is statistically inconsistent, and even positively misleading: There are cases where the parsimony criteria, applied to data generated according to one tree topology, will be optimized on a different tree topology. The question of weather AML is statistically consistent or not has been open for a long time. Mossel et al. (2009) have shown that AML can "shrink" short tree edges, resulting in a star tree with no internal resolution, which yields a better AML score than the original (resolved) model. This result implies that AML is statistically inconsistent, but not that it is positively misleading, because the star tree is compatible with any other topology. We show that AML is confusingly misleading: For some simple, four taxa (resolved) tree, the ancestral likelihood optimization criteria is maximized on an incorrect (resolved) tree topology, as well as on a star tree (both with specific edge lengths), while the tree with the original, correct topology, has strictly lower ancestral likelihood. Interestingly, the two short edges in the incorrect, resolved tree topology are of length zero, and are not adjacent, so this resolved tree is in fact a simple path. While for MP, the underlying phenomenon can be described as long edge attraction, it turns out that here we have long edge repulsion. Copyright © 2017. Published by Elsevier Ltd.

  4. Frequency-Domain Maximum-Likelihood Estimation of High-Voltage Pulse Transformer Model Parameters

    CERN Document Server

    Aguglia, D; Martins, C.D.A.

    2014-01-01

    This paper presents an offline frequency-domain nonlinear and stochastic identification method for equivalent model parameter estimation of high-voltage pulse transformers. Such kinds of transformers are widely used in the pulsed-power domain, and the difficulty in deriving pulsed-power converter optimal control strategies is directly linked to the accuracy of the equivalent circuit parameters. These components require models which take into account electric fields energies represented by stray capacitance in the equivalent circuit. These capacitive elements must be accurately identified, since they greatly influence the general converter performances. A nonlinear frequency-based identification method, based on maximum-likelihood estimation, is presented, and a sensitivity analysis of the best experimental test to be considered is carried out. The procedure takes into account magnetic saturation and skin effects occurring in the windings during the frequency tests. The presented method is validated by experim...

  5. Likelihood inference for a nonstationary fractional autoregressive model

    DEFF Research Database (Denmark)

    Johansen, Søren; Ørregård Nielsen, Morten

    2010-01-01

    This paper discusses model-based inference in an autoregressive model for fractional processes which allows the process to be fractional of order d or d-b. Fractional differencing involves infinitely many past values and because we are interested in nonstationary processes we model the data X1......,...,X_{T} given the initial values X_{-n}, n=0,1,..., as is usually done. The initial values are not modeled but assumed to be bounded. This represents a considerable generalization relative to all previous work where it is assumed that initial values are zero. For the statistical analysis we assume...... the conditional Gaussian likelihood and for the probability analysis we also condition on initial values but assume that the errors in the autoregressive model are i.i.d. with suitable moment conditions. We analyze the conditional likelihood and its derivatives as stochastic processes in the parameters, including...

  6. Design of simplified maximum-likelihood receivers for multiuser CPM systems.

    Science.gov (United States)

    Bing, Li; Bai, Baoming

    2014-01-01

    A class of simplified maximum-likelihood receivers designed for continuous phase modulation based multiuser systems is proposed. The presented receiver is built upon a front end employing mismatched filters and a maximum-likelihood detector defined in a low-dimensional signal space. The performance of the proposed receivers is analyzed and compared to some existing receivers. Some schemes are designed to implement the proposed receivers and to reveal the roles of different system parameters. Analysis and numerical results show that the proposed receivers can approach the optimum multiuser receivers with significantly (even exponentially in some cases) reduced complexity and marginal performance degradation.

  7. Aggregation, Validation, and Generalization of Qualitative Data - Methodological and Practical Research Strategies Illustrated by the Research Process of an empirically Based Typology.

    Science.gov (United States)

    Weis, Daniel; Willems, Helmut

    2017-06-01

    The article deals with the question of how aggregated data which allow for generalizable insights can be generated from single-case based qualitative investigations. Thereby, two central challenges of qualitative social research are outlined: First, researchers must ensure that the single-case data can be aggregated and condensed so that new collective structures can be detected. Second, they must apply methods and practices to allow for the generalization of the results beyond the specific study. In the following, we demonstrate how and under what conditions these challenges can be addressed in research practice. To this end, the research process of the construction of an empirically based typology is described. A qualitative study, conducted within the framework of the Luxembourg Youth Report, is used to illustrate this process. Specifically, strategies are presented which increase the likelihood of generalizability or transferability of the results, while also highlighting their limitations.

  8. Theoretical vs. empirical discriminability: the application of ROC methods to eyewitness identification.

    Science.gov (United States)

    Wixted, John T; Mickes, Laura

    2018-01-01

    Receiver operating characteristic (ROC) analysis was introduced to the field of eyewitness identification 5 years ago. Since that time, it has been both influential and controversial, and the debate has raised an issue about measuring discriminability that is rarely considered. The issue concerns the distinction between empirical discriminability (measured by area under the ROC curve) vs. underlying/theoretical discriminability (measured by d' or variants of it). Under most circumstances, the two measures will agree about a difference between two conditions in terms of discriminability. However, it is possible for them to disagree, and that fact can lead to confusion about which condition actually yields higher discriminability. For example, if the two conditions have implications for real-world practice (e.g., a comparison of competing lineup formats), should a policymaker rely on the area-under-the-curve measure or the theory-based measure? Here, we illustrate the fact that a given empirical ROC yields as many underlying discriminability measures as there are theories that one is willing to take seriously. No matter which theory is correct, for practical purposes, the singular area-under-the-curve measure best identifies the diagnostically superior procedure. For that reason, area under the ROC curve informs policy in a way that underlying theoretical discriminability never can. At the same time, theoretical measures of discriminability are equally important, but for a different reason. Without an adequate theoretical understanding of the relevant task, the field will be in no position to enhance empirical discriminability.

  9. Improving the desolvation penalty in empirical protein pKa modeling

    DEFF Research Database (Denmark)

    Olsson, Mats Henrik Mikael

    2012-01-01

    Unlike atomistic and continuum models, empirical pk(a) predicting methods need to include desolvation contributions explicitly. This study describes a new empirical desolvation method based on the Born solvation model. The new desolvation model was evaluated by high-level Poisson-Boltzmann...

  10. A scaling transformation for classifier output based on likelihood ratio: Applications to a CAD workstation for diagnosis of breast cancer

    International Nuclear Information System (INIS)

    Horsch, Karla; Pesce, Lorenzo L.; Giger, Maryellen L.; Metz, Charles E.; Jiang Yulei

    2012-01-01

    Purpose: The authors developed scaling methods that monotonically transform the output of one classifier to the ''scale'' of another. Such transformations affect the distribution of classifier output while leaving the ROC curve unchanged. In particular, they investigated transformations between radiologists and computer classifiers, with the goal of addressing the problem of comparing and interpreting case-specific values of output from two classifiers. Methods: Using both simulated and radiologists' rating data of breast imaging cases, the authors investigated a likelihood-ratio-scaling transformation, based on ''matching'' classifier likelihood ratios. For comparison, three other scaling transformations were investigated that were based on matching classifier true positive fraction, false positive fraction, or cumulative distribution function, respectively. The authors explored modifying the computer output to reflect the scale of the radiologist, as well as modifying the radiologist's ratings to reflect the scale of the computer. They also evaluated how dataset size affects the transformations. Results: When ROC curves of two classifiers differed substantially, the four transformations were found to be quite different. The likelihood-ratio scaling transformation was found to vary widely from radiologist to radiologist. Similar results were found for the other transformations. Our simulations explored the effect of database sizes on the accuracy of the estimation of our scaling transformations. Conclusions: The likelihood-ratio-scaling transformation that the authors have developed and evaluated was shown to be capable of transforming computer and radiologist outputs to a common scale reliably, thereby allowing the comparison of the computer and radiologist outputs on the basis of a clinically relevant statistic.

  11. Likelihood-based inference for clustered line transect data

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus Plenge; Schweder, Tore

    The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

  12. Likelihood-based inference for clustered line transect data

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus; Schweder, Tore

    2006-01-01

    The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

  13. Reducing the likelihood of future human activities that could affect geologic high-level waste repositories

    International Nuclear Information System (INIS)

    1984-05-01

    The disposal of radioactive wastes in deep geologic formations provides a means of isolating the waste from people until the radioactivity has decayed to safe levels. However, isolating people from the wastes is a different problem, since we do not know what the future condition of society will be. The Human Interference Task Force was convened by the US Department of Energy to determine whether reasonable means exist (or could be developed) to reduce the likelihood of future human unintentionally intruding on radioactive waste isolation systems. The task force concluded that significant reductions in the likelihood of human interference could be achieved, for perhaps thousands of years into the future, if appropriate steps are taken to communicate the existence of the repository. Consequently, for two years the task force directed most of its study toward the area of long-term communication. Methods are discussed for achieving long-term communication by using permanent markers and widely disseminated records, with various steps taken to provide multiple levels of protection against loss, destruction, and major language/societal changes. Also developed is the concept of a universal symbol to denote Caution - Biohazardous Waste Buried Here. If used for the thousands of non-radioactive biohazardous waste sites in this country alone, a symbol could transcend generations and language changes, thereby vastly improving the likelihood of successful isolation of all buried biohazardous wastes

  14. Reducing the likelihood of future human activities that could affect geologic high-level waste repositories

    Energy Technology Data Exchange (ETDEWEB)

    1984-05-01

    The disposal of radioactive wastes in deep geologic formations provides a means of isolating the waste from people until the radioactivity has decayed to safe levels. However, isolating people from the wastes is a different problem, since we do not know what the future condition of society will be. The Human Interference Task Force was convened by the US Department of Energy to determine whether reasonable means exist (or could be developed) to reduce the likelihood of future human unintentionally intruding on radioactive waste isolation systems. The task force concluded that significant reductions in the likelihood of human interference could be achieved, for perhaps thousands of years into the future, if appropriate steps are taken to communicate the existence of the repository. Consequently, for two years the task force directed most of its study toward the area of long-term communication. Methods are discussed for achieving long-term communication by using permanent markers and widely disseminated records, with various steps taken to provide multiple levels of protection against loss, destruction, and major language/societal changes. Also developed is the concept of a universal symbol to denote Caution - Biohazardous Waste Buried Here. If used for the thousands of non-radioactive biohazardous waste sites in this country alone, a symbol could transcend generations and language changes, thereby vastly improving the likelihood of successful isolation of all buried biohazardous wastes.

