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Biometrika 2002 89(1):39-48; doi:10.1093/biomet/89.1.39
© 2002 by Biometrika Trust
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A semiparametric pseudolikelihood estimation method for panel count data

Ying Zhang1

1 Department of Statistics, University of Central Florida, P.O.Box 162370, Orlando, Florida 32816, U.S.Azhang{at}mail.ucf.edu

In this paper, we study panel count data with covariates.A semiparametric pseudolikelihood estimation method is proposed based on the assumption that, given a covariate vector Z, the underlying counting process is a nonhomogeneous Poisson process with the conditional mean function given by E{N (t) |Z} = {Lambda}0 (t) exp (ß'0Z). The proposed estimation method is shown to be robust in the sense that the estimator converges to its true value regardless of whether or not N (t) is a conditional Poisson process, given Z. An iterative numerical algorithm is devised to compute the semiparametric maximum pseudolikelihood estimator of (ß0, {Lambda}0). The algorithm appears to be attractive, especially when ß0 is a high-dimensional regression parameter. Some simulation studies are conducted to validate the method. Finally, the method is applied to a real dataset from a bladder tumour study.

Key Words: Bootstrap; Consistency; Counting process; Empirical process; Iterative algorithm; Monte Carlo; Panel count data; Profile likelihood; Semiparametric maximum pseudolikelihood estimator

Received October 2000. Revised April 2001


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