Maximum Likelihood Estimation for Semiparametric Regression Models With Panel Count Data.

ABSTRACT: Panel count data, in which the observation for each study subject consists of the number of recurrent events between successive examinations, are commonly encountered in industrial reliability testing, medical research, and various other scientific investigations. We formulate the effects of potentially time-dependent covariates on one or more types of recurrent events through non-homogeneous Poisson processes with random effects. We adopt nonparametric maximum likelihood estimation under arbitrary examination schemes and develop a simple and stable EM algorithm. We show that the resulting estimators of the regression parameters are consistent and asymptotically normal, with a covariance matrix that achieves the semiparametric efficiency bound and can be estimated through profile likelihoo

SUBMITTER: Zeng BD 

PROVIDER: S-EPMC8691743 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature