Extremiles: a new perspective on asymmetric least squares

Stupfler, Gilles, Daouia, Abdelaati and Gijbels, Irène (2019) Extremiles: a new perspective on asymmetric least squares. Journal of the American Statistical Association, 114 (527). pp. 1366-1381. ISSN 1537-274X

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Quantiles and expectiles of a distribution are found to be useful descriptors of its tail in the same way as the median and mean are related to its central behavior. This paper considers a valuable alternative class to expectiles, called extremiles, which parallels the class of quantiles and includes the family of expected minima and expected maxima. The new class is motivated via several angles, which reveals its specific merits and strengths. Extremiles suggest better capability of fitting both location and spread in data points and provide an appropriate theory that better displays the interesting features of long-tailed distributions. We discuss their estimation in the range of the data and beyond the sample maximum. A number of motivating examples are given to illustrate the utility of estimated extremiles in modeling noncentral behavior. There is in particular an interesting connection with coherent measures of risk protection.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/947216
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
Identification Number: https://doi.org/10.1080/01621459.2018.1498348
Depositing User: Eprints, Support
Date Deposited: 09 Jul 2018 12:01
Last Modified: 04 May 2020 19:46
URI: https://eprints.nottingham.ac.uk/id/eprint/52820

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