Multivariate Density Estimation: Theory, Practice, and Visualization

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9780471547709: Multivariate Density Estimation: Theory, Practice, and Visualization
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Written to convey an intuitive feel for both theory and practice, its main objective is to illustrate what a powerful tool density estimation can be when used not only with univariate and bivariate data but also in the higher dimensions of trivariate and quadrivariate information. Major concepts are presented in the context of a histogram in order to simplify the treatment of advanced estimators. Features 12 four-color plates, numerous graphic illustrations as well as a multitude of problems and solutions.

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From the Back Cover:

Density estimation has long been recognized as an important tool when used with univariate and bivariate data. But the computer revolution of recent years has provided access to data of unprecedented complexity in ever-growing volume. New tools are required to detect and summarize the multivariate structure of these difficult data. Multivariate Density Estimation: Theory, Practice, and Visualization demonstrates that density estimation retains its explicative power even when applied to trivariate and quadrivariate data. By presenting the major ideas in the context of the classical histogram, the text simplifies the understanding of advanced estimators and develops links between the intuitive histogram and other methods that are more statistically efficient. The theoretical results covered are those particularly relevant to application and understanding. The focus is on methodology, new ideas, and practical advice. A hierarchical approach draws attention to the similarities among different estimators. Also, detailed discussions of nonparametric dimension reduction, nonparametric regression, additive modeling, and classification are included. Because visualization is a key element in effective multivariate nonparametric analysis, more than 100 graphic illustrations supplement the numerous problems and examples presented in the text. In addition, sixteen four-color plates help to convey an intuitive feel for both the theory and practice of density estimation in several dimensions. Ideal as an introductory textbook, Multivariate Density Estimation is also an indispensable professional reference for statisticians, biostatisticians, electrical engineers, econometricians, and other scientistsinvolved in data analysis.

About the Author:

David W. Scott, PhD, is Noah Harding Professor in the Department of Statistics at Rice University. The author of over 100 published articles, papers, and book chapters, Dr. Scott is also Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics. He is recipient of the ASA Founder’s Award and the Army Wilks Award. His research interests include computational statistics, data visualization, and density estimation. Dr. Scott is also coeditor of Wiley Interdisciplinary Reviews: Computational Statistics and previous Editor of the Journal of Computational and Graphical Statistics.

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