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Communications in Statistics - Simulation and Computation Volume 48, 2019 - Issue 3
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Original Articles

Bayesian LASSO-Regularized quantile regression for linear regression models with autoregressive errors

Yuzhu TianSchool of Mathematics and Statistics, Henan University of Science and Technology, LuoYang;School of Statistics and Mathematics, The Central University of Finance and Economics, BeiJingCorrespondence[email protected]
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,
Silian ShenSchool of Mathematics and Statistics, Henan University of Science and Technology, LuoYangView further author information
,
Ge LuSchool of Mathematics and Statistics, Henan University of Science and Technology, LuoYangView further author information
,
Manlai TangDepartment of Mathematics and Statistics, Hang Seng Management College, HongKongView further author information
&
Maozai TianSchool of Statistics, Renmin University of China, BeiJingView further author information
Pages 777-796 | Received 27 Sep 2017, Accepted 18 Oct 2017, Published online: 06 Dec 2017

Citations (4)

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Read on this site (2)

A. Aghamohammadi & M. Bahmani. (2022) Bayesian weighted composite quantile regression estimation for linear regression models with autoregressive errors. Communications in Statistics - Theory and Methods 0:0, pages 1-20.
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Yuzhu Tian, Manlai Tang, Liyong Wang & Maozai Tian. (2019) Bayesian bridge-randomized penalized quantile regression estimation for linear regression model with AP(q) perturbation. Journal of Statistical Computation and Simulation 89:15, pages 2951-2979.
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Articles from other publishers (2)

M. Giovanna Ranalli, Nicola Salvati, Lea Petrella & Francesco Pantalone. (2023) M‐quantile regression shrinkage and selection via the Lasso and Elastic Net to assess the effect of meteorology and traffic on air quality. Biometrical Journal 65:8.
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Yu-Zhu Tian, Man-Lai Tang, Wai-Sum Chan & Mao-Zai Tian. (2020) Bayesian bridge-randomized penalized quantile regression for ordinal longitudinal data, with application to firm’s bond ratings. Computational Statistics 36:2, pages 1289-1319.
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