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Overlapping coefficient is a direct measure of similarity between twodistributions which is recently becoming very useful. This paper investigatesestimation for some well-known measures of overlap, namely Matusita's measure$\rho$, Weitzman's measure $\Delta$ and $\Lambda$ based on Kullback-Leibler.Two estimation methods considered in this study are point estimation andBayesian approach. Two Inverse Lomax populations with different shapeparameters are considered. The bias and mean square error properties of theestimators are studied through a simulation study and a real data example.

On Inference of Overlapping Coefficients in Two Inverse Lomax Populations