Monotone spline-based least squares estimation for panel count data with informative observation times

Shirong Deng, Li Liu, Xingqiu Zhao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


KGaA, Weinheim 57 5 September 2015 10.1002/bimj.201400217 Research Paper Disease Modeling KGaA, Weinheim. This article discusses the statistical analysis of panel count data when the underlying recurrent event process and observation process may be correlated. For the recurrent event process, we propose a new class of semiparametric mean models that allows for the interaction between the observation history and covariates. For inference on the model parameters, a monotone spline-based least squares estimation approach is developed, and the resulting estimators are consistent and asymptotically normal. In particular, our new approach does not rely on the model specification of the observation process. The proposed inference procedure performs well through simulation studies, and it is illustrated by the analysis of bladder tumor data.
Original languageEnglish
Pages (from-to)743-765
Number of pages23
JournalBiometrical Journal
Issue number5
Publication statusPublished - 1 Sep 2015


  • Informative observation process
  • Least squares estimation
  • Monotone B-splines
  • Panel count data
  • Semiparametric mean models

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this