PAC-Bayesian aggregation of affine estimators

Autor:	Montuelle, Lucie, Le Pennec, Erwan
Přispěvatelé:	Réseau de Transport d'Electricité [Paris] (RTE), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Modélisation en pharmacologie de population (XPOP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Jazyk:	angličtina
Rok vydání:	2014
Předmět:	Oracle inequality [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] Exponentially weighted aggregation FOS: Mathematics Deviation Mathematics - Statistics Theory [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH] Statistics Theory (math.ST) Regression
Popis:	Aggregating estimators using exponential weights depending on their risk appears optimal in expectation but not in probability. We use here a slight overpenalization to obtain oracle inequality in probability for such an explicit aggregation procedure. We focus on the fixed design regression framework and the aggregation of affine estimators and obtain results for a large family of affine estimators under a non necessarily independent sub-Gaussian noise assumptions.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f320a10b3b242aebf637f13c5412a949 http://arxiv.org/abs/1410.0661 Zobrazit plný text záznamu