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- W2088016919 abstract "SUMMARY Classical regression analysis is usually performed in two steps. In a first step an appro priate 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." @default.
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- W2088016919 date "2004-03-01" @default.
- W2088016919 modified "2023-10-18" @default.
- W2088016919 title "A comparison of sequential and non-sequential designs for discrimination between nested regression models" @default.
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- W2088016919 doi "https://doi.org/10.1093/biomet/91.1.165" @default.
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