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Journal Articles Journal of Statistical Planning and Inference Year : 2023

Multivariate expectile-based distribution: properties, Bayesian inference, and applications

Abstract

Expectiles form a family of risk measures that have recently gained interest over the more common value-at-risk or return levels, primarily due to their capability to be determined by the probabilities of tail values and magnitudes of realisations at once. However, a prevalent and ongoing challenge of expectile inference is the problem of uncertainty quantification, which is especially critical in sensitive applications, such as in medical, environmental or engineering tasks. We address this issue by developing a novel distribution, termed the multivariate expectilebased distribution (MED), that possesses an expectile as a closed-form parameter. Desirable properties of the distribution, such as log-concavity, make it an excellent fitting distribution in multivariate applications. Maximum likelihood estimation and Bayesian inference algorithms are described. Simulated examples and applications to expectile and mode estimation illustrate the usefulness of the MED for uncertainty quantification.
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Dates and versions

hal-03428827 , version 1 (15-11-2021)
hal-03428827 , version 2 (02-12-2022)

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Julyan Arbel, Stéphane Girard, Hien Duy Nguyen, Antoine Usseglio-Carleve. Multivariate expectile-based distribution: properties, Bayesian inference, and applications. Journal of Statistical Planning and Inference, 2023, 225, pp.146-170. ⟨10.1016/j.jspi.2022.12.001⟩. ⟨hal-03428827v2⟩
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