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Robust model selection in regression via weighted likelihood methodology

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Author Info
Agostinelli, Claudio

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Abstract

Robust model selection procedures are introduced as a robust modification of the Akaike information criterion (AIC) and Mallows Cp. These extensions are based on the weighted likelihood methodology. When the model is correctly specified, these robust criteria are asymptotically equivalent to the classical ones under mild conditions. Robustness properties and the performance of the procedures are illustrated with examples and Monte Carlo simulations.

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File URL: http://www.sciencedirect.com/science/article/B6V1D-44JD67C-2/2/ed23f97f74339b885affa3ddc9789273
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Publisher Info
Article provided by Elsevier in its journal Statistics & Probability Letters.

Volume (Year): 56 (2002)
Issue (Month): 3 (February)
Pages: 289-300
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Handle: RePEc:eee:stapro:v:56:y:2002:i:3:p:289-300

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Related research
Keywords: Akaike information criterion Mallows Cp Robust model selection Weighted likelihood;

Cited by:
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  1. C. Agostinelli, 2002. "Robust stepwise regression," Journal of Applied Statistics, Taylor and Francis Journals, vol. 29(6), pages 825-840, August. [Downloadable!] (restricted)
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