Multivariate Density Estimation: Theory, Practice, and Visualization
Written to convey an intuitive feeling for both theory and practice, this book illustrates 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. The text features numerous graphics, problems and solutions, and references to online four-color illustrations. Theoretical statisticians and practicing engineers won't want to miss this.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Theory and Practice
The Curse of Dimensionality and Dimension Reduction
Nonparametric Regression and Additive Models
APPENDIX A Computer Graphics in 脫3
Notation and Abbreviations
Other editions - View all
ˆf(x adaptive algorithm AMISE applied approximation asymptotic bandwidth bias bins bivariate bootstrap bumps Chernoff faces choice clusters computed Consider contour covariance criterion cross-validation CURSE OF DIMENSIONALITY curve data analysis data points data-based dataset density function derivative DIMENSION REDUCTION Epanechnikov Equation example frequency polygon GEOMETRY OF MULTIVARIATE graphical higher order kernels hypersphere integrated squared interval KERNEL DENSITY ESTIMATORS linear matrix mesh Methods minimizer MISE mixture mixture density mode MULTIVARIATE DATA NONPARAMETRIC ESTIMATION NONPARAMETRIC REGRESSION normal data normal density normal kernel optimal bandwidths oversmoothed parallel coordinates parametric estimator plot pointwise polynomial problem Projection Pursuit random REPRESENTATION AND GEOMETRY result sample sizes scatter diagrams scatterplot Scott Second Edition sepal shifted histogram shown in Figure slices smoother smoothing parameter spline squared error Statist structure surface Terrell Theorem Theory transformation trivariate Tukey univariate values variables variance vector visualization width zero-bias