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P-values for high-dimensional regression

Abstract:

Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p-values are not available. An exception is a recent proposal by Wasserman and Roeder (2008) which splits the data into two parts. The number of variables is then reduced to a manageable size using the first split, while classical variable selection techniques can be applied to the remaining variables,...

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Publication status:
Published

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Publisher copy:
10.1198/jasa.2009.tm08647

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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Bühlmann, P More by this author
Journal:
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume:
104
Issue:
488
Pages:
1671-1681
Publication date:
2008-11-13
DOI:
EISSN:
1537-274X
ISSN:
0162-1459
URN:
uuid:193d3f27-8a96-4230-a326-0d1eec0a1c07
Source identifiers:
97829
Local pid:
pubs:97829
Language:
English
Keywords:

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