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Generalized smooth finite mixtures

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  • Villani, Mattias
  • Kohn, Robert
  • Nott, David J.

Abstract

We propose a general class of models and a unified Bayesian inference methodology for flexibly estimating the density of a response variable conditional on a possibly high-dimensional set of covariates. Our model is a finite mixture of component models with covariate-dependent mixing weights. The component densities can belong to any parametric family, with each model parameter being a deterministic function of covariates through a link function. Our MCMC methodology allows for Bayesian variable selection among the covariates in the mixture components and in the mixing weights. The model’s parameterization and variable selection prior are chosen to prevent overfitting. We use simulated and real data sets to illustrate the methodology.

Suggested Citation

  • Villani, Mattias & Kohn, Robert & Nott, David J., 2012. "Generalized smooth finite mixtures," Journal of Econometrics, Elsevier, vol. 171(2), pages 121-133.
    • Handle: RePEc:eee:econom:v:171:y:2012:i:2:p:121-133
    DOI: 10.1016/j.jeconom.2012.06.012
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    References listed on IDEAS

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    Cited by:

    1. Drobetz, Wolfgang & Merikas, Andreas & Merika, Anna & Tsionas, Mike G., 2014. "Corporate social responsibility disclosure: The case of international shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 18-44.
    2. Cozzini, Alberto & Jasra, Ajay & Montana, Giovanni & Persing, Adam, 2014. "A Bayesian mixture of lasso regressions with t-errors," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 84-97.
    3. Feng Li & Mattias Villani, 2013. "Efficient Bayesian Multivariate Surface Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 706-723, December.
    4. Quiroz, Matias & Villani, Mattias, 2013. "Dynamic mixture-of-experts models for longitudinal and discrete-time survival data," Working Paper Series 268, Sveriges Riksbank (Central Bank of Sweden).

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