An EM algorithm for the proportional hazards model with doubly censored data
AbstractIn this paper, we consider a new procedure for estimating parameters in the proportional hazards model with doubly censored data. Computing the maximum likelihood estimator with doubly censored data is often nontrivial and requires a certain constraint optimization procedure, which is computationally unstable and sometimes fails to converge. We propose an approximated likelihood and study the maximum approximated likelihood estimator, which is obtained by maximizing the approximated likelihood. In comparison to the maximum likelihood estimator, this new estimator is stable and always converges with an efficient EM algorithm we develop. The stability of the new estimator even with moderate sample sizes is amply demonstrated through simulated and real data. For theoretical justification of the approximated likelihood, we show the consistency of the maximum approximated likelihood estimator.
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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 57 (2013)
Issue (Month): 1 ()
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Web page: http://www.elsevier.com/locate/csda
Doubly censored data; EM algorithm; Proportional hazards model;
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- T. Cai, 2004. "Semiparametric regression analysis for doubly censored data," Biometrika, Biometrika Trust, vol. 91(2), pages 277-290, June.
- Jong S. Kim, 2003. "Maximum likelihood estimation for the proportional hazards model with partly interval-censored data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 489-502.
- Kim, Yongdai & Kim, Bumsoo & Jang, Woncheol, 2010. "Asymptotic properties of the maximum likelihood estimator for the proportional hazards model with doubly censored data," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1339-1351, July.
- Xu, Wenjing & Pan, Qing & Gastwirth, Joseph L., 2014. "Cox proportional hazards models with frailty for negatively correlated employment processes," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 295-307.
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