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Finite sample performance of sequential designs for model identification

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  • Dette, Holger
  • Kwiecien, Robert

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. An intuitively appealing approach to the design of experiment for these different purposes are sequential strategies, which use parts of the sample for model identification and adapt the design according to the outcome of the identification steps. In this paper we investigate the finite sample properties of two sequential design strategies, which were recently proposed in the literature. A detailed comparison of sequential designs for model discrimination in several regression models is given by means of a simulation study. Some non-sequential designs are also included in the study.

Suggested Citation

  • Dette, Holger & Kwiecien, Robert, 2003. "Finite sample performance of sequential designs for model identification," Technical Reports 2003,32, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200332
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    References listed on IDEAS

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    1. Atanu Biswas, 2002. "An efficient design for model discrimination and parameter estimation in linear models," Biometrika, Biometrika Trust, vol. 89(3), pages 709-718, August.
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