Ball: An R Package for Detecting Distribution Difference and Association in Metric Spaces
Dublin Core
Title
Ball: An R Package for Detecting Distribution Difference and Association in Metric Spaces
Subject
K-sample test problem, test of mutual independence problem, ball divergence, ball
covariance, metric space.
covariance, metric space.
Description
The rapid development of modern technology has created many complex datasets
in non-linear spaces, while most of the statistical hypothesis tests are only available in
Euclidean or Hilbert spaces. To properly analyze the data with more complicated structures, efforts have been made to solve the fundamental test problems in more general
spaces (Lyons 2013; Pan, Tian, Wang, and Zhang 2018; Pan, Wang, Zhang, Zhu, and Zhu
2020). In this paper, we introduce a publicly available R package Ball for the comparison
of multiple distributions and the test of mutual independence in metric spaces, which
extends the test procedures for the equality of two distributions (Pan et al. 2018) and
the independence of two random objects (Pan et al. 2020). The Ball package is computationally efficient since several novel algorithms as well as engineering techniques are
employed in speeding up the ball test procedures. Two real data analyses and diverse
numerical studies have been performed, and the results certify that the Ball package can
detect various distribution differences and complicated dependencies in complex datasets,
e.g., directional data and symmetric positive definite matrix data.
in non-linear spaces, while most of the statistical hypothesis tests are only available in
Euclidean or Hilbert spaces. To properly analyze the data with more complicated structures, efforts have been made to solve the fundamental test problems in more general
spaces (Lyons 2013; Pan, Tian, Wang, and Zhang 2018; Pan, Wang, Zhang, Zhu, and Zhu
2020). In this paper, we introduce a publicly available R package Ball for the comparison
of multiple distributions and the test of mutual independence in metric spaces, which
extends the test procedures for the equality of two distributions (Pan et al. 2018) and
the independence of two random objects (Pan et al. 2020). The Ball package is computationally efficient since several novel algorithms as well as engineering techniques are
employed in speeding up the ball test procedures. Two real data analyses and diverse
numerical studies have been performed, and the results certify that the Ball package can
detect various distribution differences and complicated dependencies in complex datasets,
e.g., directional data and symmetric positive definite matrix data.
Creator
Jin Zhu
Source
https://www.jstatsoft.org/article/view/v097i06
Publisher
Sun Yat-Sen
Universit
Universit
Date
Januari 2021
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Jin Zhu, “Ball: An R Package for Detecting Distribution Difference and Association in Metric Spaces,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8181.