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

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

Journal of Applied Mathematics, 2012, vol. 2012, issue 1

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.

Date: 2012
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1155/2012/250909

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2012:y:2012:i:1:n:250909

Access Statistics for this article

More articles in Journal of Applied Mathematics from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().