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Statistical inference based on weighted divergence measures with simulations and applications

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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.

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Acknowledgements

The authors wish to express their appreciation to the anonymous referees for their valuable comments and recommendations. The first author acknowledges the Region of Normandy, France, for the valuable financial support during his PhD.

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Authors and Affiliations

  1. Laboratoire de Mathématiques Raphaël Salem, Université de Rouen-Normandie, Rouen, France

    Thomas Gkelsinis & Vlad Stefan Barbu

  2. Department of Statistics and Actuarial-Financial Mathematics, Lab of Statistics and Data Analysis, University of the Aegean, Karlovasi, Greece

    Alex Karagrigoriou

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  1. Thomas Gkelsinis
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  2. Alex Karagrigoriou
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  3. Vlad Stefan Barbu
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Correspondence to Vlad Stefan Barbu.

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Gkelsinis, T., Karagrigoriou, A. & Barbu, V.S. Statistical inference based on weighted divergence measures with simulations and applications. Stat Papers 63, 1511–1536 (2022). https://doi.org/10.1007/s00362-022-01286-z

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  • DOI: https://doi.org/10.1007/s00362-022-01286-z

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