Non-Parametric Inference for Gini Covariance and its Variants

Sudheesh K. Kattumannil (), N. Sreelakshmi and N. Balakrishnan
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Sudheesh K. Kattumannil: Indian Statistical Institute
N. Sreelakshmi: Indian Institute of Technology
N. Balakrishnan: McMaster University

Sankhya A: The Indian Journal of Statistics, 2022, vol. 84, issue 2, No 15, 790-807

Abstract: Abstract 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.

Keywords: Gini mean difference; Gini covariance; Gini correlation; Gini index; Gini regression coefficient.; Primary: 62G05; Secondary: 62G20 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13171-020-00218-z

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