A semiparametric pseudolikelihood estimation method for panel count data

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Abstract

In this paper, we study panel count data with covariates. A semiparametric pseudolikelihood estimation method is proposed based on the assumption that, given a covariate vector Z, the underlying counting process is a nonhomogeneous Poisson process with the conditional mean function given by E{N(t)|Z} = Λ0(t) exp(β0′Z). The proposed estimation method is shown to be robust in the sense that the estimator converges to its true value regardless of whether or not N(t) is a conditional Poisson process, given Z. An iterative numerical algorithm is devised to compute the semiparametric maximum pseudolikelihood estimator of (β0, Λ0). The algorithm appears to be attractive, especially when β0 is a high-dimensional regression parameter. Some simulation studies are conducted to validate the method. Finally, the method is applied to a real dataset from a bladder tumour study.

Original languageEnglish (US)
Pages (from-to)39-48
Number of pages10
JournalBiometrika
Volume89
Issue number1
DOIs
StatePublished - Dec 1 2002
Externally publishedYes

Keywords

  • Bootstrap
  • Consistency
  • Counting process
  • Empirical process
  • Iterative algorithm
  • Monte carlo
  • Panel count data
  • Profile likelihood
  • Semiparametric maximum pseudolikelihood estimator

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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