Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99409
Title: Bayesian quantile regression for single-index models
Authors: Hu, Yuao
Lian, Heng
Gramacy, Robert B.
Issue Date: 2012
Source: Hu, Y., Gramacy, R. B., & Lian, H. (2013). Bayesian quantile regression for single-index models. Statistics and Computing, 23(4), 437-454.
Series/Report no.: Statistics and computing
Abstract: Using an asymmetric Laplace distribution, which provides a mechanism for Bayesian inference of quantile regression models, we develop a fully Bayesian approach to fitting single-index models in conditional quantile regression. In this work, we use a Gaussian process prior for the unknown nonparametric link function and a Laplace distribution on the index vector, with the latter motivated by the recent popularity of the Bayesian lasso idea. We design a Markov chain Monte Carlo algorithm for posterior inference. Careful consideration of the singularity of the kernel matrix, and tractability of some of the full conditional distributions leads to a partially collapsed approach where the nonparametric link function is integrated out in some of the sampling steps. Our simulations demonstrate the superior performance of the Bayesian method versus the frequentist approach. The method is further illustrated by an application to the hurricane data.
URI: https://hdl.handle.net/10356/99409
http://hdl.handle.net/10220/17383
DOI: http://dx.doi.org/10.1007/s11222-012-9321-0
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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