Characterization of multivariate distributions by means of univariate one
Lev B. Klebanov, Irina V. Volchenkova
TL;DR
This paper explores how multivariate distributions can be characterized using univariate variables, providing inequalities that aid in constructing multivariate two-sample tests.
Contribution
It introduces a method to identify multivariate distributions through specially constructed univariate variables and proposes inequalities useful for two-sample testing.
Findings
Derived inequalities for multivariate distribution characterization
Potential application in multivariate two-sample tests
Framework for constructing univariate-based multivariate tests
Abstract
The aim of this paper is to show a possibility to identify multivariate distribution by means of specially constructed one-dimensional random variable. We give some inequalities which may appear to helpful for a construction of multivariate two-sample tests. Key words: inequalities; multivariate distributions; two-sample tests
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStatistical Distribution Estimation and Applications · Advanced Statistical Methods and Models