Regularization in statistics

Autores: Peter J Bickel, Bo Li, Alexandre B. Tsybakov, Sara van de Geer, Bin Yu, Teófilo Valdés Sánchez , Carlos Rivero Rodríguez , Jianqing Fan, Aad van der Vaart
Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 15, Nº. 2, 2006, págs. 271-344
Idioma: inglés
DOI: 10.1007/bf02607055
Enlaces
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Resumen
- This paper is a selective review of the regularization methods scattered in statistics literature. We introduce a general conceptual approach to regularization and fit most existing methods into it. We have tried to focus on the importance of regularization when dealing with today's high-dimensional objects: data and models. A wide range of examples are discussed, including nonparametric regression, boosting, covariance matrix estimation, principal component estimation, subsampling.