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Nonparametric Bayes Modeling of Multivariate Categorical Data

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  • Dunson, David B.
  • Xing, Chuanhua

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  • Dunson, David B. & Xing, Chuanhua, 2009. "Nonparametric Bayes Modeling of Multivariate Categorical Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1042-1051.
    • Handle: RePEc:bes:jnlasa:v:104:i:487:y:2009:p:1042-1051
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    Cited by:

    1. Mengbing Li & Daniel E. Park & Maliha Aziz & Cindy M. Liu & Lance B. Price & Zhenke Wu, 2023. "Integrating sample similarities into latent class analysis: a tree‐structured shrinkage approach," Biometrics, The International Biometric Society, vol. 79(1), pages 264-279, March.
    2. Hang J. Kim & Jörg Drechsler & Katherine J. Thompson, 2021. "Synthetic microdata for establishment surveys under informative sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 255-281, January.
    3. Jing Zhou & Anirban Bhattacharya & Amy H. Herring & David B. Dunson, 2015. "Bayesian Factorizations of Big Sparse Tensors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1562-1576, December.
    4. Richardson, Robert & Hartman, Brian, 2018. "Bayesian nonparametric regression models for modeling and predicting healthcare claims," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 1-8.
    5. Durante, Daniele, 2017. "A note on the multiplicative gamma process," Statistics & Probability Letters, Elsevier, vol. 122(C), pages 198-204.
    6. Humera Razzak & Christian Heumann, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    7. Florian Huber & Gary Koop, 2023. "Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks," Working Papers 2309, University of Strathclyde Business School, Department of Economics.
    8. Hiroyuki Kasahara & Katsumi Shimotsu, 2014. "Non-parametric identification and estimation of the number of components in multivariate mixtures," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 97-111, January.
    9. Jared S. Murray & Jerome P. Reiter, 2016. "Multiple Imputation of Missing Categorical and Continuous Values via Bayesian Mixture Models With Local Dependence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1466-1479, October.
    10. Tsuyoshi Kunihama & David B. Dunson, 2013. "Bayesian Modeling of Temporal Dependence in Large Sparse Contingency Tables," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1324-1338, December.
    11. Alfò, Marco & Rocchetti, Irene, 2013. "A flexible approach to finite mixture regression models for multivariate mixed responses," Statistics & Probability Letters, Elsevier, vol. 83(7), pages 1754-1758.
    12. Daniele Durante & David B. Dunson & Joshua T. Vogelstein, 2017. "Nonparametric Bayes Modeling of Populations of Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1516-1530, October.
    13. Razzak Humera & Heumann Christian, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    14. Russo, Massimiliano & Durante, Daniele & Scarpa, Bruno, 2018. "Bayesian inference on group differences in multivariate categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 136-149.
    15. Kunihama, T. & Herring, A.H. & Halpern, C.T. & Dunson, D.B., 2016. "Nonparametric Bayes modeling with sample survey weights," Statistics & Probability Letters, Elsevier, vol. 113(C), pages 41-48.
    16. James Jackson & Robin Mitra & Brian Francis & Iain Dove, 2022. "Using saturated count models for user‐friendly synthesis of large confidential administrative databases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1613-1643, October.
    17. Zhenke Wu & Livia Casciola‐Rosen & Antony Rosen & Scott L. Zeger, 2021. "A Bayesian approach to restricted latent class models for scientifically structured clustering of multivariate binary outcomes," Biometrics, The International Biometric Society, vol. 77(4), pages 1431-1444, December.
    18. Mahsa Samsami & Ralf Wagner, 2021. "Investment Decisions with Endogeneity: A Dirichlet Tree Analysis," JRFM, MDPI, vol. 14(7), pages 1-19, July.
    19. Daniel Manrique‐Vallier, 2016. "Bayesian population size estimation using Dirichlet process mixtures," Biometrics, The International Biometric Society, vol. 72(4), pages 1246-1254, December.
    20. Daniel Manrique‐Vallier & Jingchen Hu, 2018. "Bayesian non‐parametric generation of fully synthetic multivariate categorical data in the presence of structural zeros," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 635-647, June.
    21. Paiva Thais & Reiter Jerome P., 2017. "Stop or Continue Data Collection: A Nonignorable Missing Data Approach for Continuous Variables," Journal of Official Statistics, Sciendo, vol. 33(3), pages 579-599, September.
    22. Guanhua Fang & Zhiliang Ying, 2020. "Latent Theme Dictionary Model for Finding Co-occurrent Patterns in Process Data," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 775-811, September.
    23. Yajuan Si & Jerome P. Reiter, 2013. "Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys," Journal of Educational and Behavioral Statistics, , vol. 38(5), pages 499-521, October.
    24. Guanhua Fang & Jingchen Liu & Zhiliang Ying, 2019. "On the Identifiability of Diagnostic Classification Models," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 19-40, March.

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