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|Title:||Bayesian quantile regression for single-index models||Authors:||Hu, Yuao
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
|DOI:||10.1007/s11222-012-9321-0||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SPMS Journal Articles|
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