Versatile estimation in censored single-index hazards regression

Chin Tsang Chiang, Shao Hsuan Wang, Ming Yueh Huang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

One attractive advantage of the presented single-index hazards regression is that it can take into account possibly time-dependent covariates. In such a model formulation, the main theme of this research is to develop a theoretically valid and practically feasible estimation procedure for the index coefficients and the induced survival function. In particular, compared with the existing pseudo-likelihood approaches, our one proposes an automatic bandwidth selection and suppresses an influence of outliers. By making an effective use of the considered versatile survival process, we further reduce a substantial finite-sample bias in the Chambless-Diao type estimator of the most popular time-dependent accuracy summary. The asymptotic properties of estimators and data-driven bandwidths are also established under some suitable conditions. It is found in simulations that the proposed estimators and inference procedures exhibit quite satisfactory performances. Moreover, the general applicability of our methodology is illustrated by two empirical data.

Original languageEnglish
Pages (from-to)523-551
Number of pages29
JournalAnnals of the Institute of Statistical Mathematics
Volume70
Issue number3
DOIs
StatePublished - 1 Jun 2018

Keywords

  • Accuracy measure
  • Conditional survival function
  • Cross-validation
  • Kaplan–Meier estimator
  • Pseudo-integrated least squares estimator
  • Pseudo-maximum likelihood estimator
  • Single-index hazards model
  • U-statistic

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