Non-Parametric Inference for Gini Covariance and its Variants

We obtain simple non-parametric estimators of Gini-based covariance, correlation and regression coefficients. We then establish the consistency and asymptotic normality of the proposed estimators. We provide an explicit formula for finding the asymptotic variance of the estimators. We also discuss j...

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Bibliographic Details
Published in:Sankhya. Series. A 2022-08, Vol.84 (2), p.790-807
Main Authors: Kattumannil, Sudheesh K., Sreelakshmi, N., Balakrishnan, N.
Format: Article
Language:English
Subjects:
Mathematics and Statistics
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
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Summary:We obtain simple non-parametric estimators of Gini-based covariance, correlation and regression coefficients. We then establish the consistency and asymptotic normality of the proposed estimators. We provide an explicit formula for finding the asymptotic variance of the estimators. We also discuss jackknifed versions of the proposed estimators for reducing the bias of the estimators in case of small sample sizes. Finally, we evaluate the finite-sample performance of these estimators through on Monte Carlo simulations from a bivariate Pareto distribution.
ISSN:0976-836X
0976-8378
DOI:10.1007/s13171-020-00218-z