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A General Akaike-Type Criterion for Model Selection in Robust Regression

P. Burman and D. Nolan
Biometrika
Vol. 82, No. 4 (Dec., 1995), pp. 877-886
Published by: Oxford University Press on behalf of Biometrika Trust
DOI: 10.2307/2337352
Stable URL: http://www.jstor.org/stable/2337352
Page Count: 10
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A General Akaike-Type Criterion for Model Selection in Robust Regression
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Abstract

Akaike's procedure (1970) for selecting a model minimises an estimate of the expected squared error in predicting new, independent observations. This selection criterion was designed for models fitted by least squares. A different model-fitting technique, such as least absolute deviation regression, requires an appropriate model selection procedure. This paper presents a general Akaike-type criterion applicable to a wide variety of loss functions for model fitting. It requires only that the function be convex with a unique minimum, and twice differentiable in expectation. Simulations show that the estimators proposed here well approximate their respective prediction errors.

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