Robust statistics : theory and methods (with R)

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Tác giả: Ricardo A Maronna

Ngôn ngữ: eng

ISBN-13: 978-1119214656

Ký hiệu phân loại: 519.5 Statistical mathematics

Thông tin xuất bản: Hoboken, New Jersey : WIley, 2019.

Mô tả vật lý: 1 PDF (464 pages).

Bộ sưu tập: Tài liệu truy cập mở

ID: 315153

 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 from the past decade included in this 2nd edition are: 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.
Includes bibliographical references and index.
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