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KOBAYASHI GENYA
Department Undergraduate School , School of Commerce Position Professor |
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| Language | English |
| Publication Date | 2011 |
| Type | Academic Journal |
| Peer Review | Peer reviewed |
| Title | Gibbs sampling methods for Bayesian quantile regression |
| Contribution Type | Co-authored (other than first author) |
| Journal | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION |
| Journal Type | Another Country |
| Publisher | TAYLOR & FRANCIS LTD |
| Volume, Issue, Page | 81(11),pp.1565-1578 |
| Author and coauthor | Hideo Kozumi, Genya Kobayashi |
| Details | This paper considers quantile regression models using an asymmetric Laplace distribution from a Bayesian point of view. We develop a simple and efficient Gibbs sampling algorithm for fitting the quantile regression model based on a location-scale mixture representation of the asymmetric Laplace distribution. It is shown that the resulting Gibbs sampler can be accomplished by sampling from either normal or generalized inverse Gaussian distribution. We also discuss some possible extensions of our approach, including the incorporation of a scale parameter, the use of double exponential prior, and a Bayesian analysis of Tobit quantile regression. The proposed methods are illustrated by both simulated and real data. |
| DOI | 10.1080/00949655.2010.496117 |
| ISSN | 0094-9655 |