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Alternating imputation posterior estimation of models with crossed random effects

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2011年第1期55卷 12-25页

作者： Cho, S. -J. Rabe-Hesketh, S. Vanderbilt Univ Peabody Coll Nashville TN 37203 USA Univ Calif Berkeley Grad Sch Educ Berkeley CA 94720 USA Univ London Inst Educ London WC1E 7HU England

generalized linear mixed models or latent variable models for categorical data are difficult to estimate if the random effects or latent variables vary at non-nested levels, such as persons and test items. Clayton and Rasbash (1999) suggested an Alternating Imputation Posterior (AIP) algorithm for approximate maximum likelihood estimation. For item response models with random item effects, the algorithm iterates between an item wing in which the item mean and variance are estimated for given person effects and a person wing in which the person mean and variance are estimated for given item effects. The person effects used for the item wing are sampled from the conditional posterior distribution estimated in the person wing and vice versa. Clayton and Rasbash (1999) used marginal quasi-likelihood (MQL) and penalized quasi-likelihood (PQL) estimation within the AIP algorithm, but this method has been shown to produce biased estimates in many situations, so we use maximum likelihood estimation with adaptive quadrature. We apply the proposed algorithm to the famous salamander mating data, comparing the estimates with many other methods, and to an educational testing dataset. We also present a simulation study to assess performance of the AIP algorithm and the Laplace approximation with different numbers of items and persons and a range of item and person variances. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Adaptive quadrature Crossed random effects generalized linear mixed model Item response theory Laplace approximation Random cross-classification Salamander mating data Two-way error components

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Analysis of housing prices by GEE and GLMM methodologies: a longitudinal study

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APPLIED STOCHASTIC modelS IN BUSINESS AND INDUSTRY 2013年第5期29卷 552-563页

作者： Pardo, M. C. Perez, T. Univ Complutense Madrid Dept Stat & OR 1 Madrid Spain Univ Complutense Madrid Dept Stat & OR 3 Madrid Spain

The Spanish economic crisis has led to a significant reduction in housing sales, and therefore, there has been a decrease in housing prices. In this paper, we analyze changes in the average housing price throughout Spain. We use quarterly data from a random sample of 150 municipalities from the first quarter (Q1) of 2005, before the financial crisis started, to Q1 2010. Our analysis uses generalized estimating equation and generalized linear mixed model approaches. Data published for Q2, Q3, and Q4 2010 are compared with the data fitted using these models. Finally, the methods are compared with time-series models. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： housing price generalized estimating equations generalized linear mixed model time-series model

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Analyzing ordinal data from a split-plot design in the presence of a random block effect

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QUALITY ENGINEERING 2017年第4期29卷 553-562页

作者： Arnouts, Heidi Goos, Peter Univ Antwerp Dept Engn Management Prinsstr 13 B-2000 Antwerp Belgium Univ Leuven Dept Biosyst Leuven Belgium

Many industrial experiments involve some factors that are hard to change. In this situation, experimenters often choose to perform an experiment with restricted randomization, such as a split-plot or a strip-plot experiment. In this article, we discuss the analysis of an experiment concerning the adhesion between steel tire cords and rubber. Besides an ordinal response, the experiment also involves one hard-to-change factor. Therefore, the experimenters performed a split-plot experiment. An additional complication of the experiment is that there is also a blocking factor. A proper analysis of the experiment requires the inclusion of random effects in the model to account for its split-plot nature and its blocked nature. The need for random effects and the ordinal response necessitate the use of a mixed cumulative logit model.

关键词： cumulative logit regression generalized linear mixed model random and fixed blocks split-plot design

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Measuring intrarater association between correlated ordinal ratings

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BIOMETRICAL JOURNAL 2020年第7期62卷 1687-1701页

作者： Nelson, Kerrie P. Zhou, Thomas J. Edwards, Don Boston Univ Dept Biostat 801 Massachusetts Ave Boston MA 02118 USA Univ South Carolina Dept Stat Columbia SC 29208 USA

