  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
$\rho$, Weitzman's measure $\Delta$ and $\Lambda$ 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.
