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Modeling Conditional Densities Using Finite Smooth Mixtures

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Author Info

  • Li, Feng

    ()
    (Department of Statistics)

  • Villani, Mattias

    ()
    (Research Department, Central Bank of Sweden)

  • Kohn, Robert

    ()
    (The University of New South Wales)

Abstract

Smooth mixtures, i.e. mixture models with covariate-dependent mixing weights, are very useful flexible models for conditional densities. Previous work shows that using too simple mixture components for modeling heteroscedastic and/or heavy tailed data can give a poor fit, even with a large number of components. This paper explores how well a smooth mixture of symmetric components can capture skewed data. Simulations and applications on real data show that including covariate-dependent skewness in the components can lead to substantially improved performance on skewed data, often using a much smaller number of components. Furthermore, variable selection is effective in removing unnecessary covariates in the skewness, which means that there is little loss in allowing for skewness in the components when the data are actually symmetric. We also introduce smooth mixtures of gamma and log-normal components to model positively-valued response variables.

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Bibliographic Info

Paper provided by Sveriges Riksbank (Central Bank of Sweden) in its series Working Paper Series with number 245.

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Length: 26 pages
Date of creation: 01 Aug 2010
Date of revision:
Handle: RePEc:hhs:rbnkwp:0245

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Related research

Keywords: Bayesian inference; Markov chain Monte Carlo; Mixture of Experts; Variable selection;

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