Outlier detection based on extreme value theory and applications

Autor:	Shrijita Bhattacharya, Francois Kamper, Jan Beirlant
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	estimator index Statistics and Probability tail majority vote plot max-domain of attraction local outlier factors extreme observations tail-adjusted boxplot Statistics Probability and Uncertainty
Zdroj:	Scandinavian Journal of Statistics
ISSN:	0303-6898 1467-9469
DOI:	10.3929/ethz-b-000615053
Popis:	Whether an extreme observation is an outlier or not depends strongly on the corresponding tail behavior of the underlying distribution. We develop an automatic, data-driven method rooted in the mathematical theory of extremes to identify observations that deviate from the intermediate and central characteristics. The proposed algorithm is an extension of a method previously proposed in the literature for the specific case of heavy tailed Pareto-type distributions to all max-domains of attraction. We propose some applications such as a tail-adjusted boxplot which yields a more accurate representation of possible outliers, and the identification of outliers in a multivariate context through an analysis of associated random variables such as local outlier factors. Several examples and simulation results illustrate the finite sample behavior of the algorithm and its applications. Scandinavian Journal of Statistics ISSN:0303-6898 ISSN:1467-9469
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59a13210d55a2c2999f314a8f0381a91 Zobrazit plný text záznamu Plný text