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Depth and outliers for samples of sets and random sets distributions
Author(s) -
Cascos Ignacio,
Li Qiyu,
Molchanov Ilya
Publication year - 2021
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/anzs.12326
Subject(s) - mathematics , outlier , sample (material) , sample space , regular polygon , convex set , set (abstract data type) , function (biology) , random variable , statistics , combinatorics , convex optimization , geometry , computer science , chemistry , chromatography , evolutionary biology , biology , programming language
Summary We suggest several constructions suitable to define the depth of set‐valued observations with respect to a sample of convex sets or with respect to the distribution of a random closed convex set. With the concept of a depth, it is possible to determine if a given convex set should be regarded an outlier with respect to a sample of convex closed sets. Some of our constructions are motivated by the known concepts of half‐space depth and band depth for function‐valued data. A novel construction derives the depth from a family of non‐linear expectations of random sets. Furthermore, we address the role of positions of sets for evaluation of their depth. Two case studies concern interval regression for Greek wine data and detection of outliers in a sample of particles.