  15. Calibration of two complex ecosystem models with different likelihood functions

    Science.gov (United States)

    Hidy, Dóra; Haszpra, László; Pintér, Krisztina; Nagy, Zoltán; Barcza, Zoltán

    2014-05-01

    The biosphere is a sensitive carbon reservoir. Terrestrial ecosystems were approximately carbon neutral during the past centuries, but they became net carbon sinks due to climate change induced environmental change and associated CO2 fertilization effect of the atmosphere. Model studies and measurements indicate that the biospheric carbon sink can saturate in the future due to ongoing climate change which can act as a positive feedback. Robustness of carbon cycle models is a key issue when trying to choose the appropriate model for decision support. The input parameters of the process-based models are decisive regarding the model output. At the same time there are several input parameters for which accurate values are hard to obtain directly from experiments or no local measurements are available. Due to the uncertainty associated with the unknown model parameters significant bias can be experienced if the model is used to simulate the carbon and nitrogen cycle components of different ecosystems. In order to improve model performance the unknown model parameters has to be estimated. We developed a multi-objective, two-step calibration method based on Bayesian approach in order to estimate the unknown parameters of PaSim and Biome-BGC models. Biome-BGC and PaSim are a widely used biogeochemical models that simulate the storage and flux of water, carbon, and nitrogen between the ecosystem and the atmosphere, and within the components of the terrestrial ecosystems (in this research the developed version of Biome-BGC is used which is referred as BBGC MuSo). Both models were calibrated regardless the simulated processes and type of model parameters. The calibration procedure is based on the comparison of measured data with simulated results via calculating a likelihood function (degree of goodness-of-fit between simulated and measured data). In our research different likelihood function formulations were used in order to examine the effect of the different model

  16. Fast maximum likelihood estimation of mutation rates using a birth-death process.

    Science.gov (United States)

    Wu, Xiaowei; Zhu, Hongxiao

    2015-02-07

    Since fluctuation analysis was first introduced by Luria and Delbrück in 1943, it has been widely used to make inference about spontaneous mutation rates in cultured cells. Under certain model assumptions, the probability distribution of the number of mutants that appear in a fluctuation experiment can be derived explicitly, which provides the basis of mutation rate estimation. It has been shown that, among various existing estimators, the maximum likelihood estimator usually demonstrates some desirable properties such as consistency and lower mean squared error. However, its application in real experimental data is often hindered by slow computation of likelihood due to the recursive form of the mutant-count distribution. We propose a fast maximum likelihood estimator of mutation rates, MLE-BD, based on a birth-death process model with non-differential growth assumption. Simulation studies demonstrate that, compared with the conventional maximum likelihood estimator derived from the Luria-Delbrück distribution, MLE-BD achieves substantial improvement on computational speed and is applicable to arbitrarily large number of mutants. In addition, it still retains good accuracy on point estimation. Published by Elsevier Ltd.

  17. Inglorious Empire

    DEFF Research Database (Denmark)

    Khair, Tabish

    2017-01-01

    Review of 'Inglorious Empire: What the British did to India' by Shashi Tharoor, London, Hurst Publishers, 2017, 296 pp., £20.00......Review of 'Inglorious Empire: What the British did to India' by Shashi Tharoor, London, Hurst Publishers, 2017, 296 pp., £20.00...

  18. An Empirical Mass Function Distribution

    Science.gov (United States)

    Murray, S. G.; Robotham, A. S. G.; Power, C.

    2018-03-01

    The halo mass function, encoding the comoving number density of dark matter halos of a given mass, plays a key role in understanding the formation and evolution of galaxies. As such, it is a key goal of current and future deep optical surveys to constrain the mass function down to mass scales that typically host {L}\\star galaxies. Motivated by the proven accuracy of Press–Schechter-type mass functions, we introduce a related but purely empirical form consistent with standard formulae to better than 4% in the medium-mass regime, {10}10{--}{10}13 {h}-1 {M}ȯ . In particular, our form consists of four parameters, each of which has a simple interpretation, and can be directly related to parameters of the galaxy distribution, such as {L}\\star . Using this form within a hierarchical Bayesian likelihood model, we show how individual mass-measurement errors can be successfully included in a typical analysis, while accounting for Eddington bias. We apply our form to a question of survey design in the context of a semi-realistic data model, illustrating how it can be used to obtain optimal balance between survey depth and angular coverage for constraints on mass function parameters. Open-source Python and R codes to apply our new form are provided at http://mrpy.readthedocs.org and https://cran.r-project.org/web/packages/tggd/index.html respectively.

  19. Preliminary attempt on maximum likelihood tomosynthesis reconstruction of DEI data

    International Nuclear Information System (INIS)

    Wang Zhentian; Huang Zhifeng; Zhang Li; Kang Kejun; Chen Zhiqiang; Zhu Peiping

    2009-01-01

    Tomosynthesis is a three-dimension reconstruction method that can remove the effect of superimposition with limited angle projections. It is especially promising in mammography where radiation dose is concerned. In this paper, we propose a maximum likelihood tomosynthesis reconstruction algorithm (ML-TS) on the apparent absorption data of diffraction enhanced imaging (DEI). The motivation of this contribution is to develop a tomosynthesis algorithm in low-dose or noisy circumstances and make DEI get closer to clinic application. The theoretical statistical models of DEI data in physics are analyzed and the proposed algorithm is validated with the experimental data at the Beijing Synchrotron Radiation Facility (BSRF). The results of ML-TS have better contrast compared with the well known 'shift-and-add' algorithm and FBP algorithm. (authors)

  20. Marginal Maximum Likelihood Estimation of Item Response Models in R

    Directory of Open Access Journals (Sweden)

    Matthew S. Johnson

    2007-02-01

    Full Text Available Item response theory (IRT models are a class of statistical models used by researchers to describe the response behaviors of individuals to a set of categorically scored items. The most common IRT models can be classified as generalized linear fixed- and/or mixed-effect models. Although IRT models appear most often in the psychological testing literature, researchers in other fields have successfully utilized IRT-like models in a wide variety of applications. This paper discusses the three major methods of estimation in IRT and develops R functions utilizing the built-in capabilities of the R environment to find the marginal maximum likelihood estimates of the generalized partial credit model. The currently available R packages ltm is also discussed.

  1. Maximum likelihood estimation of the attenuated ultrasound pulse

    DEFF Research Database (Denmark)

    Rasmussen, Klaus Bolding

    1994-01-01

    The attenuated ultrasound pulse is divided into two parts: a stationary basic pulse and a nonstationary attenuation pulse. A standard ARMA model is used for the basic pulse, and a nonstandard ARMA model is derived for the attenuation pulse. The maximum likelihood estimator of the attenuated...

  2. Univariate and Bivariate Empirical Mode Decomposition for Postural Stability Analysis

    Directory of Open Access Journals (Sweden)

    Jacques Duchêne

    2008-05-01

    Full Text Available The aim of this paper was to compare empirical mode decomposition (EMD and two new extended methods of  EMD named complex empirical mode decomposition (complex-EMD and bivariate empirical mode decomposition (bivariate-EMD. All methods were used to analyze stabilogram center of pressure (COP time series. The two new methods are suitable to be applied to complex time series to extract complex intrinsic mode functions (IMFs before the Hilbert transform is subsequently applied on the IMFs. The trace of the analytic IMF in the complex plane has a circular form, with each IMF having its own rotation frequency. The area of the circle and the average rotation frequency of IMFs represent efficient indicators of the postural stability status of subjects. Experimental results show the effectiveness of these indicators to identify differences in standing posture between groups.

  3. Design of Simplified Maximum-Likelihood Receivers for Multiuser CPM Systems

    Directory of Open Access Journals (Sweden)

    Li Bing

    2014-01-01

    Full Text Available A class of simplified maximum-likelihood receivers designed for continuous phase modulation based multiuser systems is proposed. The presented receiver is built upon a front end employing mismatched filters and a maximum-likelihood detector defined in a low-dimensional signal space. The performance of the proposed receivers is analyzed and compared to some existing receivers. Some schemes are designed to implement the proposed receivers and to reveal the roles of different system parameters. Analysis and numerical results show that the proposed receivers can approach the optimum multiuser receivers with significantly (even exponentially in some cases reduced complexity and marginal performance degradation.

