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Linear Shrinkage-Based Hypothesis Test for Large-Dimensional Covariance Matrix
Linköping University, Department of Management and Engineering, Production Economics. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-7855-8221
Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands.
Delft University of Technology, Delft, The Netherlands.
2024 (English)In: Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science: Essays in Honour of Wolfgang Schmid / [ed] Sven Knoth, Yarema Okhrin, Philipp Otto, Springer, 2024, p. 239-257Chapter in book (Refereed)
Abstract [en]

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).

Place, publisher, year, edition, pages
Springer, 2024. p. 239-257
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-213335DOI: 10.1007/978-3-031-69111-9_12ISBN: 9783031691102 (print)ISBN: 9783031691119 (electronic)OAI: oai:DiVA.org:liu-213335DiVA, id: diva2:1955127
Available from: 2025-04-29 Created: 2025-04-29 Last updated: 2025-04-29

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