文献详情
Bayesian LASSO-Regularized quantile regression for linear regression models with autoregressive errors
文献类型：期刊
通讯作者：Tian, YZ (reprint author), Henan Univ Sci & Technol, Sch Math & Stat, Luoyang, Peoples R China.; Tian, YZ (reprint author), Henan Univ Sci & Technol, Sch Math & Stat, Kaiyuan Rd, Luoyang City 471023, Henan, Peoples R China.
期刊名称：COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION影响因子和分区
年：2019
卷：48
期：3
页码：777-796
ISSN：0361-0918
关键词：Bayesian QR; LASSO-penalized priors; Gibbs sampler; Autoregressive error; Regression model
所属部门：统计学院
摘要：Quantile regression (QR) is a natural alternative for depicting the impact of covariates on the conditional distributions of a outcome variable instead of the mean. In this paper, we investigate Bayesian regularized QR for the linear models with autoregressive errors. LASSO-penalized type priors are forced on regression coefficients and autoregressive parameters of the model. Gibbs sampler algorithm is employed to draw the full posterior distributions of unknown parameters. Finally, the proposed ...More
Quantile regression (QR) is a natural alternative for depicting the impact of covariates on the conditional distributions of a outcome variable instead of the mean. In this paper, we investigate Bayesian regularized QR for the linear models with autoregressive errors. LASSO-penalized type priors are forced on regression coefficients and autoregressive parameters of the model. Gibbs sampler algorithm is employed to draw the full posterior distributions of unknown parameters. Finally, the proposed procedures are illustrated by some simulation studies and applied to a real data analysis of the electricity consumption. ...Hide

DOI：10.1080/03610918.2017.1397166
百度学术：Bayesian LASSO-Regularized quantile regression for linear regression models with autoregressive errors
语言：外文
被引频次：1
基金：National Natural Science Foundation of ChinaNational Natural Science Foundation of China [11501167, 11601126]; China Postdoctoral Science FoundationChina Postdoctoral Science Foundation [2017M610156]; Young academic leaders project of Henan University of Science and Technology [13490008]; Key Program of National Philosophy and Social Science Foundation [13AZD064]; Renmin University of China [15XNL008]
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Quantile Regression for Dynamic Panel Data Using Hausman-Taylor Instrumental Variables.Tao, Li, Zhang, Yuanjie, Tian, Maozai,.COMPUTATIONAL ECONOMICS. 2019, 53(3), 1033-1069.
Quantile Regression for Dynamic Panel Data Using Hausman-Taylor Instrumental Variables.Tao, Li, Zhang, Yuanjie, Tian, Maozai,.COMPUTATIONAL ECONOMICS. 2019, 53(3), 1033-1069.
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