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|a 019087264
|2 Uk
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|a 9781119214663
|q (electronic bk.)
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|a 1119214661
|q (electronic bk.)
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|a (OCoLC)1059450732
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|a 9781119214663
|b Wiley
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|a MAT
|x 003000
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|a HCDD
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|a Maronna, Ricardo A.,
|e author.
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| 245 |
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|a Robust statistics :
|b theory and methods (with R) /
|c Ricardo A. Maronna [and 3 others]
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| 250 |
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|a Second edition.
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| 264 |
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|a [Place of publication not identified] :
|b Wiley,
|c [2018]
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| 300 |
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|a 1 online resource :
|b illustrations
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 504 |
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|a Includes bibliographical references and index.
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|a Online resource; title from PDF title page (EBSCO, viewed October 30, 2018).
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|a Cover; Title Page; Copyright; Contents; Preface; Preface to the First Edition; About the Companion Website; Chapter 1 Introduction; 1.1 Classical and robust approaches to statistics; 1.2 Mean and standard deviation; 1.3 The "three sigma edit" rule; 1.4 Linear regression; 1.4.1 Straight-line regression; 1.4.2 Multiple linear regression; 1.5 Correlation coefficients; 1.6 Other parametric models; 1.7 Problems; Chapter 2 Location and Scale; 2.1 The location model; 2.2 Formalizing departures from normality; 2.3 M-estimators of location; 2.3.1 Generalizing maximum likelihood
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|a 2.3.2 The distribution of M-estimators2.3.3 An intuitive view of M-estimators; 2.3.4 Redescending M-estimators; 2.4 Trimmed and Winsorized means; 2.5 M-estimators of scale; 2.6 Dispersion estimators; 2.7 M-estimators of location with unknown dispersion; 2.7.1 Previous estimation of dispersion; 2.7.2 Simultaneous M-estimators of location and dispersion; 2.8 Numerical computing of M-estimators; 2.8.1 Location with previously-computed dispersion estimation; 2.8.2 Scale estimators; 2.8.3 Simultaneous estimation of location and dispersion; 2.9 Robust confidence intervals and tests
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|a 2.9.1 Confidence intervals2.9.2 Tests; 2.10 Appendix: proofs and complements; 2.10.1 Mixtures; 2.10.2 Asymptotic normality of M-estimators; 2.10.3 Slutsky's lemma; 2.10.4 Quantiles; 2.10.5 Alternative algorithms for M-estimators; 2.11 Recommendations and software; 2.12 Problems; Chapter 3 Measuring Robustness; 3.1 The influence function; 3.1.1 *The convergence of the SC to the IF; 3.2 The breakdown point; 3.2.1 Location M-estimators; 3.2.2 Scale and dispersion estimators; 3.2.3 Location with previously-computed dispersion estimator; 3.2.4 Simultaneous estimation
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|a 3.2.5 Finite-sample breakdown point3.3 Maximum asymptotic bias; 3.4 Balancing robustness and efficiency; 3.5 *"Optimal" robustness; 3.5.1 Bias- and variance-optimality of location estimators; 3.5.2 Bias optimality of scale and dispersion estimators; 3.5.3 The infinitesimal approach; 3.5.4 The Hampel approach; 3.5.5 Balancing bias and variance: the general problem; 3.6 Multidimensional parameters; 3.7 *Estimators as functionals; 3.8 Appendix: Proofs of results; 3.8.1 IF of general M-estimators; 3.8.2 Maximum BP of location estimators; 3.8.3 BP of location M-estimators
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|a 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
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| 650 |
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|a Robust statistics.
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| 650 |
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7 |
|a MATHEMATICS
|x Applied.
|2 bisacsh
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| 650 |
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7 |
|a MATHEMATICS
|x Probability & Statistics
|x General.
|2 bisacsh
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| 650 |
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7 |
|a Robust statistics
|2 fast
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| 758 |
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|i has work:
|a Robust statistics (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGvTPFpCwxjhmJrYtXB6w3
|4 https://id.oclc.org/worldcat/ontology/hasWork
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|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=5568377
|y Click for online access
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| 880 |
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|6 505-00/(S
|a 3.8.4 Maximum bias of location M-estimators3.8.5 The minimax bias property of the median; 3.8.6 Minimizing the GES; 3.8.7 Hampel optimality; 3.9 Problems; Chapter 4 Linear Regression 1; 4.1 Introduction; 4.2 Review of the least squares method; 4.3 Classical methods for outlier detection; 4.4 Regression M-estimators; 4.4.1 M-estimators with known scale; 4.4.2 M-estimators with preliminary scale; 4.4.3 Simultaneous estimation of regression and scale; 4.5 Numerical computing of monotone M-estimators; 4.5.1 The L1 estimator; 4.5.2 M-estimators with smooth ψ-function
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|a EBC-AC
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| 994 |
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|a 92
|b HCD
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