  4. Attitude towards, and likelihood of, complaining in the banking ...

    African Journals Online (AJOL)

    aims to determine customers' attitudes towards complaining as well as their likelihood of voicing a .... is particularly powerful and impacts greatly on customer satisfaction and retention. ...... 'Cross-national analysis of hotel customers' attitudes ...

  5. Basis material decomposition in spectral CT using a semi-empirical, polychromatic adaption of the Beer-Lambert model

    Science.gov (United States)

    Ehn, S.; Sellerer, T.; Mechlem, K.; Fehringer, A.; Epple, M.; Herzen, J.; Pfeiffer, F.; Noël, P. B.

    2017-01-01

    Following the development of energy-sensitive photon-counting detectors using high-Z sensor materials, application of spectral x-ray imaging methods to clinical practice comes into reach. However, these detectors require extensive calibration efforts in order to perform spectral imaging tasks like basis material decomposition. In this paper, we report a novel approach to basis material decomposition that utilizes a semi-empirical estimator for the number of photons registered in distinct energy bins in the presence of beam-hardening effects which can be termed as a polychromatic Beer-Lambert model. A maximum-likelihood estimator is applied to the model in order to obtain estimates of the underlying sample composition. Using a Monte-Carlo simulation of a typical clinical CT acquisition, the performance of the proposed estimator was evaluated. The estimator is shown to be unbiased and efficient according to the Cramér-Rao lower bound. In particular, the estimator is capable of operating with a minimum number of calibration measurements. Good results were obtained after calibration using less than 10 samples of known composition in a two-material attenuation basis. This opens up the possibility for fast re-calibration in the clinical routine which is considered an advantage of the proposed method over other implementations reported in the literature.

  6. Recovery of Graded Response Model Parameters: A Comparison of Marginal Maximum Likelihood and Markov Chain Monte Carlo Estimation

    Science.gov (United States)

    Kieftenbeld, Vincent; Natesan, Prathiba

    2012-01-01

    Markov chain Monte Carlo (MCMC) methods enable a fully Bayesian approach to parameter estimation of item response models. In this simulation study, the authors compared the recovery of graded response model parameters using marginal maximum likelihood (MML) and Gibbs sampling (MCMC) under various latent trait distributions, test lengths, and…

  7. Physical constraints on the likelihood of life on exoplanets

    Science.gov (United States)

    Lingam, Manasvi; Loeb, Abraham

    2018-04-01

    One of the most fundamental questions in exoplanetology is to determine whether a given planet is habitable. We estimate the relative likelihood of a planet's propensity towards habitability by considering key physical characteristics such as the role of temperature on ecological and evolutionary processes, and atmospheric losses via hydrodynamic escape and stellar wind erosion. From our analysis, we demonstrate that Earth-sized exoplanets in the habitable zone around M-dwarfs seemingly display much lower prospects of being habitable relative to Earth, owing to the higher incident ultraviolet fluxes and closer distances to the host star. We illustrate our results by specifically computing the likelihood (of supporting life) for the recently discovered exoplanets, Proxima b and TRAPPIST-1e, which we find to be several orders of magnitude smaller than that of Earth.

  8. Evidence-based care and the case for intuition and tacit knowledge in clinical assessment and decision making in mental health nursing practice: an empirical contribution to the debate.

    Science.gov (United States)

    Welsh, I; Lyons, C M

    2001-08-01

    This paper provides empirical evidence that challenges the view that methods of clinical assessment and decision making should not rely solely on logical positivist approaches. Whilst the National Health Service (NHS) Executive currently takes a hard positivist line on what constitutes evidence-based practice, data reveal that it is not always appropriate to disregard the tacit knowledge and intuition of experienced practitioners when making assessment decisions in mental health nursing practice. Data support the case for a holistic approach which may draw on intuition and tacit knowledge, as well as traditional approaches, to meet the requirements of clients with complex mental health problems. A model based on Schon's notion of reflection in and reflection on practice is proposed which demonstrates the value of intuition and tacit knowledge. This model allows the generation of insights which may ultimately be demonstrated to be acceptable and empirically testable. It is accepted that an element of risk taking is inevitable, but the inclusion of a formal analytical process into the model reduces the likelihood of inappropriate care interventions. The cognitive processes which experienced nurses use to make clinical decisions and their implications for practice will be explored.

  9. Maximal information analysis: I - various Wayne State plots and the most common likelihood principle

    International Nuclear Information System (INIS)

    Bonvicini, G.

    2005-01-01

    Statistical analysis using all moments of the likelihood L(y vertical bar α) (y being the data and α being the fit parameters) is presented. The relevant plots for various data fitting situations are presented. The goodness of fit (GOF) parameter (currently the χ 2 ) is redefined as the isoprobability level in a multidimensional space. Many useful properties of statistical analysis are summarized in a new statistical principle which states that the most common likelihood, and not the tallest, is the best possible likelihood, when comparing experiments or hypotheses

  10. Empirical Specification of Utility Functions.

    Science.gov (United States)

    Mellenbergh, Gideon J.

    Decision theory can be applied to four types of decision situations in education and psychology: (1) selection; (2) placement; (3) classification; and (4) mastery. For the application of the theory, a utility function must be specified. Usually the utility function is chosen on a priori grounds. In this paper methods for the empirical assessment…

  11. Uncovering Voter Preference Structures Using a Best-Worst Scaling Procedure: Method and Empirical Example in the British General Election of 2010

    DEFF Research Database (Denmark)

    Ormrod, Robert P.; Savigny, Heather

    Best-Worst scaling (BWS) is a method that can provide insights into the preference structures of voters. By asking voters to select the ‘best’ and ‘worst’ option (‘most important’ and ‘least important’ media in our investigation) from a short list of alternatives it is possible to uncover the rel...... the least information. We furthermore investigate group differences using an ANOVA procedure to demonstrate how contextual variables can enrich our empirical investigations using the BWS method....

  12. A Scoring Tool to Identify East African HIV-1 Serodiscordant Partnerships with a High Likelihood of Pregnancy.

    Science.gov (United States)

    Heffron, Renee; Cohen, Craig R; Ngure, Kenneth; Bukusi, Elizabeth; Were, Edwin; Kiarie, James; Mugo, Nelly; Celum, Connie; Baeten, Jared M

    2015-01-01

    HIV-1 prevention programs targeting HIV-1 serodiscordant couples need to identify couples that are likely to become pregnant to facilitate discussions about methods to minimize HIV-1 risk during pregnancy attempts (i.e. safer conception) or effective contraception when pregnancy is unintended. A clinical prediction tool could be used to identify HIV-1 serodiscordant couples with a high likelihood of pregnancy within one year. Using standardized clinical prediction methods, we developed and validated a tool to identify heterosexual East African HIV-1 serodiscordant couples with an increased likelihood of becoming pregnant in the next year. Datasets were from three prospectively followed cohorts, including nearly 7,000 couples from Kenya and Uganda participating in HIV-1 prevention trials and delivery projects. The final score encompassed the age of the woman, woman's number of children living, partnership duration, having had condomless sex in the past month, and non-use of an effective contraceptive. The area under the curve (AUC) for the probability of the score to correctly predict pregnancy was 0.74 (95% CI 0.72-0.76). Scores ≥ 7 predicted a pregnancy incidence of >17% per year and captured 78% of the pregnancies. Internal and external validation confirmed the predictive ability of the score. A pregnancy likelihood score encompassing basic demographic, clinical and behavioral factors defined African HIV-1 serodiscordant couples with high one-year pregnancy incidence rates. This tool could be used to engage African HIV-1 serodiscordant couples in counseling discussions about fertility intentions in order to offer services for safer conception or contraception that align with their reproductive goals.

  13. Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model

    International Nuclear Information System (INIS)

    Edwards, Darrin C.; Kupinski, Matthew A.; Metz, Charles E.; Nishikawa, Robert M.

    2002-01-01

    We have developed a model for FROC curve fitting that relates the observer's FROC performance not to the ROC performance that would be obtained if the observer's responses were scored on a per image basis, but rather to a hypothesized ROC performance that the observer would obtain in the task of classifying a set of 'candidate detections' as positive or negative. We adopt the assumptions of the Bunch FROC model, namely that the observer's detections are all mutually independent, as well as assumptions qualitatively similar to, but different in nature from, those made by Chakraborty in his AFROC scoring methodology. Under the assumptions of our model, we show that the observer's FROC performance is a linearly scaled version of the candidate analysis ROC curve, where the scaling factors are just given by the FROC operating point coordinates for detecting initial candidates. Further, we show that the likelihood function of the model parameters given observational data takes on a simple form, and we develop a maximum likelihood method for fitting a FROC curve to this data. FROC and AFROC curves are produced for computer vision observer datasets and compared with the results of the AFROC scoring method. Although developed primarily with computer vision schemes in mind, we hope that the methodology presented here will prove worthy of further study in other applications as well

  14. Empirical Information Metrics for Prediction Power and Experiment Planning

    Directory of Open Access Journals (Sweden)

    Christopher Lee

    2011-01-01

    Full Text Available In principle, information theory could provide useful metrics for statistical inference. In practice this is impeded by divergent assumptions: Information theory assumes the joint distribution of variables of interest is known, whereas in statistical inference it is hidden and is the goal of inference. To integrate these approaches we note a common theme they share, namely the measurement of prediction power. We generalize this concept as an information metric, subject to several requirements: Calculation of the metric must be objective or model-free; unbiased; convergent; probabilistically bounded; and low in computational complexity. Unfortunately, widely used model selection metrics such as Maximum Likelihood, the Akaike Information Criterion and Bayesian Information Criterion do not necessarily meet all these requirements. We define four distinct empirical information metrics measured via sampling, with explicit Law of Large Numbers convergence guarantees, which meet these requirements: Ie, the empirical information, a measure of average prediction power; Ib, the overfitting bias information, which measures selection bias in the modeling procedure; Ip, the potential information, which measures the total remaining information in the observations not yet discovered by the model; and Im, the model information, which measures the model’s extrapolation prediction power. Finally, we show that Ip + Ie, Ip + Im, and Ie — Im are fixed constants for a given observed dataset (i.e. prediction target, independent of the model, and thus represent a fundamental subdivision of the total information contained in the observations. We discuss the application of these metrics to modeling and experiment planning.    

