Parallel Bayesian MCMC Imputation for Multiple Distributed Lag Models

[1]  Russell G. Almond,et al.  Modeling Diagnostic Assessments with Bayesian Networks , 2007 .

[2]  Brian W. Junker,et al.  Covariates of the Rating Process in Hierarchical Models for Multiple Ratings of Test Items , 2007 .

[3]  Nian-Sheng Tang,et al.  Bayesian Methods for Analyzing Structural Equation Models With Covariates, Interaction, and Quadratic Latent Variables , 2007 .

[4]  John R. Nesselroade,et al.  Bayesian analysis of longitudinal data using growth curve models , 2007 .

[5]  Lihua Yao,et al.  A Multidimensional Item Response Modeling Approach for Improving Subscale Proficiency Estimation and Classification , 2007 .

[6]  Kristopher J Preacher,et al.  Item factor analysis: current approaches and future directions. , 2007, Psychological methods.

[7]  Joseph L Schafer,et al.  Latent class logistic regression: application to marijuana use and attitudes among high school seniors , 2006 .

[8]  Sik-Yum Lee.  Bayesian Analysis of Nonlinear Structural Equation Models with Nonignorable Missing Data , 2006 .

[9]  Robert J. Mislevy,et al.  26 Bayesian Psychometric Modeling From An Evidence-Centered Design Perspective , 2006 .

[10]  Allan S. Cohen,et al.  A Mixture Model Analysis of Differential Item Functioning , 2005 .

[11]  Damian W. Betebenner,et al.  Hierarchical Logistic Regression in Course Placement , 2004 .

[12]  Herbert Hoijtink,et al.  Model based clustering of large data sets: Tracing the development of spelling ability , 2004 .

[13]  Jeffrey A Douglas,et al.  Higher-order latent trait models for cognitive diagnosis , 2004 .

[14]  Daniel M. Bolt,et al.  Estimation of Compensatory and Noncompensatory Multidimensional Item Response Models Using Markov Chain Monte Carlo , 2003 .

[15]  Xin-Yuan Song,et al.  Bayesian model selection for mixtures of structural equation models with an unknown number of components. , 2003, The British journal of mathematical and statistical psychology.

[16]  Xin-Yuan Song,et al.  Full Maximum Likelihood Estimation of Polychoric and Polyserial Correlations With Missing Data , 2003, Multivariate behavioral research.

[17]  M.J.H. van Onna,et al.  Bayesian estimation and model selection in ordered latent class models for polytomous items , 2002 .

[18]  James A. Wollack,et al.  Recovery of Item Parameters in the Nominal Response Model: A Comparison of Marginal Maximum Likelihood Estimation and Markov Chain Monte Carlo Estimation , 2002 .

[19]  Nelson Lim,et al.  Sensitivity Analysis for Hierarchical Models Employing t Level-1 Assumptions , 2002 .

[20]  Sik-Yum Lee,et al.  Analysis of structural equation model with ignorable missing continuous and polytomous data , 2002 .

[21]  Edward H. Ip,et al.  Empirical Bayes and Item-Clustering Effects in a Latent Variable Hierarchical Model , 2002 .

[22]  Kamel Jedidi,et al.  Heterogeneous factor analysis models: A bayesian approach , 2002 .

[23]  K. Bollen.  Latent variables in psychology and the social sciences. , 2002, Annual review of psychology.

[24]  S. Sahu.  Bayesian Estimation and Model Choice in Item Response Models , 2002 .

[25]  A. Béguin,et al.  MCMC estimation and some model-fit analysis of multidimensional IRT models , 2001 .

[26]  Allan S. Cohen,et al.  A Mixture Item Response Model for Multiple-Choice Data , 2001 .

[27]  S Y Lee,et al.  Bayesian estimation and test for factor analysis model with continuous and polytomous data in several populations. , 2001, The British journal of mathematical and statistical psychology.

[28]  J. Fox,et al.  Bayesian estimation of a multilevel IRT model using gibbs sampling , 2001 .

[29]  Sik-Yum Lee,et al.  A Bayesian analysis of finite mixtures in the LISREL model , 2001 .

[30]  K R Abrams,et al.  Bayesian methods in meta-analysis and evidence synthesis. , 2001, Statistical methods in medical research.

[31]  S Y Lee,et al.  Statistical analysis of nonlinear structural equation models with continuous and polytomous data. , 2000, The British journal of mathematical and statistical psychology.

[32]  Francis Tuerlinckx,et al.  A Hierarchical IRT Model for Criterion-Referenced Measurement , 2000 .

[33]  Brian W. Junker,et al.  Applications and Extensions of MCMC in IRT: Multiple Item Types, Missing Data, and Rated Responses , 1999 .

[34]  Stephen W. Raudenbush,et al.  Synthesizing Results from the Trial State Assessment , 1999 .

[35]  Richard J. Patz,et al.  A Straightforward Approach to Markov Chain Monte Carlo Methods for Item Response Models , 1999 .

[36]  R. Scheines,et al.  Bayesian estimation and testing of structural equation models , 1999 .

[37]  Jian Qing Shi,et al.  Bayesian sampling‐based approach for factor analysis models with continuous and polytomous data , 1998 .

[38]  B. Muthén,et al.  A Bayesian approach to nonlinear latent variable models using the Gibbs sampler and the metropolis-hastings algorithm , 1998 .

[39]  Rafa M. Kasim,et al.  Application of Gibbs Sampling to Nested Variance Components Models With Heterogeneous Within-Group Variance , 1998 .

[40]  Wing Hung Wong,et al.  Bayesian Analysis in Applications of Hierarchical Models: Issues and Methods , 1996 .

[41]  D J Spiegelhalter,et al.  Bayesian approaches to random-effects meta-analysis: a comparative study. , 1995, Statistics in medicine.

[42]  David Draper,et al.  Inference and Hierarchical Modeling in the Social Sciences , 1995 .

[43]  Jürgen Rost,et al.  Rasch Models in Latent Classes: An Integration of Two Approaches to Item Analysis , 1990 .

[44]  Jan de Leeuw,et al.  On the relationship between item response theory and factor analysis of discretized variables , 1987 .

[45]  J. Ware,et al.  Random-effects models for longitudinal data. , 1982, Biometrics.

[46]  M. R. Novick,et al.  The Role of Exchangeability in Inference , 1981 .

[47]  Melvin R. Novick,et al.  Display Book for the Computer-Assisted Data Analysis (CADA) Monitor (1980). , 1981 .

[48]  G. Glass.  Primary, Secondary, and Meta-Analysis of Research1 , 1976 .

[49]  Melvin R. Novick,et al.  Further Cross-Validation Analysis of the Bayesian m-Group Regression Method1 , 1974 .