A MAXIMUM-LIKELIHOOD-ESTIMATION METHOD FOR RANDOM COEFFICIENT REGRESSION-MODELS
成果类型：
Article
署名作者：
MALLET, A
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
发表日期：
1986
页码：
645656
关键词：
摘要：
A method for estimating the distribution of the parameters of a random coefficient regression model is proposed. This distribution, accounting for interindividual variability, is assumed to lie in a wide class of probability distributions rather than in a given parametric class. Estimation is based on observations from a sample of individuals and likelihood is the estimation criterion. Experimental designs may be different among individuals, allowing the method to apply to routinely collected data. The problem has strong connections with the theory of optimum design of experiments. Conditions are given under which the problem has a unique solution, which then corresponds to a discrete distribution. A simple pharmacokinetic model involving two parameters is used as an example; these parameters have a bimodal distribution as statistical specification. Moreover, only on observation is available per individual; thus the method applies even when the model is not identifiable.