Testing Spherical Symmetry Based on Statistical Representative Points

Jiajuan Liang, Ping He () and Qiong Liu
Additional contact information
Jiajuan Liang: Department of Statistics and Data Science, BNU-HKBU United International College, Zhuhai 519087, China
Ping He: Department of Statistics and Data Science, BNU-HKBU United International College, Zhuhai 519087, China
Qiong Liu: Department of Statistics and Data Science, BNU-HKBU United International College, Zhuhai 519087, China

Mathematics, 2024, vol. 12, issue 24, 1-19

Abstract: This paper introduces a novel chisquare test for spherical symmetry, utilizing statistical representative points. The proposed representative-point-based chisquare statistic is shown, through a Monte Carlo study, to considerably improve the power performance compared to the traditional equiprobable chisquare test in many high-dimensional cases. While the test requires relatively large sample sizes to approximate the chisquare distribution, obtaining critical values from existing chisquare tables is simpler compared to many existing tests for spherical symmetry. A real-data application demonstrates the robustness of the proposed method against different choices of representative points. This paper argues that the use of representative points provides a new perspective in high-dimensional goodness-of-fit testing, offering an alternative approach to evaluating spherical symmetry in such contexts. By leveraging the flexibility of choosing the number of representative points, this method ensures more reliable detection of departures from spherical symmetry, especially in high-dimensional datasets. Overall, this research highlights the practical advantages of the proposed approach in statistical analysis, emphasizing its potential as a powerful tool in goodness-of-fit tests within the realm of high-dimensional data.

Keywords: chisquare test; goodness of fit; representative points; spherical symmetry; Student’s t-distribution (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/24/3939/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/24/3939/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:24:p:3939-:d:1543885

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().