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Robust Statistics - Theory and Methods (with R) Second Edition (Hardcover, 2nd Edition)
Loot Price: R2,196
Discovery Miles 21 960
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Robust Statistics - Theory and Methods (with R) Second Edition (Hardcover, 2nd Edition)
Expected to ship within 12 - 19 working days
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A new edition of this popular text on robust statistics, thoroughly
updated to include new and improved methods and focus on
implementation of methodology using the increasingly popular
open-source software R. Classical statistics fail to cope well with
outliers associated with deviations from standard distributions.
Robust statistical methods take into account these deviations when
estimating the parameters of parametric models, thus increasing the
reliability of fitted models and associated inference. This new,
second edition of Robust Statistics Theory and Methods (with R)
presents a broad coverage of the theory of robust statistics that
is integrated with computing methods and applications. Updated to
include important new research results of the last decade and focus
on the use of the popular software package R, it features in-depth
coverage of the key methodology, including regression, multivariate
analysis, and time series modeling. The book is illustrated
throughout by a range of examples and applications that are
supported by a companion website featuring data sets and R code
that allow the reader to reproduce the examples given in the book.
Unlike other books on the market, Robust Statistics Theory and
Methods (with R) offers the most comprehensive, definitive, and
up-to-date treatment of the subject. It features chapters on
estimating location and scale; measuring robustness; linear
regression with fixed and with random predictors; multivariate
analysis; generalized linear models; time series; numerical
algorithms; and asymptotic theory of M-estimates. Explains both the
use and theoretical justification of robust methods Guides readers
in selecting and using the most appropriate robust methods for
their problems Features computational algorithms for the core
methods Robust statistics research results of the last decade
included in this 2nd edition include: fast deterministic robust
regression, finite-sample robustness, robust regularized
regression, robust location and scatter estimation with missing
data, robust estimation with independent outliers in variables, and
robust mixed linear models. Robust Statistics aims to stimulate the
use of robust methods as a powerful tool to increase the
reliability and accuracy of statistical modelling and data
analysis. It is an ideal resource for researchers, practitioners,
and graduate students in statistics, engineering, computer science,
and physical and social sciences.
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