Bayesian analysis in single-index quantile regression with missing observation

Chang-Sheng Liu and Han-Ying Liang

Communications in Statistics - Theory and Methods, 2023, vol. 52, issue 20, 7223-7251

Abstract: Based on Bayesian method, we investigate single-index quantile regression with missing observation. In particular, using spline approximation for the link function, we construct quasi-posterior distribution of the index vector based on asymmetric Laplace likelihood with missing observation, and establish asymptotically normality of the posterior estimator of the index parameters. At the same time, we use a hierarchical model based on spike and slab Gaussian priors to do variable selection and study consistency of the variable selection. Finite sample performance of the proposed methods is analyzed via simulation and real data too.

Date: 2023
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DOI: 10.1080/03610926.2022.2042027

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