Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/81893
Authors: 
Villani, Mattias
Kohn, Robert
Giordani, Paolo
Year of Publication: 
2007
Series/Report no.: 
Sveriges Riksbank Working Paper Series 211
Abstract: 
We model a regression density nonparametrically so that at each value of the covariates the density is a mixture of normals with the means, variances and mixture probabilities of the components changing smoothly as a function of the covariates. The model extends existing models in two important ways. First, the components are allowed to be heteroscedastic regressions as the standard model with homoscedastic regressions can give a poor fit to heteroscedastic data, especially when the number of covariates is large. Furthermore, we typically need a lot fewer heteroscedastic components, which makes it easier to interpret the model and speeds up the computation. The second main extension is to introduce a novel variable selection prior into all the components of the model. The variable selection prior acts as a self adjusting mechanism that prevents overfitting and makes it feasible to fit high dimensional nonparametric surfaces. We use Bayesian inference and Markov Chain Monte Carlo methods to estimate the model. Simulated and real examples are used to show that the full generality of our model is required to fit a large class of densities.
Subjects: 
Bayesian inference
Markov Chain Monte Carlo
Mixture of Experts
Predictive inference
Splines
Value-at-Risk
Variable selection
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.