  15. A New Maximum-Likelihood Change Estimator for Two-Pass SAR Coherent Change Detection.

    Energy Technology Data Exchange (ETDEWEB)

    Wahl, Daniel E.; Yocky, David A.; Jakowatz, Charles V,

    2014-09-01

    In this paper, we derive a new optimal change metric to be used in synthetic aperture RADAR (SAR) coherent change detection (CCD). Previous CCD methods tend to produce false alarm states (showing change when there is none) in areas of the image that have a low clutter-to-noise power ratio (CNR). The new estimator does not suffer from this shortcoming. It is a surprisingly simple expression, easy to implement, and is optimal in the maximum-likelihood (ML) sense. The estimator produces very impressive results on the CCD collects that we have tested.

  16. An Algorithmic Comparison of the Hyper-Reduction and the Discrete Empirical Interpolation Method for a Nonlinear Thermal Problem

    Directory of Open Access Journals (Sweden)

    Felix Fritzen

    2018-02-01

    Full Text Available A novel algorithmic discussion of the methodological and numerical differences of competing parametric model reduction techniques for nonlinear problems is presented. First, the Galerkin reduced basis (RB formulation is presented, which fails at providing significant gains with respect to the computational efficiency for nonlinear problems. Renowned methods for the reduction of the computing time of nonlinear reduced order models are the Hyper-Reduction and the (Discrete Empirical Interpolation Method (EIM, DEIM. An algorithmic description and a methodological comparison of both methods are provided. The accuracy of the predictions of the hyper-reduced model and the (DEIM in comparison to the Galerkin RB is investigated. All three approaches are applied to a simple uncertainty quantification of a planar nonlinear thermal conduction problem. The results are compared to computationally intense finite element simulations.

  17. Validation of DNA-based identification software by computation of pedigree likelihood ratios.

    Science.gov (United States)

    Slooten, K

    2011-08-01

    Disaster victim identification (DVI) can be aided by DNA-evidence, by comparing the DNA-profiles of unidentified individuals with those of surviving relatives. The DNA-evidence is used optimally when such a comparison is done by calculating the appropriate likelihood ratios. Though conceptually simple, the calculations can be quite involved, especially with large pedigrees, precise mutation models etc. In this article we describe a series of test cases designed to check if software designed to calculate such likelihood ratios computes them correctly. The cases include both simple and more complicated pedigrees, among which inbred ones. We show how to calculate the likelihood ratio numerically and algebraically, including a general mutation model and possibility of allelic dropout. In Appendix A we show how to derive such algebraic expressions mathematically. We have set up these cases to validate new software, called Bonaparte, which performs pedigree likelihood ratio calculations in a DVI context. Bonaparte has been developed by SNN Nijmegen (The Netherlands) for the Netherlands Forensic Institute (NFI). It is available free of charge for non-commercial purposes (see www.dnadvi.nl for details). Commercial licenses can also be obtained. The software uses Bayesian networks and the junction tree algorithm to perform its calculations. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  18. Accuracy of maximum likelihood estimates of a two-state model in single-molecule FRET

    Energy Technology Data Exchange (ETDEWEB)

    Gopich, Irina V. [Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892 (United States)

    2015-01-21

    Photon sequences from single-molecule Förster resonance energy transfer (FRET) experiments can be analyzed using a maximum likelihood method. Parameters of the underlying kinetic model (FRET efficiencies of the states and transition rates between conformational states) are obtained by maximizing the appropriate likelihood function. In addition, the errors (uncertainties) of the extracted parameters can be obtained from the curvature of the likelihood function at the maximum. We study the standard deviations of the parameters of a two-state model obtained from photon sequences with recorded colors and arrival times. The standard deviations can be obtained analytically in a special case when the FRET efficiencies of the states are 0 and 1 and in the limiting cases of fast and slow conformational dynamics. These results are compared with the results of numerical simulations. The accuracy and, therefore, the ability to predict model parameters depend on how fast the transition rates are compared to the photon count rate. In the limit of slow transitions, the key parameters that determine the accuracy are the number of transitions between the states and the number of independent photon sequences. In the fast transition limit, the accuracy is determined by the small fraction of photons that are correlated with their neighbors. The relative standard deviation of the relaxation rate has a “chevron” shape as a function of the transition rate in the log-log scale. The location of the minimum of this function dramatically depends on how well the FRET efficiencies of the states are separated.

  19. Enhancing resolution and contrast in second-harmonic generation microscopy using an advanced maximum likelihood estimation restoration method

    Science.gov (United States)

    Sivaguru, Mayandi; Kabir, Mohammad M.; Gartia, Manas Ranjan; Biggs, David S. C.; Sivaguru, Barghav S.; Sivaguru, Vignesh A.; Berent, Zachary T.; Wagoner Johnson, Amy J.; Fried, Glenn A.; Liu, Gang Logan; Sadayappan, Sakthivel; Toussaint, Kimani C.

    2017-02-01

    Second-harmonic generation (SHG) microscopy is a label-free imaging technique to study collagenous materials in extracellular matrix environment with high resolution and contrast. However, like many other microscopy techniques, the actual spatial resolution achievable by SHG microscopy is reduced by out-of-focus blur and optical aberrations that degrade particularly the amplitude of the detectable higher spatial frequencies. Being a two-photon scattering process, it is challenging to define a point spread function (PSF) for the SHG imaging modality. As a result, in comparison with other two-photon imaging systems like two-photon fluorescence, it is difficult to apply any PSF-engineering techniques to enhance the experimental spatial resolution closer to the diffraction limit. Here, we present a method to improve the spatial resolution in SHG microscopy using an advanced maximum likelihood estimation (AdvMLE) algorithm to recover the otherwise degraded higher spatial frequencies in an SHG image. Through adaptation and iteration, the AdvMLE algorithm calculates an improved PSF for an SHG image and enhances the spatial resolution by decreasing the full-width-at-halfmaximum (FWHM) by 20%. Similar results are consistently observed for biological tissues with varying SHG sources, such as gold nanoparticles and collagen in porcine feet tendons. By obtaining an experimental transverse spatial resolution of 400 nm, we show that the AdvMLE algorithm brings the practical spatial resolution closer to the theoretical diffraction limit. Our approach is suitable for adaptation in micro-nano CT and MRI imaging, which has the potential to impact diagnosis and treatment of human diseases.

  20. Estimation of Cumulative Absolute Velocity using Empirical Green's Function Method

    International Nuclear Information System (INIS)

    Park, Dong Hee; Yun, Kwan Hee; Chang, Chun Joong; Park, Se Moon

    2009-01-01

    In recognition of the needs to develop a new criterion for determining when the OBE (Operating Basis Earthquake) has been exceeded at nuclear power plants, Cumulative Absolute Velocity (CAV) was introduced by EPRI. The concept of CAV is the area accumulation with the values more than 0.025g occurred during every one second. The equation of the CAV is as follows. CAV = ∫ 0 max |a(t)|dt (1) t max = duration of record, a(t) = acceleration (>0.025g) Currently, the OBE exceedance criteria in Korea is Peak Ground Acceleration (PGA, PGA>0.1g). When Odesan earthquake (M L =4.8, January 20th, 2007) and Gyeongju earthquake (M L =3.4, June 2nd, 1999) were occurred, we have had already experiences of PGA greater than 0.1g that did not even cause any damage to the poorly-designed structures nearby. This moderate earthquake has motivated Korea to begin the use of the CAV for OBE exceedance criteria for NPPs. Because the present OBE level has proved itself to be a poor indicator for small-to-moderate earthquakes, for which the low OBE level can cause an inappropriate shut down the plant. A more serious possibility is that this scenario will become a reality at a very high level. Empirical Green's Function method was a simulation technique which can estimate the CAV value and it is hereby introduced

  1. Greenery in the university environment: Students’ preferences and perceived restoration likelihood

    Science.gov (United States)

