New Developments in Statistical Information Theory Based on Entropy and Divergence Measures

• Main Author: Pardo, Leandro (auth)
• Format: Electronic Book Chapter
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2019
• Subjects: n/a; mixture index of fit; Kullback-Leibler distance; relative error estimation; minimum divergence inference; Neyman Pearson test; influence function; consistency; thematic quality assessment; asymptotic normality; Hellinger distance; nonparametric test; Berstein von Mises theorem; maximum composite likelihood estimator; 2-alternating capacities; efficiency; corrupted data; statistical distance; robustness; log-linear models; representation formula; goodness-of-fit; general linear model; Wald-type test statistics; Hölder divergence; divergence; logarithmic super divergence; information geometry; sparse; robust estimation; relative entropy; minimum disparity methods; MM algorithm; local-polynomial regression; association models; total variation; Bayesian nonparametric; ordinal classification variables; Wald test statistic; Wald-type test; composite hypotheses; compressed data; hypothesis testing; Bayesian semi-parametric; single index model; indoor localization; composite minimum density power divergence estimator; quasi-likelihood; Chernoff Stein lemma; composite likelihood; asymptotic property; Bregman divergence; robust testing; misspecified hypothesis and alternative; least-favorable hypotheses; location-scale family; correlation models; minimum penalized ?-divergence estimator; non-quadratic distance; robust; semiparametric model; divergence based testing; measurement errors; bootstrap distribution estimator; generalized renyi entropy; minimum divergence methods; generalized linear model; ?-divergence; Bregman information; iterated limits; centroid; model assessment; divergence measure; model check; two-sample test; Wald statistic
• Online Access: DOAB: download the publication; DOAB: description of the publication

This book presents new and original research in Statistical Information Theory, based on minimum divergence estimators and test statistics, from a theoretical and applied point of view, for different statistical problems with special emphasis on efficiency and robustness. Divergence statistics, base...