The LAD estimation of the change-point linear model with randomly censored data

Linjun Tang, Zhangong Zhou and Changchun Wu

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 2, 479-491

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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:45:y:2016:i:2:p:479-491

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DOI: 10.1080/03610926.2013.827720

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