Exact and approximate computation of the scatter halfspace depth

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY Computational Statistics Pub Date : 2024-05-09 DOI:10.1007/s00180-024-01500-6
Xiaohui Liu, Yuzi Liu, Petra Laketa, Stanislav Nagy, Yuting Chen
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Abstract

The scatter halfspace depth (sHD) is an extension of the location halfspace (also called Tukey) depth that is applicable in the nonparametric analysis of scatter. Using sHD, it is possible to define minimax optimal robust scatter estimators for multivariate data. The problem of exact computation of sHD for data of dimension \(d \ge 2\) has, however, not been addressed in the literature. We develop an exact algorithm for the computation of sHD in any dimension d and implement it efficiently for any dimension \(d \ge 1\). Since the exact computation of sHD is slow especially for higher dimensions, we also propose two fast approximate algorithms. All our programs are freely available in the R package scatterdepth.

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散射半空间深度的精确和近似计算
散点半空间深度(sHD)是位置半空间深度(也称为 Tukey)的扩展,适用于散点的非参数分析。利用 sHD,可以定义多元数据的最小最优稳健散点估计值。然而,对于维数为 \(d \ge 2\) 的数据,sHD 的精确计算问题在文献中还没有得到解决。我们开发了一种在任意维度 d 下计算 sHD 的精确算法,并在任意维度 (d\ge 1\ )下有效地实现了这一算法。由于sHD的精确计算速度较慢,尤其是在高维情况下,因此我们还提出了两种快速近似算法。我们的所有程序都可以在R软件包scatterdepth中免费获取。
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来源期刊
Computational Statistics
Computational Statistics 数学-统计学与概率论
CiteScore
2.90
自引率
0.00%
发文量
122
审稿时长
>12 weeks
期刊介绍: Computational Statistics (CompStat) is an international journal which promotes the publication of applications and methodological research in the field of Computational Statistics. The focus of papers in CompStat is on the contribution to and influence of computing on statistics and vice versa. The journal provides a forum for computer scientists, mathematicians, and statisticians in a variety of fields of statistics such as biometrics, econometrics, data analysis, graphics, simulation, algorithms, knowledge based systems, and Bayesian computing. CompStat publishes hardware, software plus package reports.
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