{"product_id":"robust-statistics-isbn-9781119214687","title":"Robust Statistics","description":"\u003cp\u003e\u003cb\u003eA 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.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eClassical 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\u003ci\u003e Robust Statistics\u003c\/i\u003e: \u003ci\u003eTheory and Methods (with R) \u003c\/i\u003epresents 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.\u003c\/p\u003e \u003cp\u003eUnlike other books on the market, \u003ci\u003eRobust Statistics\u003c\/i\u003e: \u003ci\u003eTheory and Methods (with R)\u003c\/i\u003e 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExplains both the use and theoretical justification of robust methods\u003c\/li\u003e \u003cli\u003eGuides readers in selecting and using the most appropriate robust methods for their problems\u003c\/li\u003e \u003cli\u003eFeatures computational algorithms for the core methods\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eRobust statistics research results of the last decade included in this 2\u003csup\u003end\u003c\/sup\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eRobust Statistics\u003c\/i\u003e 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.\u003c\/p\u003e \u003cdiv id=\"_mcePaste\"\u003ePreface\u003c\/div\u003e \u003cdiv id=\"_mcePaste\"\u003ePreface to the First Edition\u003c\/div\u003e \u003cdiv id=\"_mcePaste\"\u003eAbout the Companion Website\u003c\/div\u003e \u003cdiv id=\"_mcePaste\"\u003e1 Introduction\u003c\/div\u003e \u003cdiv id=\"_mcePaste\"\u003e2 Location and Scale\u003c\/div\u003e \u003cdiv id=\"_mcePaste\"\u003e3 Measuring Robustness\u003c\/div\u003e \u003cdiv id=\"_mcePaste\"\u003e4 Linear Regression 1\u003c\/div\u003e \u003cdiv id=\"_mcePaste\"\u003e5 Linear Regression 2\u003c\/div\u003e \u003cdiv id=\"_mcePaste\"\u003e6 Multivariate Analysis\u003c\/div\u003e \u003cdiv id=\"_mcePaste\"\u003e7 Generalized Linear Models\u003c\/div\u003e \u003cdiv id=\"_mcePaste\"\u003e8 Time Series\u003c\/div\u003e \u003cdiv id=\"_mcePaste\"\u003e9 Numerical Algorithms\u003c\/div\u003e \u003cdiv id=\"_mcePaste\"\u003e10 Asymptotic Theory of M-estimators\u003c\/div\u003e \u003cdiv id=\"_mcePaste\"\u003e11 Description of Datasets\u003c\/div\u003e \u003cdiv id=\"_mcePaste\"\u003eReferences\u003c\/div\u003e \u003cdiv id=\"_mcePaste\"\u003eIndex\u003c\/div\u003e \u003cdiv\u003e \u003c\/div\u003e  \u003cp\u003e\u003cb\u003eRicardo A. Maronna,\u003c\/b\u003e Consultant Professor, National University of La Plata, Argentina \u003c\/p\u003e\u003cp\u003e\u003cb\u003eR. Douglas Martin,\u003c\/b\u003e Departments of Applied Mathematics and Statistics, University of Washington, USA \u003c\/p\u003e\u003cp\u003e\u003cb\u003eVictor J. Yohai,\u003c\/b\u003e Department of Mathematics, University of Buenos Aires, and CONICET, Argentina \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMatías Salibián-Barrera,\u003c\/b\u003e Department of Statistics, The University of British Columbia, Canada  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA 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.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eClassical 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\u003ci\u003e Robust Statistics\u003c\/i\u003e: \u003ci\u003eTheory and Methods (with R)\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eUnlike other books on the market, \u003ci\u003eRobust Statistics\u003c\/i\u003e: \u003ci\u003eTheory and Methods (with R)\u003c\/i\u003e 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eExplains both the use and theoretical justification of robust methods\u003c\/li\u003e \u003cli\u003eGuides readers in selecting and using the most appropriate robust methods for their problems\u003c\/li\u003e \u003cli\u003eFeatures computational algorithms for the core methods\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eRobust 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eRobust Statistics\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989970665701,"sku":"NP9781119214687","price":115.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119214687.jpg?v=1761786074","url":"https:\/\/k12savings.com\/products\/robust-statistics-isbn-9781119214687","provider":"K12savings","version":"1.0","type":"link"}