    2018-01-01

    A large body of evidence shows that interaction with greenery can be beneficial for human stress reduction, emotional states, and improved cognitive function. It can, therefore, be expected that university students might benefit from greenery in the university environment. Before investing in real-life interventions in a university environment, it is necessary to first explore students’ perceptions of greenery in the university environment. This study examined (1) preference for university indoor and outdoor spaces with and without greenery (2) perceived restoration likelihood of university outdoor spaces with and without greenery and (3) if preference and perceived restoration likelihood ratings were modified by demographic characteristics or connectedness to nature in Dutch university students (N = 722). Digital photographic stimuli represented four university spaces (lecture hall, classroom, study area, university outdoor space). For each of the three indoor spaces there were four or five stimuli conditions: (1) the standard design (2) the standard design with a colorful poster (3) the standard design with a nature poster (4) the standard design with a green wall (5) the standard design with a green wall plus interior plants. The university outdoor space included: (1) the standard design (2) the standard design with seating (3) the standard design with colorful artifacts (4) the standard design with green elements (5) the standard design with extensive greenery. Multi-level analyses showed that students gave higher preference ratings to the indoor spaces with a nature poster, a green wall, or a green wall plus interior plants than to the standard designs and the designs with the colorful posters. Students also rated preference and perceived restoration likelihood of the outdoor spaces that included greenery higher than those without. Preference and perceived restoration likelihood were not modified by demographic characteristics, but students with strong

  2. Greenery in the university environment: Students' preferences and perceived restoration likelihood.

    Directory of Open Access Journals (Sweden)

    Nicole van den Bogerd

    Full Text Available A large body of evidence shows that interaction with greenery can be beneficial for human stress reduction, emotional states, and improved cognitive function. It can, therefore, be expected that university students might benefit from greenery in the university environment. Before investing in real-life interventions in a university environment, it is necessary to first explore students' perceptions of greenery in the university environment. This study examined (1 preference for university indoor and outdoor spaces with and without greenery (2 perceived restoration likelihood of university outdoor spaces with and without greenery and (3 if preference and perceived restoration likelihood ratings were modified by demographic characteristics or connectedness to nature in Dutch university students (N = 722. Digital photographic stimuli represented four university spaces (lecture hall, classroom, study area, university outdoor space. For each of the three indoor spaces there were four or five stimuli conditions: (1 the standard design (2 the standard design with a colorful poster (3 the standard design with a nature poster (4 the standard design with a green wall (5 the standard design with a green wall plus interior plants. The university outdoor space included: (1 the standard design (2 the standard design with seating (3 the standard design with colorful artifacts (4 the standard design with green elements (5 the standard design with extensive greenery. Multi-level analyses showed that students gave higher preference ratings to the indoor spaces with a nature poster, a green wall, or a green wall plus interior plants than to the standard designs and the designs with the colorful posters. Students also rated preference and perceived restoration likelihood of the outdoor spaces that included greenery higher than those without. Preference and perceived restoration likelihood were not modified by demographic characteristics, but students with strong

  3. Greenery in the university environment: Students' preferences and perceived restoration likelihood.

    Science.gov (United States)

    van den Bogerd, Nicole; Dijkstra, S Coosje; Seidell, Jacob C; Maas, Jolanda

    2018-01-01

    A large body of evidence shows that interaction with greenery can be beneficial for human stress reduction, emotional states, and improved cognitive function. It can, therefore, be expected that university students might benefit from greenery in the university environment. Before investing in real-life interventions in a university environment, it is necessary to first explore students' perceptions of greenery in the university environment. This study examined (1) preference for university indoor and outdoor spaces with and without greenery (2) perceived restoration likelihood of university outdoor spaces with and without greenery and (3) if preference and perceived restoration likelihood ratings were modified by demographic characteristics or connectedness to nature in Dutch university students (N = 722). Digital photographic stimuli represented four university spaces (lecture hall, classroom, study area, university outdoor space). For each of the three indoor spaces there were four or five stimuli conditions: (1) the standard design (2) the standard design with a colorful poster (3) the standard design with a nature poster (4) the standard design with a green wall (5) the standard design with a green wall plus interior plants. The university outdoor space included: (1) the standard design (2) the standard design with seating (3) the standard design with colorful artifacts (4) the standard design with green elements (5) the standard design with extensive greenery. Multi-level analyses showed that students gave higher preference ratings to the indoor spaces with a nature poster, a green wall, or a green wall plus interior plants than to the standard designs and the designs with the colorful posters. Students also rated preference and perceived restoration likelihood of the outdoor spaces that included greenery higher than those without. Preference and perceived restoration likelihood were not modified by demographic characteristics, but students with strong

  4. Likelihood Approximation With Hierarchical Matrices For Large Spatial Datasets

    KAUST Repository

    Litvinenko, Alexander

    2017-09-03

    We use available measurements to estimate the unknown parameters (variance, smoothness parameter, and covariance length) of a covariance function by maximizing the joint Gaussian log-likelihood function. To overcome cubic complexity in the linear algebra, we approximate the discretized covariance function in the hierarchical (H-) matrix format. The H-matrix format has a log-linear computational cost and storage O(kn log n), where the rank k is a small integer and n is the number of locations. The H-matrix technique allows us to work with general covariance matrices in an efficient way, since H-matrices can approximate inhomogeneous covariance functions, with a fairly general mesh that is not necessarily axes-parallel, and neither the covariance matrix itself nor its inverse have to be sparse. We demonstrate our method with Monte Carlo simulations and an application to soil moisture data. The C, C++ codes and data are freely available.

  5. THE EMPIRICAL METHOD OF INVESTIGATING THE CHILDHOOD SUBCULTURE: GROUP OF CHILDREN BEHAVIOR OBSERVATION IN THE GUESTHOUSE POOL

    Directory of Open Access Journals (Sweden)

    Ms. Yelena N. Suvorkina

    2016-12-01

    Full Text Available The article deals with one of the empirical research methods investigating the childhood sub-culture. The method is called observation. The author marked general theoretical position, recommendations on its implementation. Based on observations of the group of children behavior in the guesthouse pool it is found out that such category, as honesty is very important in the organization of the order, taking into account that the subculture of childhood is an open, self-organizing system. In the pool, the children come up with a wide variety of games. The adjacent areas are also involved. The author identified two borders, taking place for the child: a clear (fixed border is the side of the pool and unclear border as the transitional designation of states, qualities (dangerous – safe.

  6. Reflective equilibrium and empirical data: third person moral experiences in empirical medical ethics.

    Science.gov (United States)

    De Vries, Martine; Van Leeuwen, Evert

    2010-11-01

    In ethics, the use of empirical data has become more and more popular, leading to a distinct form of applied ethics, namely empirical ethics. This 'empirical turn' is especially visible in bioethics. There are various ways of combining empirical research and ethical reflection. In this paper we discuss the use of empirical data in a special form of Reflective Equilibrium (RE), namely the Network Model with Third Person Moral Experiences. In this model, the empirical data consist of the moral experiences of people in a practice. Although inclusion of these moral experiences in this specific model of RE can be well defended, their use in the application of the model still raises important questions. What precisely are moral experiences? How to determine relevance of experiences, in other words: should there be a selection of the moral experiences that are eventually used in the RE? How much weight should the empirical data have in the RE? And the key question: can the use of RE by empirical ethicists really produce answers to practical moral questions? In this paper we start to answer the above questions by giving examples taken from our research project on understanding the norm of informed consent in the field of pediatric oncology. We especially emphasize that incorporation of empirical data in a network model can reduce the risk of self-justification and bias and can increase the credibility of the RE reached. © 2009 Blackwell Publishing Ltd.

  7. Cosmic shear measurement with maximum likelihood and maximum a posteriori inference

    Science.gov (United States)

    Hall, Alex; Taylor, Andy

    2017-06-01

    We investigate the problem of noise bias in maximum likelihood and maximum a posteriori estimators for cosmic shear. We derive the leading and next-to-leading order biases and compute them in the context of galaxy ellipticity measurements, extending previous work on maximum likelihood inference for weak lensing. We show that a large part of the bias on these point estimators can be removed using information already contained in the likelihood when a galaxy model is specified, without the need for external calibration. We test these bias-corrected estimators on simulated galaxy images similar to those expected from planned space-based weak lensing surveys, with promising results. We find that the introduction of an intrinsic shape prior can help with mitigation of noise bias, such that the maximum a posteriori estimate can be made less biased than the maximum likelihood estimate. Second-order terms offer a check on the convergence of the estimators, but are largely subdominant. We show how biases propagate to shear estimates, demonstrating in our simple set-up that shear biases can be reduced by orders of magnitude and potentially to within the requirements of planned space-based surveys at mild signal-to-noise ratio. We find that second-order terms can exhibit significant cancellations at low signal-to-noise ratio when Gaussian noise is assumed, which has implications for inferring the performance of shear-measurement algorithms from simplified simulations. We discuss the viability of our point estimators as tools for lensing inference, arguing that they allow for the robust measurement of ellipticity and shear.

  8. Maximum likelihood estimation for Cox's regression model under nested case-control sampling

    DEFF Research Database (Denmark)

    Scheike, Thomas Harder; Juul, Anders

    2004-01-01

    -like growth factor I was associated with ischemic heart disease. The study was based on a population of 3784 Danes and 231 cases of ischemic heart disease where controls were matched on age and gender. We illustrate the use of the MLE for these data and show how the maximum likelihood framework can be used......Nested case-control sampling is designed to reduce the costs of large cohort studies. It is important to estimate the parameters of interest as efficiently as possible. We present a new maximum likelihood estimator (MLE) for nested case-control sampling in the context of Cox's proportional hazards...... model. The MLE is computed by the EM-algorithm, which is easy to implement in the proportional hazards setting. Standard errors are estimated by a numerical profile likelihood approach based on EM aided differentiation. The work was motivated by a nested case-control study that hypothesized that insulin...

