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Semiparametric proportional mean residual life model with covariates missing at random

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  • Xiaolin Chen
  • Qihua Wang

Abstract

In this paper, we consider statistical inference for the proportional mean residual life model when some covariates are missing at random. Simple and augmented inverse probability-weighted estimating equations are used to obtain the estimators of the regression coefficients and baseline mean residual life function. The unknown non-missingness probability and some unknown conditional expectations are estimated by the kernel smoothing technique. We show that the simple inverse probability-weighted estimator with estimated non-missingness probability is more efficient than that with the true non-missingness probability, while the augmented inverse probability-weighted estimator with estimated non-missingness probability and that with the true non-missingness probability have the same efficiency. The asymptotic properties of all the proposed estimators are established. Extensive simulation studies are conducted to examine the finite sample performance of the proposed estimator. At last, the proposed method is applied to the mouse leukaemia data.

Suggested Citation

  • Xiaolin Chen & Qihua Wang, 2013. "Semiparametric proportional mean residual life model with covariates missing at random," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(3), pages 647-663, September.
  • Handle: RePEc:taf:gnstxx:v:25:y:2013:i:3:p:647-663
    DOI: 10.1080/10485252.2013.779376
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    References listed on IDEAS

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    2. Qi, Lihong & Wang, C.Y. & Prentice, Ross L., 2005. "Weighted Estimators for Proportional Hazards Regression With Missing Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1250-1263, December.
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    4. Sun, Liuquan & Zhang, Zhigang, 2009. "A Class of Transformed Mean Residual Life Models With Censored Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 803-815.
    5. Y. Q. Chen & S. Cheng, 2005. "Semiparametric regression analysis of mean residual life with censored survival data," Biometrika, Biometrika Trust, vol. 92(1), pages 19-29, March.
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