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Robust Statistics (Wiley Series in Probability and Statistics) by Peter J. Huber

by Peter J. Huber | HC | VeryGood
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Hinweise des Verkäufers
“May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend ...
Binding
Hardcover
Book Title
Robust Statistics (Wiley Series in Probability and Statistics)
Weight
1 lbs
Product Group
Book
IsTextBook
Yes
ISBN
9780471418054
Kategorie

Über dieses Produkt

Product Identifiers

Publisher
Wiley & Sons, Incorporated, John
ISBN-10
0471418056
ISBN-13
9780471418054
eBay Product ID (ePID)
1074859

Product Key Features

Number of Pages
320 Pages
Publication Name
Robust Statistics
Language
English
Subject
Probability & Statistics / General
Publication Year
1981
Type
Textbook
Author
Peter J. Huber
Subject Area
Mathematics
Series
Wiley Series in Probability and Statistics Ser.
Format
Hardcover

Dimensions

Item Height
0.8 in
Item Weight
18.5 Oz
Item Length
9.2 in
Item Width
6.2 in

Additional Product Features

Edition Number
1
Intended Audience
Scholarly & Professional
LCCN
80-018627
Dewey Edition
22
Series Volume Number
56
Illustrated
Yes
Dewey Decimal
519.52
Table Of Content
1. Generalities.2. The Weak Topology and Its Metrization.3. The Basic Types of Estimates.4. Asymptotic Minimax Theory for Estimating a Location Parameter.5. Scale Estimates.6. Multiparameter Problems, In Particular Joint Estimation of Location and Scale.7. Regression.8. Robust Covariance and Correlation Matrices.9. Rubustness of Design.10. Exact Finite Sample Results.11. Miscellaneous Topics. References. Index.
Synopsis
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 techniques-especially the method of sieves-have been constructed to enable inferences to be made in abstract parameter spaces. This work began with the author's 1950 monograph on inference in stochastic processes (for general sample space) and with the sieve methodology (for general parameter space) that the author and his co-workers at Brown University developed in the 1970s. Both of these cases are studied in this volume, which is the first comprehensive treatment of the subject. 1980 Order Statistics, 2nd Ed. Herbert A. David Presents up-to-date coverage of the theory and applications of ordered random variables and their functions. Develops the distribution theory of order statistics systematically, and treats short-cut methods, robust estimation, life testing, reliability, and extreme-value theory. Applications include procedures for the treatment of outliers and other data analysis techniques. Provides extensive references to the literature and the tables contained therein. Exercises included. 1980 Theory and Applications of Stochastic Differential Equations Zeev Schuss Presents SDE theory through its applications in the physical sciences, e.g., the description of chemical reactions, solid state diffusion and electrical conductivity, population genetics, filtering problems in communication theory, etc. Introduces the stochastic calculus in a simple way, presupposing only basic analysis and probability theory. Shows the role of first passage times and exit distributions in modeling physical phenomena. Introduces analytical methods in order to obtain information on probabilistic quantities such as moments and distribution of first passage times and transition probabilities, and demonstrates the role of partial differential equations. Methods include singular perturbation techniques, old and new asymptotic methods, and boundary layer theory. 1980, The first systematic, booklength 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., 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.
LC Classification Number
QA276

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