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Regularized estimation of the Mahalanobis distance based on modified Cholesky decomposition
Linnaeus University, School of Business and Economics, Department of Economics and Statistics. (DISA ; dsm)ORCID iD: 0000-0002-0789-5826
Beijing Normal University at Zhuhai, China;United International College (BNU-HKBU), China.
Linnaeus University, School of Business and Economics, Department of Economics and Statistics.
2022 (English)In: Communications in Statistics: Case Studies, Data Analysis and Applications, ISSN 2373-7484, Vol. 8, no 4, p. 559-573Article in journal (Refereed) Published
Abstract [en]

Estimating inverse covariance matrix is an essential part of many statistical methods. This paper proposes a regularized estimator for the inverse covariance matrix. Modified Cholesky decomposition (MCD) is utilized to construct positive definite estimators. Instead of directly regularizing the inverse covariance matrix itself, we impose regularization on the Cholesky factor. The estimated inverse covariance matrix is used to build Mahalanobis distance (MD). The proposed method is evaluated by detecting outliers through simulations and empirical studies.

Place, publisher, year, edition, pages
Taylor & Francis, 2022. Vol. 8, no 4, p. 559-573
Keywords [en]
Modified Cholesky decomposition, Mahalanobis distance, regularization, smoothing, outlier detection
National Category
Probability Theory and Statistics
Research subject
Statistics/Econometrics
Identifiers
URN: urn:nbn:se:lnu:diva-115684DOI: 10.1080/23737484.2022.2107961Scopus ID: 2-s2.0-85135606762OAI: oai:DiVA.org:lnu-115684DiVA, id: diva2:1686015
Available from: 2022-08-08 Created: 2022-08-08 Last updated: 2023-04-17Bibliographically approved

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Dai, DeliangLiang, Yuli

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