Model selection in binary and tobit quantile regression using the Gibbs sampler

Publication year: 2011 Source: Computational Statistics & Data Analysis, Available online 14 October 2011 Yonggang Ji, Nan Lin, Baoxue Zhang A stochastic search variable selection approach is proposed for Bayesian model selection in binary and tobit quantile regression. A simple and efficient Gibbs sampling algorithm was developed for posterior inference using a location-scale mixture representation of the asymmetric Laplace distribution.

Publication year: 2011 Source: Computational Statistics & Data Analysis, Available online 14 October 2011 Yonggang Ji, Nan Lin, Baoxue Zhang A stochastic search variable selection approach is proposed for Bayesian model selection in binary and tobit quantile regression. A simple and efficient Gibbs sampling algorithm was developed for posterior inference using a location-scale mixture representation of the asymmetric Laplace distribution.

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Model selection in binary and tobit quantile regression using the Gibbs sampler

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