Bayesian modeling of joint and conditional distributions
| Autor: | Justinas Pelenis, Andriy Norets |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Economics and Econometrics
Chain rule (probability) Applied Mathematics Cluster-weighted modeling Conditional probability distribution Statistics::Computation symbols.namesake Joint probability distribution Statistics symbols Applied mathematics Marginal distribution Bayesian linear regression Conditional variance Mathematics Gibbs sampling |
| Zdroj: | Journal of Econometrics. 168:332-346 |
| ISSN: | 0304-4076 |
| DOI: | 10.1016/j.jeconom.2012.02.001 |
| Popis: | In this paper, we study a Bayesian approach to exible modeling of conditional distributions. The approach uses a exible model for the joint distribution of the dependent and independent variables and then extracts the conditional distributions of interest from the estimated joint distribution. We use a nite mixture of multivariate normals (FMMN) to estimate the joint distribution. The conditional distributions can then be assessed analytically or through simulations. The discrete variables are handled through the use of latent variables. The estimation procedure employs an MCMC algorithm. We provide a characterization of the Kullback{Leibler closure of FMMN and show that the joint and conditional predictive densities implied by FMMN model are consistent estimators for a large class of data generating processes with continuous and discrete observables. The method can be used as a robust regression model with discrete and continuous dependent and independent variables and as a Bayesian alternative to semi- and non-parametric models such as quantile and kernel regression. In experiments, the method compares favorably with classical nonparametric and alternative Bayesian methods. |
| Databáze: | OpenAIRE |
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