Journal article
Efficient estimation for the proportional hazards model with interval censoring
The Annals of statistics, Vol.24(2), pp.540-568
04/01/1996
DOI: 10.1214/aos/1032894452
Abstract
The maximum likelihood estimator (MLE) for the proportional hazards model with "case 1" interval censored data is studied. It is shown that the MLE for the regression parameter is asymptotically normal with √n convergence rate and achieves the information bound, even though the MLE for the baseline cumulative hazard function only converges at n1/3 rate. Estimation of the asymptotic variance matrix for the MLE of the regression parameter is also considered. To prove our main results, we also establish a general theorem showing that the MLE of the finite-dimensional parameter in a class of semiparametric models is asymptotically efficient even though the MLE of the infinite-dimensional parameter converges at a rate slower than √n. The results are illustrated by applying them to a data set from a tumorigenicity study.
Details
- Title: Subtitle
- Efficient estimation for the proportional hazards model with interval censoring
- Creators
- Jian Huang - University of Iowa
- Resource Type
- Journal article
- Publication Details
- The Annals of statistics, Vol.24(2), pp.540-568
- DOI
- 10.1214/aos/1032894452
- ISSN
- 0090-5364
- eISSN
- 2168-8966
- Language
- English
- Date published
- 04/01/1996
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257624902771
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