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

Authors:
Douglas P. Wiens;Eden K. H. Wu
Affiliations:
Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada T6G 2G1;Department of Statistics, Chinese University of Hong Kong, Hong Kong
Venue:
Computational Statistics & Data Analysis
Year:
2010

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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.