An embedded estimating equation for the additive risk model with biased-sampling data

Feipeng Zhang, Xingqiu Zhao, Yong Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

This paper presents a novel class of semiparametric estimating functions for the additive model with right-censored data that are obtained from general biased-sampling. The new estimator can be obtained using a weighted estimating equation for the covariate coeffcients, by embedding the biased-sampling data into left-truncated and right-censored data. The asymptotic properties (consistency and asymptotic normality) of the proposed estimator are derived via the modern empirical processes theory. Based on the cumulative residual processes, we also propose graphical and numerical methods to assess the adequacy of the additive risk model. The good finite-sample performance of the proposed estimator is demonstrated by simulation studies and two applications of real datasets.

Original languageEnglish
Pages (from-to)1495-1518
Number of pages24
JournalScience China Mathematics
Volume61
Issue number8
DOIs
Publication statusPublished - Aug 2018

Keywords

  • 62N02
  • 62N03
  • additive risk model
  • biased-sampling data
  • estimating equation
  • missing covariates
  • model checking

ASJC Scopus subject areas

  • Mathematics(all)

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