  9. The skewed weak lensing likelihood: why biases arise, despite data and theory being sound.

    Science.gov (United States)

    Sellentin, Elena; Heymans, Catherine; Harnois-Déraps, Joachim

    2018-04-01

    We derive the essentials of the skewed weak lensing likelihood via a simple Hierarchical Forward Model. Our likelihood passes four objective and cosmology-independent tests which a standard Gaussian likelihood fails. We demonstrate that sound weak lensing data are naturally biased low, since they are drawn from a skewed distribution. This occurs already in the framework of ΛCDM. Mathematically, the biases arise because noisy two-point functions follow skewed distributions. This form of bias is already known from CMB analyses, where the low multipoles have asymmetric error bars. Weak lensing is more strongly affected by this asymmetry as galaxies form a discrete set of shear tracer particles, in contrast to a smooth shear field. We demonstrate that the biases can be up to 30% of the standard deviation per data point, dependent on the properties of the weak lensing survey and the employed filter function. Our likelihood provides a versatile framework with which to address this bias in future weak lensing analyses.

  10. Maximum likelihood positioning algorithm for high-resolution PET scanners

    International Nuclear Information System (INIS)

    Gross-Weege, Nicolas; Schug, David; Hallen, Patrick; Schulz, Volkmar

    2016-01-01

    Purpose: In high-resolution positron emission tomography (PET), lightsharing elements are incorporated into typical detector stacks to read out scintillator arrays in which one scintillator element (crystal) is smaller than the size of the readout channel. In order to identify the hit crystal by means of the measured light distribution, a positioning algorithm is required. One commonly applied positioning algorithm uses the center of gravity (COG) of the measured light distribution. The COG algorithm is limited in spatial resolution by noise and intercrystal Compton scatter. The purpose of this work is to develop a positioning algorithm which overcomes this limitation. Methods: The authors present a maximum likelihood (ML) algorithm which compares a set of expected light distributions given by probability density functions (PDFs) with the measured light distribution. Instead of modeling the PDFs by using an analytical model, the PDFs of the proposed ML algorithm are generated assuming a single-gamma-interaction model from measured data. The algorithm was evaluated with a hot-rod phantom measurement acquired with the preclinical HYPERION II D PET scanner. In order to assess the performance with respect to sensitivity, energy resolution, and image quality, the ML algorithm was compared to a COG algorithm which calculates the COG from a restricted set of channels. The authors studied the energy resolution of the ML and the COG algorithm regarding incomplete light distributions (missing channel information caused by detector dead time). Furthermore, the authors investigated the effects of using a filter based on the likelihood values on sensitivity, energy resolution, and image quality. Results: A sensitivity gain of up to 19% was demonstrated in comparison to the COG algorithm for the selected operation parameters. Energy resolution and image quality were on a similar level for both algorithms. Additionally, the authors demonstrated that the performance of the ML

  11. Review of Elaboration Likelihood Model of persuasion

    OpenAIRE

    藤原, 武弘; 神山, 貴弥

    1989-01-01

    This article mainly introduces Elaboration Likelihood Model (ELM), proposed by Petty & Cacioppo, that is, a general attitude change theory. ELM posturates two routes to persuasion; central and peripheral route. Attitude change by central route is viewed as resulting from a diligent consideration of the issue-relevant informations presented. On the other hand, attitude change by peripheral route is viewed as resulting from peripheral cues in the persuasion context. Secondly we compare these tw...

  12. Validation of software for calculating the likelihood ratio for parentage and kinship.

    Science.gov (United States)

    Drábek, J

    2009-03-01

    Although the likelihood ratio is a well-known statistical technique, commercial off-the-shelf (COTS) software products for its calculation are not sufficiently validated to suit general requirements for the competence of testing and calibration laboratories (EN/ISO/IEC 17025:2005 norm) per se. The software in question can be considered critical as it directly weighs the forensic evidence allowing judges to decide on guilt or innocence or to identify person or kin (i.e.: in mass fatalities). For these reasons, accredited laboratories shall validate likelihood ratio software in accordance with the above norm. To validate software for calculating the likelihood ratio in parentage/kinship scenarios I assessed available vendors, chose two programs (Paternity Index and familias) for testing, and finally validated them using tests derived from elaboration of the available guidelines for the field of forensics, biomedicine, and software engineering. MS Excel calculation using known likelihood ratio formulas or peer-reviewed results of difficult paternity cases were used as a reference. Using seven testing cases, it was found that both programs satisfied the requirements for basic paternity cases. However, only a combination of two software programs fulfills the criteria needed for our purpose in the whole spectrum of functions under validation with the exceptions of providing algebraic formulas in cases of mutation and/or silent allele.

  13. Empirical questions for collective-behaviour modelling

    Indian Academy of Sciences (India)

    The collective behaviour of groups of social animals has been an active topic of study ... Models have been successful at reproducing qualitative features of ... quantitative and detailed empirical results for a range of animal systems. ... standard method [23], the redundant information recorded by the cameras can be used to.

  14. Likelihood for transcriptions in a genetic regulatory system under asymmetric stable Lévy noise.

    Science.gov (United States)

    Wang, Hui; Cheng, Xiujun; Duan, Jinqiao; Kurths, Jürgen; Li, Xiaofan

    2018-01-01

    This work is devoted to investigating the evolution of concentration in a genetic regulation system, when the synthesis reaction rate is under additive and multiplicative asymmetric stable Lévy fluctuations. By focusing on the impact of skewness (i.e., non-symmetry) in the probability distributions of noise, we find that via examining the mean first exit time (MFET) and the first escape probability (FEP), the asymmetric fluctuations, interacting with nonlinearity in the system, lead to peculiar likelihood for transcription. This includes, in the additive noise case, realizing higher likelihood of transcription for larger positive skewness (i.e., asymmetry) index β, causing a stochastic bifurcation at the non-Gaussianity index value α = 1 (i.e., it is a separating point or line for the likelihood for transcription), and achieving a turning point at the threshold value β≈-0.5 (i.e., beyond which the likelihood for transcription suddenly reversed for α values). The stochastic bifurcation and turning point phenomena do not occur in the symmetric noise case (β = 0). While in the multiplicative noise case, non-Gaussianity index value α = 1 is a separating point or line for both the MFET and the FEP. We also investigate the noise enhanced stability phenomenon. Additionally, we are able to specify the regions in the whole parameter space for the asymmetric noise, in which we attain desired likelihood for transcription. We have conducted a series of numerical experiments in "regulating" the likelihood of gene transcription by tuning asymmetric stable Lévy noise indexes. This work offers insights for possible ways of achieving gene regulation in experimental research.

  15. Likelihood-based Dynamic Factor Analysis for Measurement and Forecasting

    NARCIS (Netherlands)

    Jungbacker, B.M.J.P.; Koopman, S.J.

    2015-01-01

    We present new results for the likelihood-based analysis of the dynamic factor model. The latent factors are modelled by linear dynamic stochastic processes. The idiosyncratic disturbance series are specified as autoregressive processes with mutually correlated innovations. The new results lead to

  16. Reconceptualizing Social Influence in Counseling: The Elaboration Likelihood Model.

    Science.gov (United States)

    McNeill, Brian W.; Stoltenberg, Cal D.

    1989-01-01

    Presents Elaboration Likelihood Model (ELM) of persuasion (a reconceptualization of the social influence process) as alternative model of attitude change. Contends ELM unifies conflicting social psychology results and can potentially account for inconsistent research findings in counseling psychology. Provides guidelines on integrating…

  17. Evaluation and Recalibration of Empirical Constant for Estimation of Reference Crop Evapotranspiration against the Modified Penman Method

    Science.gov (United States)

    Sasireka, K.; Jagan Mohan Reddy, C.; Charan Reddy, C.; Ramakrishnan, K.

    2017-07-01

    The major demand in our country is irrigation demand. Looking to the low irrigation potential and small water resources, it is felt necessary to see that water must be used economically and efficiently. This may be achieved by using latest methods of determination of water requirements for crops and applying the proper water management practices. Evapotranspiration (ET) is a basic for calculation of water requirement for crops. The various popular empirical equations for reference crop evapotranspiration (ETr) belong to three categories namely, Temperature, Radiation based methods and Combined methods. The above methods are site specific; hence it is necessary to recalibrate the coefficients for applying them in India. In the present paper, the standard combined method namely FAO modified Penman method was used to recalibrate the constants in temperature based (TB) methods and it can also be used to determine the ETr for the selected station. Four TB evapotranspiration models namely Blaney-Criddle, Romanenko, Kharrufa, and, Thronthwaite methods are recalibrated and the constant in each method are redefined for the data from Lekkur station, Cuddalore district in India. The result shows that, large error existed when ETr has been calculated with original constants. Hence regression equations were developed to minimise these variations in magnitude. It was found that out of four methods the Blaney-Criddle method suits better for the selected region.

