Journal of Statistical Theory and Applications

In Press, Corrected Proof, Available Online: 15 January 2021

On Inference of Overlapping Coefficients in Two Inverse Lomax Populations

Authors
Hamza Dhaker1, *, El Hadji Deme2, Salah El-Adlouni1
1 Département de mathématiques et statistique, Université de Moncton, NB, E1A 3E9, Canada
2 LERSTAD, UFR SAT, Universite Gaston Berger, Saint-Louis, Senegal
*Corresponding author. Email: hamza.dhaker@umoncton.ca
Corresponding Author
Hamza Dhaker
Received 7 October 2019, Accepted 15 October 2020, Available Online 15 January 2021.
DOI
https://doi.org/10.2991/jsta.d.210107.002How to use a DOI?
Keywords
β-Divergence, Kernel density Estimation, Bandwidth
Abstract

Overlapping coefficient is a direct measure of similarity between two distributions which is recently becoming very useful. This paper investigates estimation for some well-known measures of overlap, namely Matusita's measure ρ, Weitzman's measure Δ and Λ based on Kullback–Leibler. Two estimation methods considered in this study are point estimation and Bayesian approach. Two inverse Lomax populations with different shape parameters are considered. The bias and mean square error properties of the estimators are studied through a simulation study and a real data example.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Statistical Theory and Applications
Publication Date
2021/01
ISSN (Online)
2214-1766
ISSN (Print)
1538-7887
DOI
https://doi.org/10.2991/jsta.d.210107.002How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Hamza Dhaker
AU  - El Hadji Deme
AU  - Salah El-Adlouni
PY  - 2021
DA  - 2021/01
TI  - On Inference of Overlapping Coefficients in Two Inverse Lomax Populations
JO  - Journal of Statistical Theory and Applications
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.d.210107.002
DO  - https://doi.org/10.2991/jsta.d.210107.002
ID  - Dhaker2021
ER  -