Abstract
The additive Cox model has been considered by many authors. However, the existing methods are either inefficient or their asymptotical properties are not well developed. In this article, we propose a global partial likelihood method to estimate the additive Cox model. We show that the proposed estimator is consistent and asymptotically normal. We also show that the linear functions of the estimated nonparametric components achieve semiparametric efficiency bound. Simulation studies show that our proposed estimator has much less mean squared error than the existing methods. Finally, we apply the proposed approach to the "nursing home" data set (Morris et al. 1994).
Original language | English |
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Pages (from-to) | 71-87 |
Number of pages | 17 |
Journal | Journal of Statistical Planning and Inference |
Volume | 169 |
DOIs | |
Publication status | Published - 1 Jan 2016 |
Keywords
- Additive Cox model
- Asymptotical properties
- Global partial likelihood
- Semiparametric efficiency
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
- Applied Mathematics