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

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...

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Bibliographic Details
Main Author: Pardo, Leandro
Format: eBook
Language:English
Published: MDPI - Multidisciplinary Digital Publishing Institute 2019
Subjects:
Single Index Model
Bregman Information
Bootstrap Distribution Estimator
Divergence Based Testing
Efficiency
Composite Minimum Density Power Divergence Estimator
Bayesian Nonparametric
Mm Algorithm
Consistency
Bayesian Semi-parametric
Log-linear Models
Composite Likelihood
Robust Estimation
Mixture Index Of Fit
N/a
2-alternating Capacities
Thematic Quality Assessment
Asymptotic Property
?-divergence
Ordinal Classification Variables
Sparse
Hölder Divergence
Wald Statistic
Centroid
Association Models
General Linear Model
Generalized Linear Model
Location-scale Family
Hellinger Distance
Model Check
Compressed Data
Correlation Models
Relative Entropy
Statistical Distance
Representation Formula
Relative Error Estimation
Corrupted Data
Semiparametric Model
Minimum Disparity Methods
Iterated Limits
Two-sample Test
Goodness-of-fit
Total Variation
Divergence
Maximum Composite Likelihood Estimator
Model Assessment
Indoor Localization
Generalized Renyi Entropy
Least-favorable Hypotheses
Nonparametric Test
Divergence Measure
Non-quadratic Distance
Bregman Divergence
Robust
Minimum Divergence Methods
Wald-type Test
Wald-type Test Statistics
Logarithmic Super Divergence
Measurement Errors
Misspecified Hypothesis And Alternative
Information Geometry
Local-polynomial Regression
Robustness
Asymptotic Normality
Berstein Von Mises Theorem
Influence Function
Robust Testing
Minimum Penalized ?-divergence Estimator
Chernoff Stein Lemma
Quasi-likelihood
Wald Test Statistic
Hypothesis Testing
Kullback-leibler Distance
Composite Hypotheses
Neyman Pearson Test
Minimum Divergence Inference
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary: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, based on maximum likelihood estimators, as well as Wald's statistics, likelihood ratio statistics and Rao's score statistics, share several optimum asymptotic properties, but are highly non-robust in cases of model misspecification under the presence of outlying observations. It is well-known that a small deviation from the underlying assumptions on the model can have drastic effect on the performance of these classical tests. Specifically, this book presents a robust version of the classical Wald statistical test, for testing simple and composite null hypotheses for general parametric models, based on minimum divergence estimators.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
Physical Description:1 electronic resource (344 p.)
ISBN:9783038979364
books978-3-03897-937-1
9783038979371

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