Multivariate Density Estimation: Theory, Practice, and Visualization

Передня обкладинка
John Wiley & Sons, 31 серп. 1992 р. - 317 стор.
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.

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Frequency Polygons
The Curse of Dimensionality and Dimension
Appendix A Computer Graphics in R3
Appendix B Data Sets
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Популярні уривки

Сторінка 292 - MARSHALL, AW and OLKIN, I. (1985). A family of bivariate distributions generated by the bivariate Bernoulli distribution, J.
Сторінка 292 - Toward a practical method which helps uncover the structure of a set of multivariate observations by finding the linear transformation which optimizes a new 'index of condensation,' " in Statistical Computation, RC Milton and JA Nelder, Ed.

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Про автора (1992)

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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