‎Divergence measures are statistical tools designed to distinguish between the information provided by distribution functions of f(x) and g(x). The magnitude of divergence has been defined using a variety of methods such as Shannon entropy and other mathematical functions through a history of more than a century. In the present study, we have briefly explained the Lin–Wong divergence measure and compared it to other statistical information such as the Kullback-Leibler, Bhattacharyya and v2 divergence as well as Shannon entropy and Fisher information on Type I censored data. Besides, we obtain some inequalities for the Lin–Wong distance and the mentioned divergences on the Type I censored scheme. Finally, we identified a number of ordering properties for the Lin–Wong distance measure based on stochastic ordering, likelihood ratio ordering and hazard rate ordering techniques.

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