Multivariate Density Estimation: Theory, Practice, and Visualization

Front Cover
John Wiley & Sons, Mar 30, 2015 - Mathematics - 384 pages

Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods

Featuring a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis.

The new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the Second Edition demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analysis. Continuing to illustrate the major concepts in the context of the classical histogram, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition also features:

  • Over 150 updated figures to clarify theoretical results and to show analyses of real data sets
  • An updated presentation of graphic visualization using computer software such as R
  • A clear discussion of selections of important research during the past decade, including mixture estimation, robust parametric modeling algorithms, and clustering
  • More than 130 problems to help readers reinforce the main concepts and ideas presented
  • Boxed theorems and results allowing easy identification of crucial ideas
  • Figures in color in the digital versions of the book
  • A website with related data sets

Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition is an ideal reference for theoretical and applied statisticians, practicing engineers, as well as readers interested in the theoretical aspects of nonparametric estimation and the application of these methods to multivariate data. The Second Edition is also useful as a textbook for introductory courses in kernel statistics, smoothing, advanced computational statistics, and general forms of statistical distributions.

 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

CONTENTS ix
3
CONTENTS
31
Theory and Practice
51
5
85
Frequency Polygons
100
x
102
6
137
CONTENTS xi
195
The Curse of Dimensionality and Dimension Reduction
217
Nonparametric Regression and Additive Models
241
Other Applications
267
APPENDIX A Computer Graphics in 脫3
296
CONTENTS xiii
302
267
305
Notation and Abbreviations
311
AUTHOR INDEX
334

xii
198

Other editions - View all

Common terms and phrases

About the author (2015)

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.

Bibliographic information