8 edition of **Statistical Inference Based on Divergence Measures (Statistics: Textbooks and Monographs)** found in the catalog.

- 157 Want to read
- 9 Currently reading

Published
**October 10, 2005**
by Chapman & Hall/CRC
.

Written in English

- Probability & statistics,
- Multivariate analysis,
- Mathematics,
- Science/Mathematics,
- Probability & Statistics - General,
- Mathematics / Statistics,
- Divergent series,
- Entropy (Information theory)

The Physical Object | |
---|---|

Format | Hardcover |

Number of Pages | 512 |

ID Numbers | |

Open Library | OL8795495M |

ISBN 10 | 1584886005 |

ISBN 10 | 9781584886006 |

book. This book uses the basic structure of generic introduction to statistics course. However, in some ways I have chosen to diverge from the traditional approach. One divergence is the introduction of R as part of the learning process. Many have used statistical . A Generalized Divergence for Statistical Inference 5 the form PDλ(dn,fθ) = 1 λ(λ+1) ∑ dn [(dn fθ)λ −1]. (5) Notice that for values of λ = 1,0,−1/2 the Cressie-Read form in Equation (5) gen-erates the PCS, the LD and the HD respectively. The LD is actually the continuous limit of .

Emphasising on MCMC methods, this book explores simulation-based inference for spatial point processes. It examines the Cox and Markov point processes. It provides a treatment of MCMC techniques, particularly those related to Statistical inference follows. Summary The Likelihood plays a key role in both introducing general notions of statistical theory, and in developing specific methods. This book introduces likelihood-based statistical theory and related methods from a classical viewpoint, and demonstrates how the main body of currently used statistical techniques can be generated from a few key concepts, in particular the likelihood.

Statistical inference: the minimum distance approach Ayanendranath Basu, Hiroyuki Shioya, Chanseok divergence density equation data distribution based hellinger distance approach You can write a book review and share your experiences. Other readers will always be interested in your opinion of the. Pardo, L., Statistical Inference Based on Divergence Measures, Chapman & Hall/CRC Monographs on Statistics & Applied Probability. CRC Press, Taylor & .

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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 Cited by: 2. 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.

Statistical inference based on divergence measures. [Leandro Pardo] Asymptotic Distributions Maximum Entropy Principle and Statistical Inference on Condensed Ordered Data Exercises Answers to Exercises GOODNESS-OF-FIT: SIMPLE NULL HYPOTHESIS Introduction Phi-divergences and Goodness-of-fit with Fixed Number of Classes Phi-divergence Test.

Request PDF | On Jan 1,Leandro Pardo and others published Statistical Inference Based on Divergence Measures | Find, read and cite all the research you need on ResearchGateAuthor: Leandro Pardo.

Statistical Inference Based on Divergence Measures (Statistics: A Series of Textbooks and Monographs Book ) - Kindle edition by Leandro Pardo. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Statistical Inference Based on Divergence Measures (Statistics: A Series of Textbooks and Monographs.

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 pCited by: Statistical Inference Based on Divergence Measures Article in Journal of the Royal Statistical Society Series A (Statistics in Society) (3) February with 33 Reads.

Get this from a library. Statistical inference based on divergence measures. [Leandro Pardo Llorente] -- "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.

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.

Statistical Inference Based on Divergence Measures. DOI link for Statistical Inference Based on Divergence Measures. Statistical Inference Based on Divergence Measures book. By Leandro Pardo. Edition 1st Edition.

First Published eBook Published 12 November Pub. location New York. Imprint Chapman and Hall/: Leandro Pardo.

Introduction. Distance or divergence measures are of key importance in a number of theoretical and applied statistical inference and data processing problems, such as estimation, detection, classification, compression, and recognition, and more recently indexing and retrieval in databases, and model selection.

The literature on such types of issues is wide and has. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon “Statistical Inference Based on Divergence measures” New Developments in Statistical Information Theory Based on Entropy and Divergence Measures.

In statistics and information geometry, divergence or a contrast function is a function which establishes the "distance" of one probability distribution to the other on a statistical divergence is a weaker notion than that of the distance, in particular the divergence need not be symmetric (that is, in general the divergence from p to q is not equal to the divergence from q to p.

Divergence measures for statistical data processing Mich ele Basseville * [email protected] Abstract: This note provides a bibliography of investigations based on or related to divergence measures for theoretical and applied inference problems.

M-estimators offer simple robust alternatives to the maximum likelihood estimator. The density power divergence (DPD) and the logarithmic density power divergence (LDPD) measures provide two classes of robust M-estimators which contain the MLE as a special case.

In each of these families, the robustness of the estimator is achieved through a density power down-weighting of outlying. Find many great new & used options and get the best deals for Statistics a Series of Textbooks and Monographs: Statistical Inference Based on Divergence Measures Vol.

by Leandro Pardo (, Hardcover / Hardcover) at the best online prices at eBay. Free shipping for many products. On the Applications of Divergence Type Measures in Testing Statistical Hypotheses.

based on information measures, scattered through the literature. Our aim is to examine a wide range of divergence type measures and their applications to statistical inferences, with special emphasis on multinomial and multivariate normal distributions. Leandro Pardo is the author of Statistical Inference Based on Divergence Measures ( avg rating, 1 rating, 0 reviews, published ), Modern Mathemat 5/5(1).

Statistical inference is the process of using data analysis to deduce properties of an underlying distribution of probability.

Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving is assumed that the observed data set is sampled from a larger population. Inferential statistics can be contrasted with descriptive statistics.

Presents classical problems of Statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence with applications to multinomial and generation populations.

On the basis of divergence measures, this book introduces minimum divergence estimators as well as divergence test statistics. Statistical Inference: The Minimum Distance Approach gives a thorough account of density-based minimum distance methods and their use in statistical inference. It covers statistical distances, density-based minimum distance methods, discrete and continuous models, asymptotic distributions, robustness, computational issues, residual adjustment.

Statistical Inference Based on Divergence Measures Statistical Inference Based on Divergence Measures John Lu, Z. Q. L. Pardo, Boca Raton, Chapman and Hall–CRC pp., $ ISBN ‐1‐‐‐6 This is a title in the series of ‘Textbooks and monographs’, having flavours of both, in that it is highly focused on the development of a special .Webcat Plus: Statistical inference based on divergence measures, 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.