Adaptive lasso penalised censored composite quantile regression. (2016)

• Record Type: Journal Article
• Authors: Bang, Sungwan; Cho, Hyungjun; Jhun, Myoungshic
• Is Part Of: International journal of data mining and bioinformatics. Volume 15:Number 1(2016)
• Page Start: 22
• Page End: 46
• Publication Date: 2016
• Subjects: adaptive lasso -- right censored data -- composite quantile regression -- inverse censoring probability -- variable selection -- accelerated failure time -- AFT -- heavy-tailed errors -- outliers -- loss function -- simulation; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

To account for censoring in estimating the accelerated failure time (AFT) model with right censored data, the weighted least squares regression (WLSR) has been developed by using the inverse-censoring-probability weights. However, it is well known that the traditional ordinary least squares may fail to produce a reliable estimator for data subject to heavy-tailed errors or outliers. For robust estimation in the AFT model, we propose the weighted composite quantile regression (WCQR) method, in which the sum of weighted multiple quantile objective functions based on the inverse-censoring-probability weights is used as a loss function. As a novel regularisation method for right censored data, we further propose the adaptive lasso penalised WCQR (AWCQR) method in order to perform simultaneous estimation and variable selection. The large sample properties of the WCQR and AWCQR estimators are established under some regularity conditions. The proposed methods are evaluated through simulation studies and real data applications.