Statistical inference on kurtosis of elliptical distributions

Multivariate elliptically-contoured distributions are widely used for modelling correlated and non-Gaussian data. In this work, we study the kurtosis of the elliptical model, which is an important parameter in many statistical analysis. Based on U-statistics, we develop an estimation method. Theoretically, we show that the proposed estimator is consistent under regular conditions, especially we relax a moment condition and the restriction that the data dimension and the sample size are of the same order. Furthermore, we derive the asymptotic normality of the estimator and evaluate the asymptotic variance through several examples, which allows us to construct a confidence interval. The performance of our method is validated by extensive simulations and real data analysis.

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Publication Details

Journal
Journal of nonparametric statistics
Published
2026-08-28
DOI
https://doi.org/10.1080/10485252.2026.2724822
Primary Topic
Bayesian Methods and Mixture Models
Type
article
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article

Statistical inference on kurtosis of elliptical distributions

Cheng Wang, Peirong Xu, Bowen Zhou
Journal of nonparametric statistics
Bayesian Methods and Mixture Models
article

Statistical inference on kurtosis of elliptical distributions

Cheng Wang, Peirong Xu, Bowen Zhou
article en

Abstract

Multivariate elliptically-contoured distributions are widely used for modelling correlated and non-Gaussian data. In this work, we study the kurtosis of the elliptical model, which is an important parameter in many statistical analysis. Based on U-statistics, we develop an estimation method. Theoretically, we show that the proposed estimator is consistent under regular conditions, especially we relax a moment condition and the restriction that the data dimension and the sample size are of the same order. Furthermore, we derive the asymptotic normality of the estimator and evaluate the asymptotic variance through several examples, which allows us to construct a confidence interval. The performance of our method is validated by extensive simulations and real data analysis.

Journal of nonparametric statistics
Shanghai Jiao Tong University (CN)
Openalex Percentile: Top 99%
Bayesian Methods and Mixture Models
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