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

Minggen Lu, Ying Zhang, Jian Huang

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


We study semiparametric likelihood-based methods for panel count data with proportional mean model E[ℕ (t)|Z] = Λ 0(t) exp(β T 0 Z), where Z is a vector of covariates and Λ 0(t) is the baseline mean function. We propose to estimate Λ 0(t) and β 0 jointly with Λ 0(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 Λ 0(t) are consistent with a possibly better than n 1/3 convergence rate if Λ 0(t) is sufficiently smooth. The normality of the estimators of β 0 is 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 (US)
Pages (from-to)1060-1070
Number of pages11
JournalJournal of the American Statistical Association
Issue number487
StatePublished - 2009
Externally publishedYes


  • 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


Dive into the research topics of 'Semiparametric estimation methods for panel count data using monotone B-splines'. Together they form a unique fingerprint.

Cite this