1996 | OriginalPaper | Buchkapitel
Bootstrap Variable—Selection and Confidence Sets
verfasst von : Rudolf Beran
Erschienen in: Robust Statistics, Data Analysis, and Computer Intensive Methods
Verlag: Springer New York
Enthalten in: Professional Book Archive
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This paper analyzes estimation by bootstrap variable-selection in a simple Gaussian model where the dimension of the unknown parameter may exceed that of the data. A naive use of the bootstrap in this problem produces risk estimators for candidate variable-selections that have a strong upward bias. Resampling from a less overfitted model removes the bias and leads to bootstrap variable-selections that minimize risk asymptotically. A related bootstrap technique generates confidence sets that are centered at the best bootstrap variable-selection and have two further properties: the asymptotic coverage probability for the unknown parameter is as desired and the confidence set is geometrically smaller than a classical competitor. The results suggest a possible approach to confidence sets in other inverse problems where a regularization technique is used.