Multivariate Density Estimation: Theory, Practice, and Visualization / Edition 1 available in Hardcover
- Pub. Date:
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.
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.
Table of Contents
Representation and Geometry of Multivariate Data.
Nonparametric Estimation Criteria.
Histograms: Theory and Practice.
Averaged Shifted Histograms.
Kernel Density Estimators.
The Curse of Dimensionality and Dimension Reduction.
Nonparametric Regression and Additive Models.