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A semiparametric pseudolikelihood estimation method for panel count data

Listed author(s):
  • Ying Zhang
Registered author(s):

    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} = &Lgr;-sub-0 (t) exp (&bgr;′-sub-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 (&bgr;-sub-0, &Lgr;-sub-0). The algorithm appears to be attractive, especially when &bgr;-sub-0 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. Copyright Biometrika Trust 2002, Oxford University Press.

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    Article provided by Biometrika Trust in its journal Biometrika.

    Volume (Year): 89 (2002)
    Issue (Month): 1 (March)
    Pages: 39-48

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    Handle: RePEc:oup:biomet:v:89:y:2002:i:1:p:39-48
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