A comparison of sequential and non-sequential designs for discrimination between nested regression models
成果类型：
Article
署名作者：
Dette, H; Kwiecien, R
署名单位：
Ruhr University Bochum; RWTH Aachen University
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/91.1.165
发表日期：
2004
页码：
165176
关键词：
摘要：
Classical regression analysis is usually performed in two steps. In a first step an appropriate model is identified to describe the data-generating process and in a second step statistical inference is performed in the identified model. In this paper we investigate a sequential and a non-sequential design strategy, which take into account these different goals of the analysis for a class of nested models. It is demonstrated that non-sequential designs usually identify the 'correct' model with a higher probability than sequential methods, Although non-sequential designs can never be guaranteed to achieve the best possible efficiency in the 'correct' model, it is demonstrated by means of a simulation study that for realistic sample sizes the efficiencies of the non-sequential designs for the estimation of the parameters in the 'correct' model are at least as high as the corresponding efficiencies of the sequential methods.