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Empirical Likelihood Estimation for Population Pharmacokinetic Study Based on Generalized Linear Model

Author

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  • Fang-rong Yan
  • Jin-guan Lin
  • Yuan Huang
  • Jun-lin Liu
  • Tao Lu

Abstract

To obtain efficient estimation of parameters is a major objective in population pharmacokinetic study. In this paper, we propose an empirical likelihood‐based method to analyze the population pharmacokinetic data based on the generalized linear model. A nonparametric version of the Wilk′s theorem for the limiting distributions of the empirical likelihood ratio is derived. Simulations are conducted to demonstrate the accuracy and efficiency of empirical likelihood method. An application illustrating our methods and supporting the simulation study results is presented. The results suggest that the proposed method is feasible for population pharmacokinetic data.

Suggested Citation

  • Fang-rong Yan & Jin-guan Lin & Yuan Huang & Jun-lin Liu & Tao Lu, 2012. "Empirical Likelihood Estimation for Population Pharmacokinetic Study Based on Generalized Linear Model," Journal of Applied Mathematics, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnljam:v:2012:y:2012:i:1:n:250909
    DOI: 10.1155/2012/250909
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    References listed on IDEAS

    as
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    4. Li, Gang, 1995. "On nonparametric likelihood ratio estimation of survival probabilities for censored data," Statistics & Probability Letters, Elsevier, vol. 25(2), pages 95-104, November.
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