Power in High-dimensional testing Problems

Anders Kock and David Preinerstorfer

Working Papers ECARES from ULB -- Universite Libre de Bruxelles

Abstract: Fan et al. (2015) recently introduced a remarkable method for increasing asymptotic power of tests in high-dimensional testing problems. If applicable to a given test, their power enhancement principle leads to an improved test that has the same asymptotic size, uniformly non-inferior asymptotic power, and is consistent against a strictly broader range of alternatives than the initially given test. We study under which conditions this method can be applied and show the following: In asymptotic regimes where the dimensionality of the parameter space is fixed as sample size increases, there often exist tests that can not be further improved with the power enhancement principle. When the dimensionality of the parameter space can increase with sample size, however, there typically is a range of "slowly" diverging rates for which every test with asymptotic size smaller than one can be improved with the power enhancement principle. While the latter statement in general does not extend to all rates at which the dimensionality increases with sample size, we give sufficient conditions under which this is the case.

Keywords: high-dimensional testing problems; power enhancement principle; power enhancement component; asymptotic enhanceability; marginal LAN (search for similar items in EconPapers)
JEL-codes: C12 (search for similar items in EconPapers)
Pages: 22 p.
Date: 2017-11
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Journal Article: Power in High‐Dimensional Testing Problems (2019)
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