A global partial likelihood estimation in the additive Cox proportional hazards model

Huazhen Lin, Ye He, Jian Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)

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 languageEnglish
Pages (from-to)71-87
Number of pages17
JournalJournal of Statistical Planning and Inference
Volume169
DOIs
Publication statusPublished - 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

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