  18. Physician Bayesian updating from personal beliefs about the base rate and likelihood ratio.

    Science.gov (United States)

    Rottman, Benjamin Margolin

    2017-02-01

    Whether humans can accurately make decisions in line with Bayes' rule has been one of the most important yet contentious topics in cognitive psychology. Though a number of paradigms have been used for studying Bayesian updating, rarely have subjects been allowed to use their own preexisting beliefs about the prior and the likelihood. A study is reported in which physicians judged the posttest probability of a diagnosis for a patient vignette after receiving a test result, and the physicians' posttest judgments were compared to the normative posttest calculated from their own beliefs in the sensitivity and false positive rate of the test (likelihood ratio) and prior probability of the diagnosis. On the one hand, the posttest judgments were strongly related to the physicians' beliefs about both the prior probability as well as the likelihood ratio, and the priors were used considerably more strongly than in previous research. On the other hand, both the prior and the likelihoods were still not used quite as much as they should have been, and there was evidence of other nonnormative aspects to the updating, such as updating independent of the likelihood beliefs. By focusing on how physicians use their own prior beliefs for Bayesian updating, this study provides insight into how well experts perform probabilistic inference in settings in which they rely upon their own prior beliefs rather than experimenter-provided cues. It suggests that there is reason to be optimistic about experts' abilities, but that there is still considerable need for improvement.

  19. Powdered alcohol: Awareness and likelihood of use among a sample of college students.

    Science.gov (United States)

    Vail-Smith, Karen; Chaney, Beth H; Martin, Ryan J; Don Chaney, J

    2016-01-01

    In March 2015, the Alcohol and Tobacco Tax and Trade Bureau approved the sale of Palcohol, the first powdered alcohol product to be marketed and sold in the U.S. Powdered alcohol is freeze-dried, and one individual-serving size packet added to 6 ounces of liquid is equivalent to a standard drink. This study assessed awareness of powered alcohol and likelihood to use and/or misuse powdered alcohol among college students. Surveys were administered to a convenience sample of 1,841 undergraduate students. Only 16.4% of respondents had heard of powdered alcohol. After being provided a brief description of powdered alcohol, 23% indicated that they would use the product if available, and of those, 62.1% also indicated likelihood of misusing the product (eg, snorting it, mixing it with alcohol). Caucasian students (OR = 1.5) and hazardous drinkers (based on AUDIT-C scores; OR = 4.7) were significantly more likely to indicate likelihood of use. Hazardous drinkers were also six times more likely to indicate likelihood to misuse the product. These findings can inform upstream prevention efforts in states debating bans on powdered alcohol. In states where powdered alcohol will soon be available, alcohol education initiatives should be updated to include information on the potential risks of use and be targeted to those populations most likely to misuse. This is the first peer-reviewed study to assess the awareness of and likelihood to use and/or misuse powdered alcohol, a potentially emerging form of alcohol. © American Academy of Addiction Psychiatry.

  20. Practical Statistics for LHC Physicists: Descriptive Statistics, Probability and Likelihood (1/3)

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    These lectures cover those principles and practices of statistics that are most relevant for work at the LHC. The first lecture discusses the basic ideas of descriptive statistics, probability and likelihood. The second lecture covers the key ideas in the frequentist approach, including confidence limits, profile likelihoods, p-values, and hypothesis testing. The third lecture covers inference in the Bayesian approach. Throughout, real-world examples will be used to illustrate the practical application of the ideas. No previous knowledge is assumed.

  1. A composite likelihood approach for spatially correlated survival data

    Science.gov (United States)

    Paik, Jane; Ying, Zhiliang

    2013-01-01

    The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory. PMID:24223450

  2. A composite likelihood approach for spatially correlated survival data.

    Science.gov (United States)

    Paik, Jane; Ying, Zhiliang

    2013-01-01

    The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory.

  3. A Scoring Tool to Identify East African HIV-1 Serodiscordant Partnerships with a High Likelihood of Pregnancy.

    Directory of Open Access Journals (Sweden)

    Renee Heffron

    Full Text Available HIV-1 prevention programs targeting HIV-1 serodiscordant couples need to identify couples that are likely to become pregnant to facilitate discussions about methods to minimize HIV-1 risk during pregnancy attempts (i.e. safer conception or effective contraception when pregnancy is unintended. A clinical prediction tool could be used to identify HIV-1 serodiscordant couples with a high likelihood of pregnancy within one year.Using standardized clinical prediction methods, we developed and validated a tool to identify heterosexual East African HIV-1 serodiscordant couples with an increased likelihood of becoming pregnant in the next year. Datasets were from three prospectively followed cohorts, including nearly 7,000 couples from Kenya and Uganda participating in HIV-1 prevention trials and delivery projects.The final score encompassed the age of the woman, woman's number of children living, partnership duration, having had condomless sex in the past month, and non-use of an effective contraceptive. The area under the curve (AUC for the probability of the score to correctly predict pregnancy was 0.74 (95% CI 0.72-0.76. Scores ≥ 7 predicted a pregnancy incidence of >17% per year and captured 78% of the pregnancies. Internal and external validation confirmed the predictive ability of the score.A pregnancy likelihood score encompassing basic demographic, clinical and behavioral factors defined African HIV-1 serodiscordant couples with high one-year pregnancy incidence rates. This tool could be used to engage African HIV-1 serodiscordant couples in counseling discussions about fertility intentions in order to offer services for safer conception or contraception that align with their reproductive goals.

  4. Empirical evaluation of decision support systems: Needs, definitions, potential methods, and an example pertaining to waterfowl management

    Science.gov (United States)

    Sojda, R.S.

    2007-01-01

    Decision support systems are often not empirically evaluated, especially the underlying modelling components. This can be attributed to such systems necessarily being designed to handle complex and poorly structured problems and decision making. Nonetheless, evaluation is critical and should be focused on empirical testing whenever possible. Verification and validation, in combination, comprise such evaluation. Verification is ensuring that the system is internally complete, coherent, and logical from a modelling and programming perspective. Validation is examining whether the system is realistic and useful to the user or decision maker, and should answer the question: “Was the system successful at addressing its intended purpose?” A rich literature exists on verification and validation of expert systems and other artificial intelligence methods; however, no single evaluation methodology has emerged as preeminent. At least five approaches to validation are feasible. First, under some conditions, decision support system performance can be tested against a preselected gold standard. Second, real-time and historic data sets can be used for comparison with simulated output. Third, panels of experts can be judiciously used, but often are not an option in some ecological domains. Fourth, sensitivity analysis of system outputs in relation to inputs can be informative. Fifth, when validation of a complete system is impossible, examining major components can be substituted, recognizing the potential pitfalls. I provide an example of evaluation of a decision support system for trumpeter swan (Cygnus buccinator) management that I developed using interacting intelligent agents, expert systems, and a queuing system. Predicted swan distributions over a 13-year period were assessed against observed numbers. Population survey numbers and banding (ringing) studies may provide long term data useful in empirical evaluation of decision support.

  5. Probabilistic performance estimators for computational chemistry methods: The empirical cumulative distribution function of absolute errors

    Science.gov (United States)

    Pernot, Pascal; Savin, Andreas

    2018-06-01

    Benchmarking studies in computational chemistry use reference datasets to assess the accuracy of a method through error statistics. The commonly used error statistics, such as the mean signed and mean unsigned errors, do not inform end-users on the expected amplitude of prediction errors attached to these methods. We show that, the distributions of model errors being neither normal nor zero-centered, these error statistics cannot be used to infer prediction error probabilities. To overcome this limitation, we advocate for the use of more informative statistics, based on the empirical cumulative distribution function of unsigned errors, namely, (1) the probability for a new calculation to have an absolute error below a chosen threshold and (2) the maximal amplitude of errors one can expect with a chosen high confidence level. Those statistics are also shown to be well suited for benchmarking and ranking studies. Moreover, the standard error on all benchmarking statistics depends on the size of the reference dataset. Systematic publication of these standard errors would be very helpful to assess the statistical reliability of benchmarking conclusions.

  6. Simultaneous determination of exponential background and Gaussian peak functions in gamma ray scintillation spectrometers by maximum likelihood technique

    International Nuclear Information System (INIS)

    Eisler, P.; Youl, S.; Lwin, T.; Nelson, G.

    1983-01-01

    Simultaneous fitting of peaks and background functions from gamma-ray spectrometry using multichannel pulse height analysis is considered. The specific case of Gaussian peak and exponential background is treated in detail with respect to simultaneous estimation of both functions by using a technique which incorporates maximum likelihood method as well as a graphical method. Theoretical expressions for the standard errors of the estimates are also obtained. The technique is demonstrated for two experimental data sets. (orig.)

  7. Composite likelihood and two-stage estimation in family studies

    DEFF Research Database (Denmark)

    Andersen, Elisabeth Anne Wreford

    2004-01-01

    In this paper register based family studies provide the motivation for linking a two-stage estimation procedure in copula models for multivariate failure time data with a composite likelihood approach. The asymptotic properties of the estimators in both parametric and semi-parametric models are d...

  8. The Rise and Fall of Andean Empires: El Nino History Lessons.

    Science.gov (United States)

    Wright, Kenneth R.

