Premium
Monotone spline‐based least squares estimation for panel count data with informative observation times
Author(s) -
Deng Shirong,
Liu Li,
Zhao Xingqiu
Publication year - 2015
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201400217
Subject(s) - mathematics , statistics , spline (mechanical) , monotone polygon , least squares function approximation , estimation , count data , econometrics , algorithm , estimator , economics , engineering , poisson distribution , geometry , structural engineering , management
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.