Semiparametric median residual life model and inference

Yanyuan Ma, Guosheng Yin

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

For randomly censored data, the authors propose a general class of semiparametric median residual life models. They incorporate covariates in a generalized linear form while leaving the baseline median residual life function completely unspecified. Despite the non-identifiability of the survival function for a given median residual life function, a simple and natural procedure is proposed to estimate the regression parameters and the baseline median residual life function. The authors derive the asymptotic properties for the estimators, and demonstrate the numerical performance of the proposed method through simulation studies. The median residual life model can be easily generalized to model other quantiles, and the estimation method can also be applied to the mean residual life model.

Original languageEnglish (US)
Pages (from-to)665-679
Number of pages15
JournalCanadian Journal of Statistics
Volume38
Issue number4
DOIs
StatePublished - Dec 1 2010

Fingerprint

Residual Life
Baseline
Mean Residual Life
Model
Survival Function
Linear Forms
Censored Data
Quantile
Asymptotic Properties
Covariates
Regression
Median
Inference
Simulation Study
Estimator
Estimate
Demonstrate

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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Semiparametric median residual life model and inference. / Ma, Yanyuan; Yin, Guosheng.

In: Canadian Journal of Statistics, Vol. 38, No. 4, 01.12.2010, p. 665-679.

Research output: Contribution to journalArticle

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