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A New Design Criterion When Heteroscedasticity is Ignored

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Abstract

This paper examines the construction of optimal designs when one assumes a homoscedastic linear model, but the underlying model is heteroscedastic. A criterion that takes this type of misspecification into account is formulated and an equivalence theorem is given. We also provide explicit optimal designs for single-factor and multi-factor experiments under various heteroscedastic assumptions and discuss the relationship between the D-optimal design sought here and the conventional D-optimal design.

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Authors and Affiliations

  1. Department of Applied Statistics and Operations Research, Bowling Green State University, Bowling Green, OH, 43403, USA

    Grace Montepiedra

  2. Department of Biostatistics, UCLA, 10833 Le Conte Ave, Los Angeles, CA, 90095-1772, USA

    Weng Kee Wong

Authors
  1. Grace Montepiedra
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  2. Weng Kee Wong
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Montepiedra, G., Wong, W.K. A New Design Criterion When Heteroscedasticity is Ignored. Annals of the Institute of Statistical Mathematics 53, 418–426 (2001). https://doi.org/10.1023/A:1012435125788

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