Title

A Semiparametric Pseudolikelihood Estimation Method For Panel Count Data

Keywords

Bootstrap; Consistency; Counting process; Empirical process; Iterative algorithm; Monte carlo; Panel count data; Profile likelihood; Semiparametric maximum pseudolikelihood estimator

Abstract

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} = Λ0(t) exp(β0′Z). 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 (β0, Λ0). The algorithm appears to be attractive, especially when β0 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. © 2002 Biometrika Trust.

Publication Date

12-1-2002

Publication Title

Biometrika

Volume

89

Issue

1

Number of Pages

39-48

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1093/biomet/89.1.39

Socpus ID

0346020373 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0346020373

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