Efficiency for Regularization Parameter Selection in Penalized Likelihood Estimation of Misspecified Models

It has been shown that AIC-type criteria are asymptotically efficient selectors of the tuning parameter in non-concave penalized regression methods under the assumption that the population variance is known or that a consistent estimator is available. We relax this assumption to prove that AIC itsel...

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Veröffentlicht in:	arXiv.org 2013-02
Hauptverfasser:	Flynn, Cheryl J, Hurvich, Clifford M, Simonoff, Jeffrey S
Format:	Artikel
Sprache:	eng
Schlagworte:	Asymptotic properties Computer simulation Generalized linear models Maximum likelihood estimation Parameter estimation Regression Regularization Selectors Statistical models Statistics - Machine Learning
Online-Zugang:	Volltext
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