Semiparametric partially linear varying coefficient models with panel count data

Xin He, Xuenan Feng, Xingwei Tong, Xingqiu Zhao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)


This paper studies semiparametric regression analysis of panel count data, which arise naturally when recurrent events are considered. Such data frequently occur in medical follow-up studies and reliability experiments, for example. To explore the nonlinear interactions between covariates, we propose a class of partially linear models with possibly varying coefficients for the mean function of the counting processes with panel count data. The functional coefficients are estimated by B-spline function approximations. The estimation procedures are based on maximum pseudo-likelihood and likelihood approaches and they are easy to implement. The asymptotic properties of the resulting estimators are established, and their finite-sample performance is assessed by Monte Carlo simulation studies. We also demonstrate the value of the proposed method by the analysis of a cancer data set, where the new modeling approach provides more comprehensive information than the usual proportional mean model.
Original languageEnglish
Pages (from-to)439-466
Number of pages28
JournalLifetime Data Analysis
Issue number3
Publication statusPublished - 1 Jul 2017


  • Asymptotic normality
  • B-spline
  • Counting process
  • Maximum likelihood
  • Maximum pseudo-likelihood
  • Panel count data
  • Varying-coefficient

ASJC Scopus subject areas

  • Applied Mathematics


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