Superconsistency of tests in high dimensions
Journal
Econometric Theory
Type
forthcoming
Date Issued
2022
Author(s)
Kock, Anders Bredahl
;
Preinerstorfer, David
Abstract
To assess whether there is some signal in a big database, aggregate tests for the global null hypothesis of no effect are routinely applied in practice before more specialized analysis is carried out. Although a plethora of aggregate tests is available, each test has its strengths but also its blind spots. In a Gaussian sequence model, we study whether it is possible to obtain a test with substantially better consistency properties than the likelihood ratio (i.e., Euclidean norm based) test. We establish an impossibility result, showing that in the high-dimensional framework we consider, the set of alternatives for which a test may improve upon the likelihood ratio test -- that is, its superconsistency points -- is always asymptotically negligible in a relative volume sense.
Language
English
HSG Classification
contribution to scientific community
Refereed
Yes
Subject(s)
Eprints ID
266186