Adaptive model selection using empirical complexities

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Annals of Statistics, 27, 6, (1999), pp. 1830-1864
To cite or link this document: Lugosi, Gábor Nobel, Andrew B.
dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa 1998-06-01
dc.identifier.citation Annals of Statistics, 27, 6, (1999), pp. 1830-1864
dc.description.abstract Given $n$ independent replicates of a jointly distributed pair $(X,Y) \in {\cal R}^d \times {\cal R}$, we wish to select from a fixed sequence of model classes ${\cal F}_1, {\cal F}_2, \ldots$ a deterministic prediction rule $f: {\cal R}^d \to {\cal R}$ whose risk is small. We investigate the possibility of empirically assessing the {\em complexity} of each model class, that is, the actual difficulty of the estimation problem within each class. The estimated complexities are in turn used to define an adaptive model selection procedure, which is based on complexity penalized empirical risk. The available data are divided into two parts. The first is used to form an empirical cover of each model class, and the second is used to select a candidate rule from each cover based on empirical risk. The covering radii are determined empirically to optimize a tight upper bound on the estimation error. An estimate is chosen from the list of candidates in order to minimize the sum of class complexity and empirical risk. A distinguishing feature of the approach is that the complexity of each model class is assessed empirically, based on the size of its empirical cover. Finite sample performance bounds are established for the estimates, and these bounds are applied to several non-parametric estimation problems. The estimates are shown to achieve a favorable tradeoff between approximation and estimation error, and to perform as well as if the distribution-dependent complexities of the model classes were known beforehand. In addition, it is shown that the estimate can be consistent, and even possess near optimal rates of convergence, when each model class has an infinite VC or pseudo dimension. For regression estimation with squared loss we modify our estimate to achieve a faster rate of convergence.
dc.language.iso eng
dc.relation.ispartofseries Economics and Business Working Papers Series; 323
dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
dc.title Adaptive model selection using empirical complexities
dc.type info:eu-repo/semantics/workingPaper 2016-06-04T02:50:24Z
dc.subject.keyword Statistics, Econometrics and Quantitative Methods
dc.subject.keyword complexity regularization
dc.subject.keyword classification
dc.subject.keyword pattern recognition
dc.subject.keyword regression estimation
dc.subject.keyword curve fitting
dc.subject.keyword minimum description length
dc.rights.accessRights info:eu-repo/semantics/openAccess

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