Statistical inference based on weighted divergence measures with simulations and applications

Thomas Gkelsinis (), Alex Karagrigoriou () and Vlad Stefan Barbu ()
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Thomas Gkelsinis: Université de Rouen-Normandie
Alex Karagrigoriou: University of the Aegean
Vlad Stefan Barbu: Université de Rouen-Normandie

Statistical Papers, 2022, vol. 63, issue 5, No 6, 1536 pages

Abstract: Abstract In this paper we propose the Directed Goodness of Fit (DGoF) test and the Directed test of Homogeneity (DHom). These types of tests are constructed based on a particular type of discrepancy measures called weighted (or directed) divergences. These measures allow the researcher to focus on specific subsets of the support without, at the same time, losing the information of the others. The performance of the proposed tests examined for a variety of distributions via extensive Monte Carlo simulations. Also, comparisons with the most known tests in the literature are placed to validate the usefulness of the proposed results. Finally, we achieve significantly more powerful tests as compared to the classical ones with comparable error rates.

Keywords: Directed information; Goodness of fit test; Homogeneity test; Information measures; Divergence; Weighted divergence; 62G10; 62H15; 62P20; 62E20 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00362-022-01286-z

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