The belief of utilizing functionals of data idea, corresponding to entropies or divergences, in statistical inference isn't really new. even if, although divergence statistics became a superb substitute to the classical probability ratio attempt and the Pearson-type statistic in discrete types, many statisticians stay blind to this robust approach.

Statistical Inference in keeping with Divergence Measures explores classical difficulties of statistical inference, corresponding to estimation and speculation checking out, at the foundation of measures of entropy and divergence. the 1st chapters shape an outline, from a statistical standpoint, of crucial measures of entropy and divergence and examine their houses. the writer then examines the statistical research of discrete multivariate information with emphasis is on difficulties in contingency tables and loglinear types utilizing phi-divergence attempt data in addition to minimal phi-divergence estimators. the ultimate bankruptcy appears at checking out in most cases populations, providing the attention-grabbing risk of introducing replacement try out information to classical ones like Wald, Rao, and chance ratio. every one bankruptcy concludes with routines that make clear the theoretical effects and current extra effects that supplement the most discussions.

Clear, entire, and logically built, this ebook bargains a different chance to achieve not just a brand new viewpoint on a few ordinary information difficulties, however the instruments to place it into perform.

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