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Author: Georgy L. Shevlyakov
Publisher: Walter de Gruyter
ISBN: 3110936003
Category : Mathematics
Languages : en
Pages : 325
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Book Description
The series is devoted to the publication of high-level monographs and surveys which cover the whole spectrum of probability and statistics. The books of the series are addressed to both experts and advanced students.
Author: Georgy L. Shevlyakov
Publisher: Walter de Gruyter
ISBN: 3110936003
Category : Mathematics
Languages : en
Pages : 325
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Book Description
The series is devoted to the publication of high-level monographs and surveys which cover the whole spectrum of probability and statistics. The books of the series are addressed to both experts and advanced students.
Author: Ricardo A. Maronna
Publisher: John Wiley & Sons
ISBN: 1119214688
Category : Mathematics
Languages : en
Pages : 466
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Book Description
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.
Author: David C. Hoaglin
Publisher: John Wiley & Sons
ISBN: 0471384917
Category : Mathematics
Languages : en
Pages : 484
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Book Description
Originally published in hardcover in 1982, this book is now offered in a Wiley Classics Library edition. A contributed volume, edited by some of the preeminent statisticians of the 20th century, Understanding of Robust and Exploratory Data Analysis explains why and how to use exploratory data analysis and robust and resistant methods in statistical practice.
Author: Eric Neumayer
Publisher: Cambridge University Press
ISBN: 1108415393
Category : Business & Economics
Languages : en
Pages : 269
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Book Description
This highly accessible book presents robustness testing as the methodology for conducting quantitative analyses in the presence of model uncertainty.
Author: Stephan Morgenthaler
Publisher: Birkhauser
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 304
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Book Description
The book serves as an insightful and useful companion for students interested in research or scientists who want to learn about modern developments in the field of data analysis.
Author: Yuriy Kharin
Publisher: Springer Science & Business Media
ISBN: 3319008404
Category : Mathematics
Languages : en
Pages : 356
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Book Description
This book offers solutions to such topical problems as developing mathematical models and descriptions of typical distortions in applied forecasting problems; evaluating robustness for traditional forecasting procedures under distortionism and more.
Author: Sheng Li
Publisher: Springer
ISBN: 3319601768
Category : Computers
Languages : en
Pages : 224
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Book Description
This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary. Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.
Author: Robert L. Launer
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 330
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Book Description
An introduction to robust estimation; The robustness of residual displays; Robust smoothing; Robust pitman-like estimators; Robust estimation in the presence of outliers; Study of robustness by simulation: particularly improvement by adjustment and combination; Robust techniques for the user; Application of robust regression to trajectory data reduction; Tests for censoring of extreme values (especially) when population distributions are incompletely defined; Robust estimation for time series autoregressions; Robust techniques in communication; Robustness in the strategy of scientific model building; A density-quantile function perspective on robust.
Author: Georgy L. Shevlyakov
Publisher:
ISBN: 9783110629569
Category :
Languages : en
Pages :
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Book Description
Author: David C. Hoaglin
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 472
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Book Description
Textbook on robust and exploratory data analysis and related statistical methods - covers stem-and-leaf displays, letter values, boxplots and batch graphic displays, resistant lines, analysis of two- way tables by medians, examining residuals, mathematical aspects of transformation, scale estimators, comparison of location estimators, confidence intervals for location, etc. References.
