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Partially Collapsed Gibbs Samplers: Theory and Methods

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  • van Dyk, David A.
  • Park, Taeyoung

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  • van Dyk, David A. & Park, Taeyoung, 2008. "Partially Collapsed Gibbs Samplers: Theory and Methods," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 790-796, June.
    • Handle: RePEc:bes:jnlasa:v:103:y:2008:m:june:p:790-796
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    Cited by:

    1. Wai-Yin Poon & Hai-Bin Wang, 2014. "Multivariate partially linear single-index models: Bayesian analysis," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(4), pages 755-768, December.
    2. Peter Knaus & Sylvia Fruhwirth-Schnatter, 2023. "The Dynamic Triple Gamma Prior as a Shrinkage Process Prior for Time-Varying Parameter Models," Papers 2312.10487, arXiv.org.
    3. Chan, Joshua C.C. & Eisenstat, Eric & Koop, Gary, 2016. "Large Bayesian VARMAs," Journal of Econometrics, Elsevier, vol. 192(2), pages 374-390.
    4. Conti, Gabriella & Frühwirth-Schnatter, Sylvia & Heckman, James J. & Piatek, Rémi, 2014. "Bayesian exploratory factor analysis," Journal of Econometrics, Elsevier, vol. 183(1), pages 31-57.
    5. Lian, Heng, 2010. "Sparse Bayesian hierarchical modeling of high-dimensional clustering problems," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1728-1737, August.
    6. Monica Billio & Roberto Casarin & Matteo Iacopini, 2024. "Bayesian Markov-Switching Tensor Regression for Time-Varying Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(545), pages 109-121, January.
    7. Zhang, Yifan & Fong, Duncan K.H. & DeSarbo, Wayne S., 2021. "A generalized ordinal finite mixture regression model for market segmentation," International Journal of Research in Marketing, Elsevier, vol. 38(4), pages 1055-1072.
    8. Kim, Jaeho, 2015. "Bayesian Inference in a Non-linear/Non-Gaussian Switching State Space Model: Regime-dependent Leverage Effect in the U.S. Stock Market," MPRA Paper 67153, University Library of Munich, Germany.
    9. Berrett, Candace & Calder, Catherine A., 2012. "Data augmentation strategies for the Bayesian spatial probit regression model," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 478-490.
    10. Kuo, Kun-Lin & Wang, Yuchung J., 2018. "Simulating conditionally specified models," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 171-180.
    11. Bresson Georges & Chaturvedi Anoop & Rahman Mohammad Arshad & Shalabh, 2021. "Seemingly unrelated regression with measurement error: estimation via Markov Chain Monte Carlo and mean field variational Bayes approximation," The International Journal of Biostatistics, De Gruyter, vol. 17(1), pages 75-97, May.
    12. Mauro Bernardi & Ghislaine Gayraud & Lea Petrella, 2013. "Bayesian inference for CoVaR," Papers 1306.2834, arXiv.org, revised Nov 2013.
    13. Axel Finke & Adam Johansen & Dario Spanò, 2014. "Static-parameter estimation in piecewise deterministic processes using particle Gibbs samplers," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 577-609, June.
    14. Yang Liu & Xiaojing Wang, 2020. "Bayesian Nonparametric Monotone Regression of Dynamic Latent Traits in Item Response Theory Models," Journal of Educational and Behavioral Statistics, , vol. 45(3), pages 274-296, June.
    15. Ricardo C. Pedroso & Rosangela H. Loschi & Fernando Andrés Quintana, 2023. "Multipartition model for multiple change point identification," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 759-783, June.
    16. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2017. "Cohort effects in mortality modelling: a Bayesian state-space approach," Papers 1703.08282, arXiv.org.
    17. Jeong, Seonghyun & Park, Minjae & Park, Taeyoung, 2017. "Analysis of binary longitudinal data with time-varying effects," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 145-153.
    18. repec:bfi:wpaper:2014-014 is not listed on IDEAS
    19. Yang Ni & Veerabhadran Baladandayuthapani & Marina Vannucci & Francesco C. Stingo, 2022. "Bayesian graphical models for modern biological applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 197-225, June.
    20. Waley W. J. Liang & Herbert K. H. Lee, 2019. "Bayesian nonstationary Gaussian process models via treed process convolutions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 797-818, September.
    21. Gianluca Mastrantonio & Giovanna Jona Lasinio & Alessio Pollice & Lorenzo Teodonio & Giulia Capotorti, 2022. "A Dirichlet process model for change‐point detection with multivariate bioclimatic data," Environmetrics, John Wiley & Sons, Ltd., vol. 33(1), February.
    22. Baragatti, M. & Pommeret, D., 2012. "A study of variable selection using g-prior distribution with ridge parameter," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1920-1934.
    23. Zhang, Tingting & Sun, Yinge & Li, Huazhang & Yan, Guofen & Tanabe, Seiji & Miao, Ruizhong & Wang, Yaotian & Caffo, Brian S. & Quigg, Mark S., 2020. "Bayesian inference of a directional brain network model for intracranial EEG data," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).

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