Explicit bivariate simplicial depth

IF 1.4 3区 数学 Q2 STATISTICS & PROBABILITY Journal of Multivariate Analysis Pub Date : 2024-09-30 DOI:10.1016/j.jmva.2024.105375
Erik Mendroš, Stanislav Nagy
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Abstract

The simplicial depth (SD) is a celebrated tool defining elements of nonparametric and robust statistics for multivariate data. While many properties of SD are well-established, and its applications are abundant, explicit expressions for SD are known only for a handful of the simplest multivariate probability distributions. This paper deals with SD in the plane. It (i) develops a one-dimensional integral formula for SD of any properly continuous probability distribution, (ii) gives exact explicit expressions for SD of uniform distributions on (both convex and non-convex) polygons in the plane or on the boundaries of such polygons, and (iii) discusses several implications of these findings to probability and statistics: (a) An upper bound on the maximum SD in the plane, (b) an implication for a test of symmetry of a bivariate distribution, and (c) a connection of SD with the classical Sylvester problem from geometric probability.
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显式二维简单深度
简单深度(SD)是一种著名的工具,它定义了多元数据的非参数和稳健统计要素。虽然简约深度的许多特性已得到证实,其应用也非常广泛,但简约深度的明确表达式只适用于少数最简单的多元概率分布。本文讨论平面中的自变量。本文(i) 建立了任何适当连续概率分布的 SD 的一维积分公式,(ii) 给出了平面内(凸和非凸)多边形上或这些多边形边界上均匀分布的 SD 的精确明确表达式,(iii) 讨论了这些发现对概率论和统计学的若干影响:(a) 平面上最大 SD 的上限,(b) 双变量分布对称性检验的含义,以及 (c) SD 与几何概率中经典的西尔维斯特问题的联系。
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来源期刊
Journal of Multivariate Analysis
Journal of Multivariate Analysis 数学-统计学与概率论
CiteScore
2.40
自引率
25.00%
发文量
108
审稿时长
74 days
期刊介绍: Founded in 1971, the Journal of Multivariate Analysis (JMVA) is the central venue for the publication of new, relevant methodology and particularly innovative applications pertaining to the analysis and interpretation of multidimensional data. The journal welcomes contributions to all aspects of multivariate data analysis and modeling, including cluster analysis, discriminant analysis, factor analysis, and multidimensional continuous or discrete distribution theory. Topics of current interest include, but are not limited to, inferential aspects of Copula modeling Functional data analysis Graphical modeling High-dimensional data analysis Image analysis Multivariate extreme-value theory Sparse modeling Spatial statistics.
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