A comparative study of robust designs for M-estimated regression models
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.
Year of publication: |
2010
|
---|---|
Authors: | Wiens, Douglas P. ; Wu, Eden K.H. |
Published in: |
Computational Statistics & Data Analysis. - Elsevier, ISSN 0167-9473. - Vol. 54.2010, 6, p. 1683-1695
|
Publisher: |
Elsevier |
Keywords: | Bias Bounded influence Cubic regression Finite design space Generalized M-estimation Ordinary M-estimation Polynomial regression Redescending Simulated annealing |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
Distributions minimizing Fisher information for location in Kolmogorov neighbourhoods
Wu, Eden K.H., (1997)
-
Robust designs for 3D shape analysis with spherical harmonic descriptors
Dette, Holger, (2007)
-
Robust designs for series estimation
Dette, Holger, (2007)
- More ...