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A flexible approach to finite mixture regression models for multivariate mixed responses

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  • Alfò, Marco
  • Rocchetti, Irene

Abstract

We describe regression models for multivariate mixed responses, where association between outcomes is modeled through discrete, outcome-specific, latent effects, accounting for heterogeneity and dependence. We relax the standard unidimensionality hypothesis, and adopt a multidimensional latent class approach, with possibly different numbers of locations in each margin, and a full association structure.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:stapro:v:83:y:2013:i:7:p:1754-1758
    DOI: 10.1016/j.spl.2013.04.004
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    References listed on IDEAS

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    1. Marco Alfò & Giovanni Trovato, 2004. "Semiparametric mixture models for multivariate count data, with application," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 426-454, December.
    2. Murat K. Munkin & Pravin K. Trivedi, 1999. "Simulated maximum likelihood estimation of multivariate mixed-Poisson regression models, with application," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 29-48.
    3. 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.
    4. Murray Aitkin, 1999. "A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models," Biometrics, The International Biometric Society, vol. 55(1), pages 117-128, March.
    5. Rabe-Hesketh, Sophia & Skrondal, Anders & Pickles, Andrew, 2005. "Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects," Journal of Econometrics, Elsevier, vol. 128(2), pages 301-323, October.
    6. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    7. Chib, Siddhartha & Winkelmann, Rainer, 2001. "Markov Chain Monte Carlo Analysis of Correlated Count Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 428-435, October.
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