Title: Focused estimation and model averaging for high-dimensional data: an overview
Authors: Claeskens, Gerda # ×
Issue Date: 2012
Publisher: Vereniging voor Statistiek
Series Title: Statistica Neerlandica vol:66 issue:3 pages:272-287
Abstract: The quest for a good estimator of a certain focus or target is present regardless of the dimensionality of the data. Obtaining such a good estimator with low mean squared error (MSE), or a prediction with low prediction error often proceeds via a variable selection or model selection search. Estimators can also be averaged to enlarge the space of possible estimators in an attempt to further lower the MSE. While these methods are being studied mostly for unpenalized estimation methods in situations with the number of variables much smaller than the sample size, this article concentrates on the additional difficulties and challenges when applying focused model selection for squared error loss with penalized estimation, for example, in a context of high-dimensional data.
ISSN: 0039-0402
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Operations Research and Business Statistics (ORSTAT), Leuven
Leuven Statistics Research Centre (LStat)
× corresponding author
# (joint) last author

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