STOCHASTIC OPTIMIZATION ALGORITHMS OF A BAYESIAN DESIGN CRITERION FOR BAYESIAN PARAMETER ESTIMATION OF NONLINEAR REGRESSION MODELS - APPLICATION IN PHARMACOKINETICS

摘要

This article proposes three stochastic algorithms to optimize a Bayesian design criterion for Bayesian estimation of the parameters of nonlinear regression models; this criterion is the information expected from an experiment. The first algorithm is based on a stochastic version of the simplex with an adaptive sampling procedure. The others are stochastic approximation algorithms: the Kiefer-Wolfowitz and the pseudogradient algorithms. We first present the information criterion and the optimization algorithms. The efficiency of each algorithm for optimizing this Bayesian design criterion is then assessed by a simulation study for a nonlinear model assuming a discrete prior distribution. An application for designing an experiment to estimate the kinetics of radioiodine thyroid uptake is then proposed. (C) 1997 Elsevier Science Inc. [References: 45]