    2000-01-01

    Provides information on El Nino and the methods for investigating ancient climate record. Traces the rise and fall of the Andean empires focusing on the climatic forces that each empire (Tiwanaku, Wari, Moche, and Inca) endured. States that modern societies should learn from the experiences of these ancient civilizations. (CMK)

  9. Women's autonomy and reproductive health care utilisation: empirical evidence from Tajikistan.

    Science.gov (United States)

    Kamiya, Yusuke

    2011-10-01

    Women's autonomy is widely considered to be a key to improving maternal health in developing countries, whereas there is no consistent empirical evidence to support this claim. This paper examines whether or not and how women's autonomy within the household affects the use of reproductive health care, using a household survey data from Tajikistan. Estimation is performed by the bivariate probit model whereby woman's use of health services and the level of women's autonomy are recursively and simultaneously determined. The data is from a sample of women aged 15-49 from the Tajikistan Living Standard Measurement Survey 2007. Women's autonomy as measured by women's decision-making on household financial matters increase the likelihood that a woman receives antenatal and delivery care, whilst it has a negative effect on the probability of attending to four or more antenatal consultations. The hypothesis that women's autonomy and reproductive health care utilisation are independently determined is rejected for most of the estimation specifications, indicating the importance of taking into account the endogenous nature of women's autonomy when assessing its effect on health care use. The empirical results reconfirm the assertion that women's status within the household is closely linked to reproductive health care utilisation in developing countries. Policymakers therefore need not only to implement not only direct health interventions but also to focus on broader social policies which address women's empowerment. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  10. An extended technology acceptance model for detecting influencing factors: An empirical investigation

    Directory of Open Access Journals (Sweden)

    Mohamd Hakkak

    2013-11-01

    Full Text Available The rapid diffusion of the Internet has radically changed the delivery channels applied by the financial services industry. The aim of this study is to identify the influencing factors that encourage customers to adopt online banking in Khorramabad. The research constructs are developed based on the technology acceptance model (TAM and incorporates some extra important control variables. The model is empirically verified to study the factors influencing the online banking adoption behavior of 210 customers of Tejarat Banks in Khorramabad. The findings of the study suggest that the quality of the internet connection, the awareness of online banking and its benefits, the social influence and computer self-efficacy have significant impacts on the perceived usefulness (PU and perceived ease of use (PEOU of online banking acceptance. Trust and resistance to change also have significant impact on the attitude towards the likelihood of adopting online banking.

  11. NOx PREDICTION FOR FBC BOILERS USING EMPIRICAL MODELS

    Directory of Open Access Journals (Sweden)

    Jiří Štefanica

    2014-02-01

    Full Text Available Reliable prediction of NOx emissions can provide useful information for boiler design and fuel selection. Recently used kinetic prediction models for FBC boilers are overly complex and require large computing capacity. Even so, there are many uncertainties in the case of FBC boilers. An empirical modeling approach for NOx prediction has been used exclusively for PCC boilers. No reference is available for modifying this method for FBC conditions. This paper presents possible advantages of empirical modeling based prediction of NOx emissions for FBC boilers, together with a discussion of its limitations. Empirical models are reviewed, and are applied to operation data from FBC boilers used for combusting Czech lignite coal or coal-biomass mixtures. Modifications to the model are proposed in accordance with theoretical knowledge and prediction accuracy.

  12. On Integrating Student Empirical Software Engineering Studies with Research and Teaching Goals

    NARCIS (Netherlands)

    Galster, Matthias; Tofan, Dan; Avgeriou, Paris

    2012-01-01

    Background: Many empirical software engineering studies use students as subjects and are conducted as part of university courses. Aim: We aim at reporting our experiences with using guidelines for integrating empirical studies with our research and teaching goals. Method: We document our experience

  13. LIKELIHOOD ESTIMATION OF PARAMETERS USING SIMULTANEOUSLY MONITORED PROCESSES

    DEFF Research Database (Denmark)

    Friis-Hansen, Peter; Ditlevsen, Ove Dalager

    2004-01-01

    The topic is maximum likelihood inference from several simultaneously monitored response processes of a structure to obtain knowledge about the parameters of other not monitored but important response processes when the structure is subject to some Gaussian load field in space and time. The consi....... The considered example is a ship sailing with a given speed through a Gaussian wave field....

  14. HLA Match Likelihoods for Hematopoietic Stem-Cell Grafts in the U.S. Registry

    Science.gov (United States)

    Gragert, Loren; Eapen, Mary; Williams, Eric; Freeman, John; Spellman, Stephen; Baitty, Robert; Hartzman, Robert; Rizzo, J. Douglas; Horowitz, Mary; Confer, Dennis; Maiers, Martin

    2018-01-01

    Background Hematopoietic stem-cell transplantation (HSCT) is a potentially lifesaving therapy for several blood cancers and other diseases. For patients without a suitable related HLA-matched donor, unrelated-donor registries of adult volunteers and banked umbilical cord–blood units, such as the Be the Match Registry operated by the National Marrow Donor Program (NMDP), provide potential sources of donors. Our goal in the present study was to measure the likelihood of finding a suitable donor in the U.S. registry. Methods Using human HLA data from the NMDP donor and cord-blood-unit registry, we built population-based genetic models for 21 U.S. racial and ethnic groups to predict the likelihood of identifying a suitable donor (either an adult donor or a cord-blood unit) for patients in each group. The models incorporated the degree of HLA matching, adult-donor availability (i.e., ability to donate), and cord-blood-unit cell dose. Results Our models indicated that most candidates for HSCT will have a suitable (HLA-matched or minimally mismatched) adult donor. However, many patients will not have an optimal adult donor — that is, a donor who is matched at high resolution at HLA-A, HLA-B, HLA-C, and HLA-DRB1. The likelihood of finding an optimal donor varies among racial and ethnic groups, with the highest probability among whites of European descent, at 75%, and the lowest probability among blacks of South or Central American descent, at 16%. Likelihoods for other groups are intermediate. Few patients will have an optimal cord-blood unit — that is, one matched at the antigen level at HLA-A and HLA-B and matched at high resolution at HLA-DRB1. However, cord-blood units mismatched at one or two HLA loci are available for almost all patients younger than 20 years of age and for more than 80% of patients 20 years of age or older, regardless of racial and ethnic background. Conclusions Most patients likely to benefit from HSCT will have a donor. Public investment in

  15. An empirical formula for scattered neutron components in fast neutron radiography

    International Nuclear Information System (INIS)

    Dou Haifeng; Tang Bin

    2011-01-01

    Scattering neutrons are one of the key factors that may affect the images of fast neutron radiography. In this paper, a mathematical model for scattered neutrons is developed on a cylinder sample, and an empirical formula for scattered neutrons is obtained. According to the results given by Monte Carlo methods, the parameters in the empirical formula are obtained with curve fitting, which confirms the logicality of the empirical formula. The curve-fitted parameters of common materials such as 6 LiD are given. (authors)

  16. Likelihood inference for a fractionally cointegrated vector autoregressive model

    DEFF Research Database (Denmark)

    Johansen, Søren; Ørregård Nielsen, Morten

    2012-01-01

    such that the process X_{t} is fractional of order d and cofractional of order d-b; that is, there exist vectors ß for which ß'X_{t} is fractional of order d-b, and no other fractionality order is possible. We define the statistical model by 0inference when the true values satisfy b0¿1/2 and d0-b0......We consider model based inference in a fractionally cointegrated (or cofractional) vector autoregressive model with a restricted constant term, ¿, based on the Gaussian likelihood conditional on initial values. The model nests the I(d) VAR model. We give conditions on the parameters...... process in the parameters when errors are i.i.d. with suitable moment conditions and initial values are bounded. When the limit is deterministic this implies uniform convergence in probability of the conditional likelihood function. If the true value b0>1/2, we prove that the limit distribution of (ß...

  17. Empirical Bayesian inference and model uncertainty

    International Nuclear Information System (INIS)

    Poern, K.

    1994-01-01

    This paper presents a hierarchical or multistage empirical Bayesian approach for the estimation of uncertainty concerning the intensity of a homogeneous Poisson process. A class of contaminated gamma distributions is considered to describe the uncertainty concerning the intensity. These distributions in turn are defined through a set of secondary parameters, the knowledge of which is also described and updated via Bayes formula. This two-stage Bayesian approach is an example where the modeling uncertainty is treated in a comprehensive way. Each contaminated gamma distributions, represented by a point in the 3D space of secondary parameters, can be considered as a specific model of the uncertainty about the Poisson intensity. Then, by the empirical Bayesian method each individual model is assigned a posterior probability

  18. Organizational Justice and Men's Likelihood to Sexually Harass: The Moderating Role of Sexism and Personality

    Science.gov (United States)

    Krings, Franciska; Facchin, Stephanie

    2009-01-01

    This study demonstrated relations between men's perceptions of organizational justice and increased sexual harassment proclivities. Respondents reported higher likelihood to sexually harass under conditions of low interactional justice, suggesting that sexual harassment likelihood may increase as a response to perceived injustice. Moreover, the…

  19. An empirical investigation of Australian Stock Exchange data

    Science.gov (United States)

    Bertram, William K.

    2004-10-01

    We present an empirical study of high frequency Australian equity data examining the behaviour of distribution tails and the existence of long memory. A method is presented allowing us to deal with Australian Stock Exchange data by splitting it into two separate data series representing an intraday and overnight component. Power-law exponents for the empirical density functions are estimated and compared with results from other studies. Using the autocorrelation and variance plots we find there to be a strong indication of long-memory type behaviour in the absolute return, volume and transaction frequency.

  20. Alternative Approaches to Evaluation in Empirical Microeconomics

    Science.gov (United States)

    Blundell, Richard; Dias, Monica Costa

    2009-01-01

    This paper reviews some of the most popular policy evaluation methods in empirical microeconomics: social experiments, natural experiments, matching, instrumental variables, discontinuity design, and control functions. It discusses identification of traditionally used average parameters and more complex distributional parameters. The adequacy,…