Multivariate Nonparametric Regression and Visualization: With R and Applications to Finance (Wiley Series in Computational Statistics)

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9780470384428: Multivariate Nonparametric Regression and Visualization: With R and Applications to Finance (Wiley Series in Computational Statistics)
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A modern approach to statistical learning and its applications through visualization methods

With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generating mechanisms, the book begins with an overview of classification and regression.

The book then introduces and examines various tested and proven visualization techniques for learning samples and functions. Multivariate Nonparametric Regression and Visualization identifies risk management, portfolio selection, and option pricing as the main areas in which statistical methods may be implemented in quantitative finance. The book provides coverage of key statistical areas including linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods. Exploring the additional applications of nonparametric and semiparametric methods, Multivariate Nonparametric Regression and Visualization features: * An extensive appendix with R-package training material to encourage duplication and modification of the presented computations and research * Multiple examples to demonstrate the applications in the field of finance * Sections with formal definitions of the various applied methods for readers to utilize throughout the book

Multivariate Nonparametric Regression and Visualization is an ideal textbook for upper-undergraduate and graduate-level courses on nonparametric function estimation, advanced topics in statistics, and quantitative finance. The book is also an excellent reference for practitioners who apply statistical methods in quantitative finance.

Biografía del autor:

JUSSI KLEMELA, PhD, is Senior Research Fellow in the Department of Mathematical Sciences at the University of Oulu. He has written numerous journal articles on his research interests, which include density estimation and the implementation of cutting edge visualization tools. Dr. Klemela is the author of Smoothing of Multivariate Data: Density Estimation and Visualization, also published by Wiley.

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Descripción John Wiley and Sons Ltd, United States, 2014. Hardback. Estado de conservación: New. New.. 236 x 157 mm. Language: English . Brand New Book. A modern approach to statistical learning and its applications through visualization methods With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generating mechanisms, the book begins with an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for learning samples and functions. Multivariate Nonparametric Regression and Visualization identifies risk management, portfolio selection, and option pricing as the main areas in which statistical methods may be implemented in quantitative finance. The book provides coverage of key statistical areas including linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods. Exploring the additional applications of nonparametric and semiparametric methods, Multivariate Nonparametric Regression and Visualization features: * An extensive appendix with R-package training material to encourage duplication and modification of the presented computations and research * Multiple examples to demonstrate the applications in the field of finance * Sections with formal definitions of the various applied methods for readers to utilize throughout the book Multivariate Nonparametric Regression and Visualization is an ideal textbook for upper-undergraduate and graduate-level courses on nonparametric function estimation, advanced topics in statistics, and quantitative finance. The book is also an excellent reference for practitioners who apply statistical methods in quantitative finance. Nº de ref. de la librería AAH9780470384428

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Descripción John Wiley and Sons Ltd, United States, 2014. Hardback. Estado de conservación: New. New.. 236 x 157 mm. Language: English . Brand New Book. A modern approach to statistical learning and its applications through visualization methods With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generating mechanisms, the book begins with an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for learning samples and functions. Multivariate Nonparametric Regression and Visualization identifies risk management, portfolio selection, and option pricing as the main areas in which statistical methods may be implemented in quantitative finance. The book provides coverage of key statistical areas including linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods. Exploring the additional applications of nonparametric and semiparametric methods, Multivariate Nonparametric Regression and Visualization features: * An extensive appendix with R-package training material to encourage duplication and modification of the presented computations and research * Multiple examples to demonstrate the applications in the field of finance * Sections with formal definitions of the various applied methods for readers to utilize throughout the book Multivariate Nonparametric Regression and Visualization is an ideal textbook for upper-undergraduate and graduate-level courses on nonparametric function estimation, advanced topics in statistics, and quantitative finance. The book is also an excellent reference for practitioners who apply statistical methods in quantitative finance. Nº de ref. de la librería AAH9780470384428

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Descripción 2014. Hardcover. Estado de conservación: New. 164mm x 257mm x. Hardcover. Covering classification and regression, Statistical Learning is the first of its kind to use visualization techniques to identify, test, and analyze classifiers for their most accurate .Shipping may be from multiple locations in the US or from the UK, depending on stock availability. 392 pages. 0.786. Nº de ref. de la librería 9780470384428

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Descripción 2014. Hardcover. Estado de conservación: New. 164mm x 257mm x. Hardcover. Covering classification and regression, Statistical Learning is the first of its kind to use visualization techniques to identify, test, and analyze classifiers for their m.Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. 392 pages. 0.786. Nº de ref. de la librería 9780470384428

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Descripción Wiley-Blackwell, 2014. Hardcover. Estado de conservación: New. 15 x 25 cm. "This book uniquely utilizes visualization tools to explain and study statistical learning methods. Covering classification and regression, the book is divided into two parts. First, various visualization methods are introduced and explained. Here, the reader is presented with applications of visualization techniques to learning samples (including projection pursuit, graphical matrices, and parallel coordinate plots) as well as functions, and sets. Next, the author provides a "toolbox" that contains formal definitions of the methods applied in the book and then proceeds to present visualizations of classified learning samples and classified test samples. Visualization methods are applied for the initial exploration of data, to identify the correct type of classifier, and to estimate the best achievable classification error. Once identified, the classifier's properties, proper uses, and overall performance are demonstrated and measured using visualization methods. Key areas of coverage include linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods In addition to providing applications to engineering and biomedicine, the author also uses financial data sets as real data examples to illustrate nonparametric function estimation. The author's own R software is used throughout to reproduce and modify the book's computations and research. Readers can duplicate these applications using the software, available via the book's related Web site"-- Our orders are sent from our warehouse locally or directly from our international distributors to allow us to offer you the best possible price and delivery time. Book. Nº de ref. de la librería MM-60499340

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