A note on the generalized degrees of freedom under the L1 loss function

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Xiaoli Gao, Associate Professor (Creator)
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/

Abstract: Generalized degrees of freedom measure the complexity of a modeling procedure; a modeling procedure is a combination of model selection and model fitting. In this manuscript, we consider two definitions of generalized degrees of freedom for a modeling procedure under the L1 loss function, and investigate the connections between those two definitions. We also propose the extended Akaike information criterion, the adaptive model selection, and the extended generalized cross-validation under the L1 loss function. Finally, we extend the results to M-estimation.

Additional Information

Journal of Statistical Planning and Inference, 141(2), 677-686
Language: English
Date: 2011
Adaptive model selection, Covariance penalty, Degrees of freedom, Generalized cross-validation, Least absolute deviations, Modeling procedure

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