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A comparative study of robust designs for M-estimated regression models

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  • Wiens, Douglas P.
  • Wu, Eden K.H.

Abstract

We obtain designs which are optimally robust against possibly misspecified regression models, assuming that the parameters are to be estimated by one of several types of M-estimation. Such designs minimize the maximum mean squared error of the predicted values, with the maximum taken over a class of departures from the fitted response function. One purpose of the study is to determine if, and how, the designs change in response to the robust methods of estimation as compared to classical least squares estimation. To this end, numerous examples are presented and discussed.

Suggested Citation

  • Wiens, Douglas P. & Wu, Eden K.H., 2010. "A comparative study of robust designs for M-estimated regression models," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1683-1695, June.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:6:p:1683-1695
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    References listed on IDEAS

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    1. Wiens, Douglas P., 1996. "Asymptotics of generalized M-estimation of regression and scale with fixed carriers, in an approximately linear model," Statistics & Probability Letters, Elsevier, vol. 30(3), pages 271-285, October.
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    Cited by:

    1. Linglong Kong & Douglas P. Wiens, 2015. "Model-Robust Designs for Quantile Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 233-245, March.

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