Variability between raters' ordinal scores is commonly observed in imaging tests, leading to uncertainty in the diagnostic process. In breast cancer screening, a radiologist visually interprets mammograms and MRIs, while skin diseases, Alzheimer's disease, and psychiatric conditions are graded based on clinical judgment. Consequently, studies are often conducted in clinical settings to investigate whether a new training tool can improve the interpretive performance of raters. In such studies, a large group of experts each classify a set of patients' test results on two separate occasions, before and after some form of training with the goal of assessing the impact of training on experts' paired ratings. However, due to the correlated nature of the ordinal ratings, few statistical approaches are available to measure association between raters' paired scores. Existing measures are restricted to assessing association at just one time point for a single screening test. We propose here a novel paired kappa to provide a summary measure of association between many raters' paired ordinal assessments of patients' test results before versus after rater training. Intrarater association also provides valuable insight into the consistency of ratings when raters view a patient's test results on two occasions with no intervention undertaken between viewings. In contrast to existing correlated measures, the proposed kappa is a measure that provides an overall evaluation of the association among multiple raters' scores from two time points and is robust to the underlying disease prevalence. We implement our proposed approach in two recent breast-imaging studies and conduct extensive simulation studies to evaluate properties and performance of our summary measure of association.

关键词： association generalized linear mixed model model-based kappa ordinal classifications screening test

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Impact of lockdown and change in mobility patterns on road fatalities during COVID-19 pandemic

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TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH 2021年第5-6期13卷 447-460页

作者： Gupta, Monik Pawar, Nishant Mukund Velaga, Nagendra R. Indian Inst Technol Civil Engn Dept Transportat Syst Engn Mumbai Maharashtra India

The present study examines the impact of lockdown measures taken to control the spread of COVID-19 on road fatalities. The study is based on the data collected over the duration of six months for fifteen countries based on severity of COVID-19. Lockdown stringency and six daily mobility measures were selected to study the change in road mobility patterns due to lockdown measures. The fifteen countries were categorized into two clusters (C1 and C2) and generalized linear mixed model was considered for analysis. The model results revealed that stringent lockdown measures and high residential mobility reduced Crash Fatality Ratio (CFR) by 0.36% and 1.84%, respectively in cluster C2. Further, in cluster C1, travel restrictions on workplace and recreational activities decreased CFR up to 0.31% and 0.26%, respectively. Thus, overall model results specified that stringent law enforcement and appropriate compliance from the road users can effectively lessen traffic-related fatalities in unprecedented times.

关键词： COVID-19 changed mobility road fatality k-means clustering silhouette value generalized linear mixed model

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Large scale faecal (spraint) counts indicate the population status of endangered Eurasian otters (Lutra lutra)

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ECOLOGICAL INDICATORS 2020年 109卷 105844-000页

作者： Hong, Sungwon Di Febbraro, Mirko Loy, Anna Cowan, Phil Joo, Gea-Jae Pusan Natl Univ Dept Biol Sci Busan 46241 South Korea Univ Molise Dept Biosci & Terr I-86090 Contrada Fonte Lappone Pesche Italy Manaaki Whenua Landcare Res Lincoln 7640 New Zealand

The validity of using spraint (otter faeces) density for population monitoring has been debated for more than 30 years. In this study, we investigated endangered Eurasian otter (Lutra lutra) spraint occurrence and densities at large scales (over 23,800 km(2), a quarter of South Korea) over three years (2014-2016). To clarify the spatial heterogeneity of spraint density and count distributions, we applied the global Morans' I test and hot spot analysis. We also constructed models with 30 environmental factors (six landscape, eight anthropogenic, 13 aquatic health indices, one prey abundance, and two meteorological factors) using generalized linear mixed models with repeated measurements. Our geographical analysis showed regional clusters of otters extending over distances of more than 80 km. The most parsimonious model, a zero-inflated negative binomial model, indicated that our otter spraint counts were significantly positively related to the benthic macro-invertebrate index and precipitation and negatively related to proportion of home range covered by water. In addition, this model showed that absence probabilities of otter spraint were significantly positively related to human populations and negatively related to the number of fish species and altitude. The best explanatory model suggests that our count data was highly related to otter population status, and also affected by anthropogenic disturbance.

关键词： Non-invasive monitoring Endangered species Conservation generalized linear mixed model Hot spot analysis Lutra lutra

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Hierarchical Bayesian modeling of heterogeneous cluster- and subject-level associations between continuous and binary outcomes in dairy production

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BIOMETRICAL JOURNAL 2012年第2期54卷 230-248页

作者： Bello, Nora M. Steibel, Juan P. Tempelman, Robert J. Michigan State Univ Dept Anim Sci E Lansing MI 48824 USA Kansas State Univ Dept Stat Manhattan KS 66506 USA Michigan State Univ Dept Fisheries & Wildlife E Lansing MI 48824 USA

