The LAD estimation of the change-point linear model with randomly censored data. Issue 2 (17th January 2016)

• Record Type: Journal Article
• Authors: Tang, Linjun; Zhou, Zhangong; Wu, Changchun
• Is Part Of: Communications in statistics. Volume 45:Issue 2(2016)
• Page Start: 479
• Page End: 491
• Publication Date: 2016-01-17
• Subjects: Linear model -- Change-point -- Random censoring -- LAD; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2013.827720
• Languages: English
• ISSNs: 0361-0926

Abstract

Abstract: In this paper, a change-point linear model with randomly censored data is investigated. We propose the least absolute deviation estimation procedure for regression and change-point parameters simultaneously. The asymptotic properties of the change-point and regression parameter estimators are obtained. We show that the resulting regression parameter estimator is asymptotically normal, and the change-point estimator converges weakly to the minimizer of a given random process. The extensive simulation studies and the analysis of an acute myocardial infarction data set are conducted to illustrate the finite sample performance of the proposed method.