Robust Statistics
Theory and Methods
(Sprache: Englisch)
Robust Statistics fills the need for a solid, up to date text that presents a broad overview of the theory of robust statistics, integrated with applications and computing. The book features in depth coverage of the key methodology, including regression, multivariate analysis, and time series.
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Produktdetails
Produktinformationen zu „Robust Statistics “
Robust Statistics fills the need for a solid, up to date text that presents a broad overview of the theory of robust statistics, integrated with applications and computing. The book features in depth coverage of the key methodology, including regression, multivariate analysis, and time series.
Klappentext zu „Robust Statistics “
Classical statistical techniques fail to cope well with deviations from a standard distribution. Robust statistical methods take into account these deviations while estimating the parameters of parametric models, thus increasing the accuracy of the inference. Research into robust methods is flourishing, with new methods being developed and different applications considered.Robust Statistics sets out to explain the use of robust methods and their theoretical justification. It provides an up-to-date overview of the theory and practical application of the robust statistical methods in regression, multivariate analysis, generalized linear models and time series. This unique book:
* Enables the reader to select and use the most appropriate robust method for their particular statistical model.
* Features computational algorithms for the core methods.
* Covers regression methods for data mining applications.
* Includes examples with real data and applications using the S-Plus robust statistics library.
* Describes the theoretical and operational aspects of robust methods separately, so the reader can choose to focus on one or the other.
* Supported by a supplementary website featuring time-limited S-Plus download, along with datasets and S-Plus code to allow the reader to reproduce the examples given in the book.
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 ideal for researchers, practitioners and graduate students of statistics, electrical, chemical and biochemical engineering, and computer vision. There is also much to benefit researchers from other sciences, such as biotechnology, who need to use robust statistical methods in their work.
Inhaltsverzeichnis zu „Robust Statistics “
Preface1. Introduction
2. Location and Scale
3. Measuring Robustness
4 Linear Regression 1
5 Linear Regression 2
6. Multivariate Analysis
7. Generalized Linear Models
8. Time Series
9. Numerical Algorithms
10. Asymptotic Theory of M-estimates
11. Robust Methods in S-Plus
12. Description of Data Sets
Bibliography
Index
Autoren-Porträt von Ricardo Maronna
Ricardo Maronna, Department of Mathematics, Universidad Nacional de La Plata, Argentina.Doug Martin, Department of Statistics, University of Washington, USA
Experienced and well-respected statistician. One of the original developers of the robust statistics library for the S-Plus statistical software language.
Victor Yohai, Department of Mathematics, University of Buenos Aires, Argentina.
Bibliographische Angaben
- Autor: Ricardo Maronna
- 2006, 1. Auflage, 424 Seiten, Maße: 22,9 cm, Gebunden, Englisch
- Verlag: Wiley & Sons
- ISBN-10: 0470010924
- ISBN-13: 9780470010921
Sprache:
Englisch
Pressezitat
"This book belongs on the desk of every statistician working in robust statistics, and the authors are to be congratulated for providing the profession with a much needed and valuable resource for teaching and research." ( Journal of the American Statistical Association , June 2008) " a great book for graduate students as well as for applied scientists and data analysts." ( MAA Reviews , Feb 2007)
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