Modeling Conditional Densities Using Finite Smooth Mixtures
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.
|Date of creation:||01 Aug 2010|
|Contact details of provider:|| Postal: Sveriges Riksbank, SE-103 37 Stockholm, Sweden|
Phone: 08 - 787 00 00
Fax: 08-21 05 31
Web page: http://www.riksbank.com/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:hhs:rbnkwp:0245. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Lena Löfgren)
If references are entirely missing, you can add them using this form.