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Communications in Statistics - Theory and Methods Volume 45, 2016 - Issue 12
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Original Articles

LAD-Lasso variable selection for doubly censored median regression models

Xiuqing ZhouSchool of Mathematical Sciences and Institute of Finance and Statistics, Nanjing Normal University, Nanjing, P. R. ChinaCorrespondence[email protected]
&
Guoxiang LiuSchool of Mathematical Sciences and Institute of Finance and Statistics, Nanjing Normal University, Nanjing, P. R. China
Pages 3658-3667 | Received 06 Nov 2013, Accepted 11 Mar 2014, Published online: 04 May 2016

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Read on this site (3)

Tate Jacobson & Hui Zou. (2024) High-Dimensional Censored Regression via the Penalized Tobit Likelihood. Journal of Business & Economic Statistics 42:1, pages 286-297.
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Xin Zeng, Yuanyuan Ju & Liucang Wu. (2023) Variable selection in finite mixture of median regression models using skew-normal distribution. Statistical Theory and Related Fields 7:1, pages 30-48.
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Yu Zhang, Jiangli Wang & Weiping Zhang. Variable selection and subgroup analysis for high-dimensional censored data. Statistical Theory and Related Fields 0:0, pages 1-21.
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Articles from other publishers (3)

Kostas Florios, Alexandros Louka & Yannis Bilias. (2024) LASSO can fix the statistical properties of the CLAD estimator under heavy censoring. SSRN Electronic Journal.
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Hongyu Li, Xieting Xu, Yajun Lu, Xi Yu, Tong Zhao & Rufei Zhang. (2022) Robust Variable Selection Based on Relaxed Lad Lasso. Symmetry 14:10, pages 2161.
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Perrine Soret, Marta Avalos, Linda Wittkop, Daniel Commenges & Rodolphe Thiébaut. (2018) Lasso regularization for left-censored Gaussian outcome and high-dimensional predictors. BMC Medical Research Methodology 18:1.
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