Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/81914
Authors: 
Li, Feng
Villani, Mattias
Kohn, Robert
Year of Publication: 
2010
Series/Report no.: 
Sveriges Riksbank Working Paper Series 245
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.
Subjects: 
Bayesian inference
Markov chain Monte Carlo
Mixture of Experts
Variable selection
Document Type: 
Working Paper

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