The augmentation of categorical outcomes with underlying Gaussian variables in bivariate generalized mixed effects models has facilitated the joint modeling of continuous and binary response variables. These models typically assume that random effects and residual effects (co)variances are homogeneous across all clusters and subjects, respectively. Motivated by conflicting evidence about the association between performance outcomes in dairy production systems, we consider the situation where these (co)variance parameters may themselves be functions of systematic and/or random effects. We present a hierarchical Bayesian extension of bivariate generalized linear models whereby functions of the (co)variance matrices are specified as linear combinations of fixed and random effects following a square-root-free Cholesky reparameterization that ensures necessary positive semidefinite constraints. We test the proposed model by simulation and apply it to the analysis of a dairy cattle data set in which the random herd-level and residual cow-level effects (co)variances between a continuous production trait and binary reproduction trait are modeled as functions of fixed management effects and random cluster effects.

关键词： Bivariate Bayesian model Dairy production systems generalized linear mixed model Heterogeneous covariance mixed outcomes

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Composite Likelihood Estimation for Multivariate Probit Latent Traits models

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2013年第14期42卷 2551-2566页

作者： Feddag, M. -L. Univ Southampton Stat Sci Res Inst Southampton SO17 1BJ Hants England

Inference in generalized linear mixed models with multivariate random effects is often made cumbersome by the high-dimensional intractable integrals involved in the marginal likelihood. This article presents an inferential methodology based on the marginal composite likelihood approach for the probit latent traits models. This method belonging to the broad class of pseudo-likelihood involves marginal pairs probabilities of the responses which has analytical expression. The different results are illustrated with a simulation study and with an analysis of real data from health related quality of life.

关键词： Composite likelihood Fixed effects Gauss Hermite quadrature generalized linear mixed model Health related quality of life Longitudinal data Maximum marginal likelihood Pairwise likelihood Probit link Random effects Rasch model

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A mixed effects model for overdispersed count data in animal breeding

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BIOMETRICS 1996年第1期52卷 265-279页

作者： Tempelman, RJ Gianola, D LOUISIANA STATE UNIV DEPT EXPTL STAT LOUISIANA AGR EXPT STN BATON ROUGE LA 70802 USA UNIV WISCONSIN DEPT DAIRY SCI MADISON WI 53706 USA

Count data models are developed for animal breeding applications to account for more variability than in a Poisson mixed effects model. A gamma distribution is assigned to Poisson parameters, thereby leading to a negative binomial model. The natural log of the expected value of the Poisson parameter is expressed as a linear function of fixed and random polygenic effects. The negative binomial and Poisson mixed models were compared in two simulations. In the first, marginal maximum likelihood (MML) estimates of genetic variances obtained under a Poisson ''sire'' model (PSM) and under a Poisson ''animal'' model (PAM), accounting for half-sib relationships, were different, contrary to what occurs in a Gaussian mixed linear model. MML estimates of genetic variance under a negative binomial ''sire'' model were less biased than estimates under a PSM, and had a slightly smaller mean squared error (MSE). The second simulation compared ''animal'' models in which the variance of the residuals was larger than the genetic variance. Empirical relative bias and MSE of MML estimates of genetic variance were larger under a PAM that ignored the residuals than under a negative binomial animal model. Differences in performance widened as genetic variance increased. An application to the analysis of number of artificial inseminations until conception in dairy heifers is presented to illustrate potential differences in genetic variance estimates under the two animal models.

关键词： animal breeding counts generalized linear mixed model negative binomial variance components

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Bayesian modeling of retrospective time-to-pregnancy data with digit preference bias

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MATHEMATICAL AND COMPUTER modelLING 2006年第11-12期43卷 1424-1433页

作者： Price, Karen L. Seaman, John W. Baylor Univ Inst Stat Dept Stat Sci Waco TX 76798 USA Eli Lilly & Co Indianapolis IN 46285 USA

The study of factors affecting human fertility is an important problem affording interesting statistical and computational challenges. Analyses of human fertility rates must cope with extra variability in fecundability parameters as well as a host of covariates ranging from the obvious, such as coital frequency, to the subtle, like the smoking habits of the female's mother. In retrospective human fecundity studies, researchers ask couples the time required to conceive. This time-to-pregnancy data often exhibits digit preference bias, among other problems. We introduce computationally intensive models with sufficient flexibility to represent such bias and other causes yielding a similar lack of monotonicity in conception probabilities. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： iterative simulation methods Markov chain Monte Carlo slice sampling generalized linear mixed model hierarchical centering

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