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Bayesian estimation of random effects models for multivariate responses of mixed data

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  • Wagner, Helga
  • Tüchler, Regina

Abstract

A random effects model is presented to estimate multivariate data of mixed data types. Such data typically appear in studies where different response variables are measured repeatedly for one subject. It is possible to relate normal, binary, multinomial and count data by our joint model. Further flexibility with respect to model specification is obtained by including modern variable selection techniques. Auxiliary mixture sampling leads to a Gibbs sampling type scheme which is easy to implement since no additional tuning is needed. The method is illustrated by transaction data of a costumer cohort acquired by an apparel retailer.

Suggested Citation

  • Wagner, Helga & Tüchler, Regina, 2010. "Bayesian estimation of random effects models for multivariate responses of mixed data," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1206-1218, May.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:5:p:1206-1218
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    References listed on IDEAS

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    1. Smith M. & Kohn R., 2002. "Parsimonious Covariance Matrix Estimation for Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1141-1153, December.
    2. Fruhwirth-Schnatter, Sylvia & Fruhwirth, Rudolf, 2007. "Auxiliary mixture sampling with applications to logistic models," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3509-3528, April.
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    Cited by:

    1. Aßmann, Christian & Boysen-Hogrefe, Jens, 2011. "A Bayesian approach to model-based clustering for binary panel probit models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 261-279, January.
    2. Ling Zhou & Huazhen Lin & Xinyuan Song & Yi Li, 2014. "Selection of Latent Variables for Multiple Mixed-outcome Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 1064-1082, December.
    3. Wagner, Helga & Duller, Christine, 2012. "Bayesian model selection for logistic regression models with random intercept," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1256-1274.

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