Semiparametric estimation methods for panel count data using monotone B-splines

Minggen Lu, Ying Zhang, Jian Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

40 Citations (Scopus)

Abstract

We study semiparametric likelihood-based methods for panel count data with proportional mean model E[ℕ (t)|Z] = Λ0(t) exp(βT0Z), where Z is a vector of covariates and Λ0(t) is the baseline mean function. We propose to estimate Λ0(t) and β0jointly with Λ0(t) approximated by monotone B-splines and to compute the estimators using generalized Rosen algorithm proposed by Jamshidian (2004). We show that the proposed spline-based likelihood estimators of Λ0(t) are consistent with a possibly better than n1/3convergence rate if Λ0(t) is sufficiently smooth. The normality of the estimators of β0is also established. Comparisons between the proposed estimators and their alternatives studied in Wellner and Zhang (2007) are made through simulations studies, regarding their finite sample performance and computational complexity. A real example from a bladder tumor clinical trial is used to illustrate the methods.
Original languageEnglish
Pages (from-to)1060-1070
Number of pages11
JournalJournal of the American Statistical Association
Volume104
Issue number487
DOIs
Publication statusPublished - 14 Oct 2009
Externally publishedYes

Keywords

  • B-splines
  • Counting process
  • Empirical process
  • Generalized rosen algorithm
  • Maximum likelihood method
  • Maximum pseudolikelihood method
  • Monte Carlo

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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