Linear shrinkage-based hypothesis test for large-dimensional covariance matrix

Taras Bodnar, Nestor Parolya*, Frederik Veldman

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

Abstract

The chapter is concerned with finding the asymptotic distribution of the estimated shrinkage intensity used in the definition of the linear shrinkage estimator of the covariance matrix, derived by Bodnar et al. (J Multivar Anal 132:215–228, 2014). As a result, a new test statistic is proposed which is deduced from the linear shrinkage estimator. This result is a ready-to-use multivariate hypothesis test in the large-dimensional asymptotic framework and constitutes the main result of the chapter. The theoretical findings are compared by means of a simulation study with existing tests, in particular with the commonly used corrected likelihood ratio test and the corrected John test, both derived by Wang and Yao (Electron J Stat 7:2164–2192, 2013).
Original languageEnglish
Title of host publicationAdvanced Statistical Methods in Process Monitoring, Finance, and Environmental Science: Essays in Honour of Wolfgang Schmid
EditorsSven Knoth, Yarema Okhrin, Philipp Otto
PublisherSpringer
Pages239-257
ISBN (Electronic)978-3-031-69111-9
ISBN (Print)978-3-031-69110-2
DOIs
Publication statusPublished - 2024

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Fingerprint

Dive into the research topics of 'Linear shrinkage-based hypothesis test for large-dimensional covariance matrix'. Together they form a unique fingerprint.

Cite this