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On the Identification of Extreme Elements in a Residual for the GMANOVA-MANOVA Model
Linköping University, Department of Mathematics, Applied Mathematics. Linköping University, Faculty of Science & Engineering. Department of Mathematics, University of Rwanda, Kigali, Rwanda.
Linköping University, Department of Mathematics, Applied Mathematics. Linköping University, Faculty of Science & Engineering. Department of Energy and Technology, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Linköping University, Department of Mathematics, Applied Mathematics. Linköping University, Faculty of Science & Engineering. (Matematisk statistik)ORCID iD: 0000-0001-9896-4438
2022 (English)In: Innovations in Multivariate Statistical Modeling: Navigating Theoretical and Multidisciplinary Domains / [ed] Andriëtte Bekker, Johannes T. Ferreira, Mohammad Arashi and Ding-Geng Chen, Cham: Springer Cham , 2022, p. 119-135Chapter in book (Refereed)
Abstract [en]

Two different matrix residuals in a special GMANOVA-MANOVA model have previously been established (see Byukusenge et al., 2021, “On residual analysis in the GMANOVA-MANOVA model”). The residual that is studied in this article is constructed via the difference of the observed group means and the estimated mean structure. The residual provides information about the appropriateness of the model assumptions concerning the mean structure. The aim of this paper is to study the distribution of the largest elements (by absolute value) of the residual via two data sets. Parametric bootstrap is used to identify thresholds so that extreme elements of the residuals can be identified.

Place, publisher, year, edition, pages
Cham: Springer Cham , 2022. p. 119-135
Series
Emerging Topics in Statistics and Biostatistics, ISSN 2524-7735, E-ISSN 2524-7743 ; 1
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-198562DOI: 10.1007/978-3-031-13971-0_6Libris ID: 0hnn46zkx6svcq1wISBN: 9783031139710 (print)OAI: oai:DiVA.org:liu-198562DiVA, id: diva2:1805528
Available from: 2023-10-17 Created: 2023-10-17 Last updated: 2023-11-21Bibliographically approved

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Byukusenge, Béatricevon Rosen, DietrichSingull, Martin

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CiteExportLink to record
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