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 |
Pardo Leandro
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Lo trovi qui: Univ. Federico II | ||
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Robust Procedures for Estimating and Testing in the Framework of Divergence Measures
| Robust Procedures for Estimating and Testing in the Framework of Divergence Measures |
| Autore | Pardo Leandro |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (333 p.) |
| Soggetto topico | Research & information: general |
| Soggetto non controllato |
Bayes error rate
Bayesian decision making Bhattacharyya coefficient/distance bias and variance trade-off classification composite likelihood composite minimum density power divergence estimators concentration bounds contingency tables convergence rates COVID-19 pandemic CUSUM monitoring density power divergence disparity divergence measures epidemiology estimation of α Friedman-Rafsky test statistic Galton-Watson branching processes with immigration GLM model Hellinger distance Hellinger integrals Henze-Penrose divergence INARCH(1) model INGARCH model integer-valued time series Kullback-Leibler information distance/divergence large deviations MDPDE minimal spanning trees minimum density power divergence estimator minimum pseudodistance estimation mixed-scale data model selection monitoring n/a numerical minimization one-parameter exponential family pearson residuals power divergences rare event probabilities relative entropy Renyi divergences residual adjustment function robust change point test robustness Robustness S-estimation SPC statistical distances time series of counts Tukey's biweight |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557680103321 |
Pardo Leandro
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Lo trovi qui: Univ. Federico II | ||
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