Bayesian composite quantile regression for the single-index model

被引：3

作者：

Yuan, Xiaohui ^{[1
]}

Xiang, Xuefei ^{[2
]}

Zhang, Xinran ^{[2
]}

机构：

[1] Jilin Univ, Sch Math, Changchun, Peoples R China

[2] Changchun Univ Technol, Sch Math & Stat, Changchun, Peoples R China

来源：

PLOS ONE | 2023年 / 18卷 / 05期

关键词：

VARIABLE SELECTION; SEMIPARAMETRIC ESTIMATION;

D O I：

10.1371/journal.pone.0285277

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

By using a Gaussian process prior and a location-scale mixture representation of the asymmetric Laplace distribution, we develop a Bayesian analysis for the composite quantile single-index regression model. The posterior distributions for the unknown parameters are derived, and the Markov chain Monte Carlo sampling algorithms are also given. The proposed method is illustrated by three simulation examples and a real dataset.

引用

页数：17

共 32 条

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Antoniadis A, 2004, STAT SINICA, V14, P1147

[2] Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in financial economics [J].