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eBook Robust Statistics (Wiley Series in Probability and Statistics) download

by Peter J. Huber

eBook Robust Statistics (Wiley Series in Probability and Statistics) download ISBN: 0471650722
Author: Peter J. Huber
Publisher: Wiley-Interscience; 1 edition (December 23, 2003)
Language: English
Pages: 328
ePub: 1474 kb
Fb2: 1896 kb
Rating: 4.6
Other formats: mobi lrf lrf doc
Category: Math Sciences
Subcategory: Mathematics

Wiley series in probability and mathematical statistics) A Wiley-Interscience publication.

Professor of Statistics Harvard University Cambridge, Massachusetts. John Wiley & Sons. Wiley series in probability and mathematical statistics) A Wiley-Interscience publication. 1. Robust statistics. A test is called distribution-free if the probability of falsely rejecting the null hypothesis is the same for all possible underlying continuous distribu-tions (optimal robustness of validity). The typical examples are the two-sample rank tests for testing equality between distributions. Most distribution-free tests happen to have a reasonably stable power and thus also a good robustness of total performance.

Robust Correlation: Theory and Applications (Wiley Series in Probability and Statistics). In the 1970s Peter Huber was one of the innovative geniuses that developed the area of robust statistical methods. Georgy L. Shevlyakov. After the famous Princeton robustness study that Huber participated in there was a scattered set of techniques that were shown to be robust estimators of location based on simulations over wide classes of probability distributions. Huber and Hampel were the leaders at putting together some mathematical theory for robustness.

Other volumes in the Wiley Series in Probability and Mathematical Statistics Abstract Inference UIf Grenander The traditional setting of statistical inference is when both sample space and parameter space are finite dimensional Euclidean spaces or subjects of such spaces. During the last decades, however, a theory has been developed that allows the sample space to be some abstract space. More recently, mathematical y the method of sieves-have been constructed to enable inferences to be made in abstract parameter spaces.

Features a new section on time series in connection with a more expanded treatment of pseudo-values.

Peter J. Huber, PhD, has over thirty-five years of academic experience and has previously served as professor of statistics at ETH Zurich (Switzerland), Harvard University, Massachusetts Institute of Technology, and the University of Bayreuth (Germany). Features a new section on time series in connection with a more expanded treatment of pseudo-values.

The first systematic, book-length treatment of the subject. Robust Statistics Wiley Series in Probability and Statistics (Том 579). Begins with a general introduction and the formal mathematical background behind qualitative and quantitative robustness. Provides selected numerical algorithms for computing robust estimates, as well as convergence proofs. Peter J. Huber was formerly a Professor of Statistics at Harvard University and ETH Zurich. Dr. Huber received his P. in Mathematics from ETH Zurich in 1961. Библиографические данные. John Wiley & Sons, 2005.

in probability and statistics for students in engineering and applied sciences. to this day, An Introduction to Probability and Statistics is now revised to incorporate new information. Introduction to Probability and Statistics for Engineers and Scientists. 29 MB·23,062 Downloads. Probability and statistics. FOR ENGINEERS AND SCIENTISTS. probability and statistics textbook. 73 MB·19,784 Downloads. Introduction to the Normal Curve. Robust Statistics Wiley Series in Probability and Statistics – Svazek 579.

John Wiley & Sons, 4. 2. 2005 - Počet stran: 320. 0 Recenze. The first systematic, book-length treatment of the subject. Autor. 0471725242, 9780471725244.

Robust Statistics book. Robust Statistics (Wiley Series in Probability and Statistics). 0471650722 (ISBN13: 9780471650720).

Wiley Series in Probability and Statistics (Book 693)

Wiley Series in Probability and Statistics (Book 693).

The first systematic, book-length treatment of the subject. Begins with a general introduction and the formal mathematical background behind qualitative and quantitative robustness. Stresses concepts. Provides selected numerical algorithms for computing robust estimates, as well as convergence proofs. Tables contain quantitative robustness information for a variety of estimates.
Comments: (3)
HappyLove
Professor Huber has established himself as one of the titans of robust statistics, with numerous texts and monographs on the subject spanning multiple decades. With this 2nd edition of his comprehensive look at what it means for a statistic to be robust, Prof. Huber helps the mathematical statistician dive deeply into formal algebraic descriptions of robustness that are broadly applicable.

A small word of caution: This text assumes at least one-semester graduate-level introductions to real analysis and topology; otherwise, many of the discussions even in Chapter 1 will be mystifying. But if a little bit of weak(-star) topology talk doesn't faze you, then you're in for a treat!
Mr_Mix
In the 1970s Peter Huber was one of the innovative geniuses that developed the area of robust statistical methods. After the famous Princeton robustness study that Huber participated in there was a scattered set of techniques that were shown to be robust estimators of location based on simulations over wide classes of probability distributions. Huber and Hampel were the leaders at putting together some mathematical theory for robustness.

This book was the first attempt to unify the mathematical ideas into a general theory. It is intended for research statisticians and is a masterpiece for the subject. There are now other good books of a more practical nature. Huber also wrote a nice monograph in the SIAM series around the same time. It is now 20 years since the publication of the book and it perhaps deserves to be recognized by republication as a Wiley Classic.
Meztihn
If you need clear explanations about robust statistics, if you need ideas to perform robust regression, or if you need some ground to develop robust algorithms, all you need is this text, and only this text. It covers theoretical as well as practical aspects of robust statistics. If you need more modern theoretical materials on robust statistics, Rieder's Asymptotic Robust Statistics is the companion text.