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Divergence Measures : Mathematical Foundations and Applications in Information-Theoretic and Statistical Problems
| Divergence Measures : Mathematical Foundations and Applications in Information-Theoretic and Statistical Problems |
| Autore | Sason Igal |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (256 p.) |
| Soggetto topico |
Mathematics & science
Research & information: general |
| Soggetto non controllato |
Augustin-Csiszár mutual information
Bahadur efficiency Bayes risk bootstrap Bregman divergence capacitory discrimination Carlson-Levin inequality chi-squared divergence conditional limit theorem conditional Rényi divergence convexity data transmission difference of convex (DC) programming dimensionality reduction discriminant analysis error exponents f-divergence f-divergences horse betting hypothesis testing information contraction information geometry information inequalities information measures Jensen diversity Jensen-Bregman divergence Jensen-Shannon centroid Jensen-Shannon divergence Kelly gambling large deviations Markov chains maximal correlation maximum likelihood method of types minimum divergence estimator mixture family mutual information n/a Pinsker's inequality relative entropy Rényi divergence Rényi entropy Rényi mutual information skew-divergence statistical divergences statistical inference strong data-processing inequalities total variation α-mutual information |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Divergence Measures |
| Record Nr. | UNINA-9910576871103321 |
Sason Igal
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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New Developments in Statistical Information Theory Based on Entropy and Divergence Measures / Leandro Pardo
| New Developments in Statistical Information Theory Based on Entropy and Divergence Measures / Leandro Pardo |
| Autore | Pardo Leandro |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (344 p.) |
| Soggetto non controllato |
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 |
| ISBN |
9783038979371
3038979376 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910346856403321 |
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Pardo Leandro
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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