Lin–Wong divergence and relations on type I censored data

Abstract

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