Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/81914 
Erscheinungsjahr: 
2010
Schriftenreihe/Nr.: 
Sveriges Riksbank Working Paper Series No. 245
Verlag: 
Sveriges Riksbank, Stockholm
Zusammenfassung: 
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.
Schlagwörter: 
Bayesian inference
Markov chain Monte Carlo
Mixture of Experts
Variable selection
Dokumentart: 
Working Paper
Erscheint in der Sammlung:

Datei(en):
Datei
Größe
654.12 kB





Publikationen in EconStor sind urheberrechtlich geschützt.