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

1. Verfasser:	Pardo, Leandro , [VerfasserIn]
Umfang/Format:	1 online resource (344 pages).
ISBN:	books978-3-03897-937-1 9783038979364 9783038979371
Schlagworte:	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-Zugang:	DOAB: download the publication DOAB: description of the publication

Zugang:	Open Access
Benutzungs- und Reproduktionsbedingungen:	Creative Commons
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