Variable selection and coefficient estimation via composite quantile regression with randomly censored data

Composite quantile regression with randomly censored data is studied. Moreover, adaptive LASSO methods for composite quantile regression with randomly censored data are proposed. The consistency, asymptotic normality and oracle property of the proposed estimators are established. The proposals are illustrated via simulation studies and the Australian AIDS dataset.

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Year of publication:	2012
Authors:	Jiang, Rong ; Qian, Weimin ; Zhou, Zhangong
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 82.2012, 2, p. 308-317
Publisher:	Elsevier
Subject:	Kaplan–Meier estimator \| Randomly censored data \| Composite quantile regression \| Variable selection \| LASSO

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010576151