國立虎尾科技大學 |

Robust statistics through the monitoring approach = applications in regression /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Robust statistics through the monitoring approach/ by Anthony C. Atkinson ... [et al.].
其他題名:	applications in regression /
其他作者:	Atkinson, A. C.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xxvii, 546 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Robust statistics. -
電子資源:	https://doi.org/10.1007/978-3-031-88365-1
ISBN:	9783031883651

Robust statistics through the monitoring approach = applications in regression /
Robust statistics through the monitoring approachapplications in regression /[electronic resource] :by Anthony C. Atkinson ... [et al.]. - Cham :Springer Nature Switzerland :2025. - xxvii, 546 p. :ill. (some col.), digital ;24 cm. - Springer series in statistics,2197-568X. - Springer series in statistics..

Preface -- Introduction and the Grand Plan -- Introduction to M-Estimation for Univariate Samples -- Robust Estimators in Multiple Regression -- The Monitoring Approach in Multiple Regression -- Practical Comparison of the Different Estimators -- Transformations -- Non-parametric Regression -- Extensions of the Multiple Regression Model -- Model selection -- Some Robust Data Analyses -- Software and Datasets -- Solutions -- References -- Author Index.

Open access.

This open access book presents robust statistical methods and procedures through the monitoring approach, with an emphasis on applications to linear regression. Illustrating the theory, it explores both large and small-sample properties. The performance of the forward search and of the monitoring of static robust estimators for regression data are illuminated through numerous data analyses using MATLAB and R. The book describes the results of many years' work of the authors in the development of powerful methods of robust regression analysis. Robust methods are designed to analyse contaminated data. The well-established static robust methods estimate model features, such as parameter estimates, assuming the amount of contamination in the data is known. These methods are described in detail in Chapter 2 for estimation in a simple sample. The extension to regression is presented in Chapter 3, with an emphasis on S-estimation and related procedures as well as on least trimmed squares. The monitoring methods of Chapter 4, including the forward search, find the appropriate level of robustness for each data set and so avoid biased estimation from the inclusion of outliers and inefficiency due to the deletion of uncontaminated observations. This analysis is followed by examples which illustrate the use of the interactive graphical analyses associated with the authors' FSDA toolbox. Numerical comparisons of the size and power of outlier tests appear in Chapter 5. Later chapters illustrate applications to response transformation in regression and to non-parametric regression. Extensions of the robust multiple regression model include Bayesian, heteroskedastic, time series and compositional regression, together with the clustering of regression models. Finally, several approaches to model selection are investigated and robust analyses of regression data are presented that illustrate the use of the techniques introduced earlier. Exercises are given at the end of each chapter, with solutions at the end of the book. The MATLAB code can be reproduced using MATLAB Online, without the need for a license, or via the language-agnostic Jupyter notebook environment, after installing the MATLAB kernel. Online computer code is available for all examples and exercises, together with a series of YouTube videos. Aimed at professional statisticians and researchers concerned with insightful data analysis, as well as postgraduate students, the book may also serve as a text for a modern interactive robust regression course.

ISBN: 9783031883651

Standard No.: 10.1007/978-3-031-88365-1doiSubjects--Topical Terms:

528156
Robust statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Robust statistics through the monitoring approach = applications in regression /
LDR:04106nam a2200349 a 4500 001 1166958
003 DE-He213
005 20250727130209.0
006 m d
007 cr nn 008maaau
008 251217s2025 sz s 0 eng d
020 $a 9783031883651 $q (electronic bk.)
020 $a 9783031883644 $q (paper)
024 7 $a 10.1007/978-3-031-88365-1 $2 doi
035 $a 978-3-031-88365-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
090 $a QA276 $b .R667 2025
245 0 0 $a Robust statistics through the monitoring approach $h [electronic resource] : $b applications in regression / $c by Anthony C. Atkinson ... [et al.].
260 $a Cham : $c 2025. $b Springer Nature Switzerland : $b Imprint: Springer,
300 $a xxvii, 546 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer series in statistics, $x 2197-568X
505 0 $a Preface -- Introduction and the Grand Plan -- Introduction to M-Estimation for Univariate Samples -- Robust Estimators in Multiple Regression -- The Monitoring Approach in Multiple Regression -- Practical Comparison of the Different Estimators -- Transformations -- Non-parametric Regression -- Extensions of the Multiple Regression Model -- Model selection -- Some Robust Data Analyses -- Software and Datasets -- Solutions -- References -- Author Index.
506 $a Open access.
520 $a This open access book presents robust statistical methods and procedures through the monitoring approach, with an emphasis on applications to linear regression. Illustrating the theory, it explores both large and small-sample properties. The performance of the forward search and of the monitoring of static robust estimators for regression data are illuminated through numerous data analyses using MATLAB and R. The book describes the results of many years' work of the authors in the development of powerful methods of robust regression analysis. Robust methods are designed to analyse contaminated data. The well-established static robust methods estimate model features, such as parameter estimates, assuming the amount of contamination in the data is known. These methods are described in detail in Chapter 2 for estimation in a simple sample. The extension to regression is presented in Chapter 3, with an emphasis on S-estimation and related procedures as well as on least trimmed squares. The monitoring methods of Chapter 4, including the forward search, find the appropriate level of robustness for each data set and so avoid biased estimation from the inclusion of outliers and inefficiency due to the deletion of uncontaminated observations. This analysis is followed by examples which illustrate the use of the interactive graphical analyses associated with the authors' FSDA toolbox. Numerical comparisons of the size and power of outlier tests appear in Chapter 5. Later chapters illustrate applications to response transformation in regression and to non-parametric regression. Extensions of the robust multiple regression model include Bayesian, heteroskedastic, time series and compositional regression, together with the clustering of regression models. Finally, several approaches to model selection are investigated and robust analyses of regression data are presented that illustrate the use of the techniques introduced earlier. Exercises are given at the end of each chapter, with solutions at the end of the book. The MATLAB code can be reproduced using MATLAB Online, without the need for a license, or via the language-agnostic Jupyter notebook environment, after installing the MATLAB kernel. Online computer code is available for all examples and exercises, together with a series of YouTube videos. Aimed at professional statisticians and researchers concerned with insightful data analysis, as well as postgraduate students, the book may also serve as a text for a modern interactive robust regression course.
650 0 $a Robust statistics. $3 528156
650 0 $a Regression analysis. $3 569541
650 1 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Applied Statistics. $3 1205141
650 2 4 $a Statistics and Computing. $3 1366004
650 2 4 $a Linear Models and Regression. $3 1366135
700 1 $a Atkinson, A. C. $3 1495758
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Springer series in statistics. $3 882180
856 4 0 $u https://doi.org/10.1007/978-3-031-88365-1
950 $a Mathematics and Statistics (SpringerNature-11649)