Multivariate Robust Statistics : Methods and Computation

Valentin Todorov

ISBN 10: 3838108140 / ISBN 13: 9783838108148

Published by Südwestdeutscher Verlag Für Hochschulschriften, 2009

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - The goal of robust statistics is to develop methods that can cope with the presence of outliers in the data and nevertheless produce reasonable results. In this book some of the most popular robust multivariate methods are investigated and new methods are proposed. Their performance is evaluated and compared in a variety of situations. The focus is on high breakdown point methods for discriminant analysis, multivariate tests and their basis, the robust estimators for multivariate location and covariance. The routine use of robust methods in a wide area of application domains is unthinkable without the computational power of today's personal computers and the availability of ready to use implementations of the algorithms. A unified computational platform organized as common patterns which we call statistical design patterns in analogy to the design patterns widely used in software engineering is proposed. The concrete implementation is an object oriented framework for robust multivariate analysis developed in R, an environment for statistical computing and graphics (R Development Core Team, 2009).

Seller Inventory # 9783838108148

Bibliographic Details

• Title: Multivariate Robust Statistics : Methods and Computation
• Publisher: Südwestdeutscher Verlag Für Hochschulschriften
• Publication Date: 2009
• Language: English
• ISBN 10: 3838108140
• ISBN 13: 9783838108148
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 10D millimeters 220 millimeters width by 150 millimeters height by 10 millimeters depth
• Weight: 250 grams

About this title

Synopsis

The goal of robust statistics is to develop methods that can cope with the presence of outliers in the data and nevertheless produce reasonable results. In this book some of the most popular robust multivariate methods are investigated and new methods are proposed. Their performance is evaluated and compared in a variety of situations. The focus is on high breakdown point methods for discriminant analysis, multivariate tests and their basis, the robust estimators for multivariate location and covariance. The routine use of robust methods in a wide area of application domains is unthinkable without the computational power of today’s personal computers and the availability of ready to use implementations of the algorithms. A unified computational platform organized as common patterns which we call statistical design patterns in analogy to the design patterns widely used in software engineering is proposed. The concrete implementation is an object oriented framework for robust multivariate analysis developed in R, an environment for statistical computing and graphics (R Development Core Team, 2009).

About the Author

Valentin Todorov is a Management Information Officer with theUnited Nations Industrial Development Organization (UNIDO). Hereceived a doctoral degree from Vienna University of Technology,Austria.Peter Filzmoser is a professor at Vienna University ofTechnology. He holds a doctoral degree and habilitation from thesame University.