On Inference of Overlapping Coefficients in Two Inverse Lomax Populations
- https://doi.org/10.2991/jsta.d.210107.002How to use a DOI?
- β-Divergence, Kernel density Estimation, Bandwidth
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 , Weitzman's measure and 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.
- © 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 -