A comparison of sequential and non‐sequential designs for discrimination between nested regression models

Abstract

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.