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

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  • Linjun Tang
  • Zhangong Zhou
  • Changchun Wu

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.

Suggested Citation

  • Linjun Tang & Zhangong Zhou & Changchun Wu, 2016. "The LAD estimation of the change-point linear model with randomly censored data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(2), pages 479-491, January.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:2:p:479-491
    DOI: 10.1080/03610926.2013.827720
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