JSM 2005 - Toronto

Abstract #303736

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 143
Type: Contributed
Date/Time: Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #303736
Title: An Optimization Approach for the Parameter Estimation of the Nonlinear Mixed-effects Models
Author(s): Jing Wang*+
Companies: Louisiana State University
Address: 7250 Perkins Rd, Baton Rouge, LA, 70808, United States
Keywords: Nonlinear Mixed Effects Model ; Importance Sampling ; Stochastic Approximation ; Repeated-Measurements data ; Maximum Likelihood
Abstract:

Nonlinear mixed-effects models (NLMM) have received a great deal of attention in the statistical literature in recent years because of the flexibility they offer in handling the unbalanced repeated-measurements data that arise in different areas of investigation, such as pharmacokinetics. We concentrate here on maximum likelihood estimation for the parameters in nonlinear mixed-effects models. A rather complex numerical issue for maximum likelihood estimation in nonlinear mixed-effects models is the evaluation of the likelihood, which is given in the form of a multiple integral that, in most cases, does not have a closed-form expression. We restrict our attention in this article on numerical methods based on approximation for the likelihood. In addition, for a general optimization problem, recursive procedures need to be used to update the parameter estimates. The objective of this article is to propose an optimization approach for the parameter estimation in nonlinear mixed-effects models. This optimization method implements importance sampling for approximating likelihood and a stochastic recursive procedure for updating parameter estimates.

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Revised March 2005