New Developments In Statistical Information Theory Based On Entropy And Divergence Measures PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download New Developments In Statistical Information Theory Based On Entropy And Divergence Measures PDF full book. Access full book title New Developments In Statistical Information Theory Based On Entropy And Divergence Measures.
Author | : Leandro Pardo |
Publisher | : MDPI |
Total Pages | : 344 |
Release | : 2019-05-20 |
Genre | : Social Science |
ISBN | : 3038979368 |
Download New Developments in Statistical Information Theory Based on Entropy and Divergence Measures Book in PDF, ePub and Kindle
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.
Author | : Leandro Pardo |
Publisher | : |
Total Pages | : 344 |
Release | : 2019 |
Genre | : Social sciences (General) |
ISBN | : 9783038979371 |
Download New Developments in Statistical Information Theory Based on Entropy and Divergence Measures Book in PDF, ePub and Kindle
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.
Author | : Leandro Pardo |
Publisher | : CRC Press |
Total Pages | : 513 |
Release | : 2018-11-12 |
Genre | : Mathematics |
ISBN | : 1420034812 |
Download Statistical Inference Based on Divergence Measures Book in PDF, ePub and Kindle
The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this p
Author | : Andres M. Kowalski, Raul D. Rossignoli and Evaldo M. F. Curado |
Publisher | : Bentham Science Publishers |
Total Pages | : 432 |
Release | : 2013-12-13 |
Genre | : Science |
ISBN | : 1608057607 |
Download Concepts and Recent Advances in Generalized Information Measures and Statistics Book in PDF, ePub and Kindle
Since the introduction of the information measure widely known as Shannon entropy, quantifiers based on information theory and concepts such as entropic forms and statistical complexities have proven to be useful in diverse scientific research fields. This book contains introductory tutorials suitable for the general reader, together with chapters dedicated to the basic concepts of the most frequently employed information measures or quantifiers and their recent applications to different areas, including physics, biology, medicine, economics, communication and social sciences. As these quantifiers are powerful tools for the study of general time and data series independently of their sources, this book will be useful to all those doing research connected with information analysis. The tutorials in this volume are written at a broadly accessible level and readers will have the opportunity to acquire the knowledge necessary to use the information theory tools in their field of interest.
Author | : Robert M. Gray |
Publisher | : Springer Science & Business Media |
Total Pages | : 346 |
Release | : 2013-03-14 |
Genre | : Computers |
ISBN | : 1475739826 |
Download Entropy and Information Theory Book in PDF, ePub and Kindle
This book is devoted to the theory of probabilistic information measures and their application to coding theorems for information sources and noisy channels. The eventual goal is a general development of Shannon's mathematical theory of communication, but much of the space is devoted to the tools and methods required to prove the Shannon coding theorems. These tools form an area common to ergodic theory and information theory and comprise several quantitative notions of the information in random variables, random processes, and dynamical systems. Examples are entropy, mutual information, conditional entropy, conditional information, and discrimination or relative entropy, along with the limiting normalized versions of these quantities such as entropy rate and information rate. Much of the book is concerned with their properties, especially the long term asymptotic behavior of sample information and expected information. This is the only up-to-date treatment of traditional information theory emphasizing ergodic theory.
Author | : Leandro Pardo |
Publisher | : Chapman and Hall/CRC |
Total Pages | : 512 |
Release | : 2005-10-10 |
Genre | : Mathematics |
ISBN | : 9781584886006 |
Download Statistical Inference Based on Divergence Measures Book in PDF, ePub and Kindle
The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this powerful approach. Statistical Inference Based on Divergence Measures explores classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence. The first two chapters form an overview, from a statistical perspective, of the most important measures of entropy and divergence and study their properties. The author then examines the statistical analysis of discrete multivariate data with emphasis is on problems in contingency tables and loglinear models using phi-divergence test statistics as well as minimum phi-divergence estimators. The final chapter looks at testing in general populations, presenting the interesting possibility of introducing alternative test statistics to classical ones like Wald, Rao, and likelihood ratio. Each chapter concludes with exercises that clarify the theoretical results and present additional results that complement the main discussions. Clear, comprehensive, and logically developed, this book offers a unique opportunity to gain not only a new perspective on some standard statistics problems, but the tools to put it into practice.
Author | : Solomon Kullback |
Publisher | : Courier Corporation |
Total Pages | : 436 |
Release | : 2012-09-11 |
Genre | : Mathematics |
ISBN | : 0486142043 |
Download Information Theory and Statistics Book in PDF, ePub and Kindle
Highly useful text studies logarithmic measures of information and their application to testing statistical hypotheses. Includes numerous worked examples and problems. References. Glossary. Appendix. 1968 2nd, revised edition.
Author | : Igal Sason |
Publisher | : Mdpi AG |
Total Pages | : 256 |
Release | : 2022-06 |
Genre | : |
ISBN | : 9783036543321 |
Download Divergence Measures Book in PDF, ePub and Kindle
Data science, information theory, probability theory, statistical learning and other related disciplines greatly benefit from non-negative measures of dissimilarity between pairs of probability measures. These are known as divergence measures, and exploring their mathematical foundations and diverse applications is of significant interest. The present Special Issue, entitled "Divergence Measures: Mathematical Foundations and Applications in Information-Theoretic and Statistical Problems", includes eight original contributions, and it is focused on the study of the mathematical properties and applications of classical and generalized divergence measures from an information-theoretic perspective. It mainly deals with two key generalizations of the relative entropy: namely, the Rényi divergence and the important class of f -divergences. It is our hope that the readers will find interest in this Special Issue, which will stimulate further research in the study of the mathematical foundations and applications of divergence measures.
Author | : |
Publisher | : Academic Press |
Total Pages | : 313 |
Release | : 1995-03-27 |
Genre | : Technology & Engineering |
ISBN | : 0080577571 |
Download Advances in Imaging and Electron Physics Book in PDF, ePub and Kindle
Advances in Imaging and Electron Physics
Author | : Chi-hau Chen |
Publisher | : World Scientific |
Total Pages | : 582 |
Release | : 2015-12-15 |
Genre | : Computers |
ISBN | : 9814656534 |
Download Handbook of Pattern Recognition and Computer Vision (5th Edition) Book in PDF, ePub and Kindle
The book provides an up-to-date and authoritative treatment of pattern recognition and computer vision, with chapters written by leaders in the field. On the basic methods in pattern recognition and computer vision, topics range from statistical pattern recognition to array grammars to projective geometry to skeletonization, and shape and texture measures. Recognition applications include character recognition and document analysis, detection of digital mammograms, remote sensing image fusion, and analysis of functional magnetic resonance imaging data, etc.