Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models
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Xiao Zhang | W. John Boscardin | Thomas R. Belin | T. Belin | W. Boscardin | Xiao Zhang | W. J. Boscardin
[1] C. Bhat,et al. Simulation Estimation of Mixed Discrete Choice Models with the Use of Randomized Quasi–Monte Carlo Sequences , 2005 .
[2] Peter E. Rossi,et al. Bayesian Statistics and Marketing , 2005 .
[3] Christian P. Robert,et al. Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.
[4] Andrew Gelman,et al. Applied Bayesian Modeling And Causal Inference From Incomplete-Data Perspectives , 2005 .
[5] J. Geweke,et al. Alternative computational approaches to inference in the multinomial probit model , 1994 .
[6] W. Wong,et al. The calculation of posterior distributions by data augmentation , 1987 .
[7] D. V. Dyk,et al. A Bayesian analysis of the multinomial probit model using marginal data augmentation , 2005 .
[8] D. Rubin,et al. Inference from Iterative Simulation Using Multiple Sequences , 1992 .
[9] Roshan Bastani,et al. Low-income women with breast abnormalities: system predictors of timely diagnostic resolution. , 2007, Cancer control : journal of the Moffitt Cancer Center.
[10] Joseph G. Ibrahim,et al. Monte Carlo Methods in Bayesian Computation , 2000 .
[11] D. McFadden,et al. ESTIMATION BY SIMULATION , 1994 .
[12] Lynn Kuo,et al. DISCRETE CHOICE MODELS BASED ON THE SCALE MIXTURE OF MULTIVARIATE NORMAL DISTRIBUTIONS , 2002 .
[13] John Geweke,et al. Statistical inference in the multinomial multiperiod probit model , 1997 .
[14] Peter E. Rossi,et al. A Bayesian analysis of the multinomial probit model with fully identified parameters , 2000 .
[15] Roshan Bastani,et al. Latinas with abnormal breast findings: patient predictors of timely diagnostic resolution. , 2007, Journal of women's health.
[16] Peter E. Rossi,et al. Modeling Consumer Demand for Variety , 2002 .
[17] S. Zeger,et al. Multivariate Regression Analyses for Categorical Data , 1992 .
[18] P. Holland,et al. Discrete Multivariate Analysis. , 1976 .
[19] T. Belin,et al. Sampling Correlation Matrices in Bayesian Models With Correlated Latent Variables , 2006 .
[20] D. McFadden. A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration , 1989 .
[21] W. Gilks. Markov Chain Monte Carlo , 2005 .
[22] Michel Bierlaire,et al. DISCRETE CHOICE MODELS , 1998 .
[23] S. Chib,et al. Bayesian analysis of binary and polychotomous response data , 1993 .
[24] Sylvia Richardson,et al. Markov Chain Monte Carlo in Practice , 1997 .
[25] T. Louis,et al. Bayes and Empirical Bayes Methods for Data Analysis. , 1997 .
[26] Adrian F. M. Smith,et al. Sampling-Based Approaches to Calculating Marginal Densities , 1990 .
[27] Thomas F. Golob,et al. Trucking Industry Adoption of Information Technology: A Structural Multivariate Discrete Choice Model , 2002 .
[28] D. McFadden,et al. MIXED MNL MODELS FOR DISCRETE RESPONSE , 2000 .
[29] Agostino Nobile,et al. A hybrid Markov chain for the Bayesian analysis of the multinomial probit model , 1998, Stat. Comput..
[30] Siddhartha Chib,et al. MCMC Methods for Fitting and Comparing Multinomial Response Models , 1998 .
[31] Bradley P. Carlin,et al. Markov Chain Monte Carlo conver-gence diagnostics: a comparative review , 1996 .
[32] W. Boscardin,et al. Modeling the Covariance and Correlation Matrix of Repeated Measures , 2005 .
[33] C. Bhat. The multiple discrete-continuous extreme value (MDCEV) model : Role of utility function parameters, identification considerations, and model extensions , 2008 .
[34] V. Hajivassiliou,et al. Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models , 1993 .
[35] Peter Congdon. Bayesian statistical modelling , 2002 .
[36] B. Muthén. Latent variable structural equation modeling with categorical data , 1983 .
[37] John Geweke,et al. Recursively Simulating Multinomial Multiperiod Probit Probabilities , 1994 .
[38] K Y Liang,et al. Longitudinal data analysis for discrete and continuous outcomes. , 1986, Biometrics.
[39] David B. Dunson,et al. Bayesian Data Analysis , 2010 .
[40] Kenneth Train,et al. Mixed Logit with Bounded Distributions of Correlated Partworths , 2005 .
[41] S. Zeger,et al. Longitudinal data analysis using generalized linear models , 1986 .
[42] D. Wise,et al. A CONDITIONAL PROBIT MODEL FOR QUALITATIVE CHOICE: DISCRETE DECISIONS RECOGNIZING INTERDEPENDENCE AND HETEROGENEOUS PREFERENCES' , 1978 .
[43] Jun S. Liu,et al. Monte Carlo strategies in scientific computing , 2001 .
[44] Andreas Ziegler,et al. Simulated Classical Tests in the Multiperiod Multinomial Probit Model , 2002 .
[45] Kosuke Imai,et al. MNP: R Package for Fitting the Multinomial Probit Model , 2005 .
[46] David A. Hensher,et al. The Mixed Logit Model: the State of Practice and Warnings for the Unwary , 2001 .
[47] Amelia C. Regan,et al. Trucking industry adoption of information technology: a multivariate discrete choice model , 2002 .
[48] Agostino Nobile,et al. Comment: Bayesian multinomial probit models with a normalization constraint , 2000 .
[49] Bradley P. Carlin,et al. BAYES AND EMPIRICAL BAYES METHODS FOR DATA ANALYSIS , 1996, Stat. Comput..
[50] Chandra R. Bhat,et al. A multiple discrete–continuous extreme value model: formulation and application to discretionary time-use decisions , 2005 .
[51] Peter E. Rossi,et al. An exact likelihood analysis of the multinomial probit model , 1994 .