todorov valentin (9 risultati)

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Taschenbuch. Condizione: Neu. Multivariate Robust Statistics | Methods and Computation | Valentin Todorov (u. a.) | Taschenbuch | 156 S. | Deutsch | 2015 | Südwestdeutscher Verlag für Hochschulschriften | EAN 9783838108148 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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). 156 pp. Deutsch.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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).VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 156 pp. Deutsch.

Taschenbuch. Condizione: Neu. 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).