Version 1
: Received: 16 March 2024 / Approved: 18 March 2024 / Online: 18 March 2024 (11:36:37 CET)
How to cite:
Makled, R.A.; Cheng, W. Exploring Multivariate Statistics: Unveiling the Power of Eigenvalues in Wishart Distribution Analysis. Preprints2024, 2024031027. https://doi.org/10.20944/preprints202403.1027.v1
Makled, R.A.; Cheng, W. Exploring Multivariate Statistics: Unveiling the Power of Eigenvalues in Wishart Distribution Analysis. Preprints 2024, 2024031027. https://doi.org/10.20944/preprints202403.1027.v1
Makled, R.A.; Cheng, W. Exploring Multivariate Statistics: Unveiling the Power of Eigenvalues in Wishart Distribution Analysis. Preprints2024, 2024031027. https://doi.org/10.20944/preprints202403.1027.v1
APA Style
Makled, R.A., & Cheng, W. (2024). Exploring Multivariate Statistics: Unveiling the Power of Eigenvalues in Wishart Distribution Analysis. Preprints. https://doi.org/10.20944/preprints202403.1027.v1
Chicago/Turabian Style
Makled, R.A. and Weihu Cheng. 2024 "Exploring Multivariate Statistics: Unveiling the Power of Eigenvalues in Wishart Distribution Analysis" Preprints. https://doi.org/10.20944/preprints202403.1027.v1
Abstract
Energy distance is used to eigenvalue distributions in this new Wishart distribution study, providing a new perspective on high-dimensional data. Multivariate statistics relies on Wishart distribution to explain covariance matrices. This analysis shows how degrees of freedom and covariance matrix configurations affect eigenvalue dispersion. We use energy distance based on eigenvalue distributions to assess Wishart distribution differences without the "curse of dimensionality" via extensive simulation simulations. This approach improves measurement accuracy and processing efficiency in distribution comparisons and enables new analytical pathways in scientific fields that need data covariance comprehension. Our results show that degrees of freedom and matrix structure considerably affect energy distances, expanding the theoretical framework and practical applications of statistical models.
Keywords
Energy Distance, Energy Statistic, Wishart distributions, Goodness of Fit, high-dimensional data.
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
