Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
415165 | Computational Statistics & Data Analysis | 2010 | 13 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Douglas P. Wiens, Eden K.H. Wu,