JSM 2005 Online Program

Abstract #302856

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	110
Type:	Other
Date/Time:	Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor:	ASA
Abstract - #302856
Title:	Multivariate Probability Density Estimation
Author(s):	David Scott*+
Companies:	Rice University
Address:	POB 1892, Houston, TX, 77251-1892, United States
Keywords:	histogram ; smoothing ; kernel density estimation ; mode trees
Abstract:	The need to estimate multivariate probability densities arises in applications such as discriminant analysis, clustering, nonparametric regression and smoothing, image processing, and gene expression analysis. This introductory overview session consists of two lectures in the areas of nonparametric and semiparametric probability density estimation. In the first talk, techniques such as histograms, averaged shifted histograms, kernel methods, and mode trees will be covered. Dr. Scott is the author of Multivariate Density Estimation: Theory, Practice, and Visualization (John Wiley & Sons 1992). In the field of nonparametric density estimation, Dr. Scott has provided fundamental understanding of many estimators, including the histogram, frequency polygon, averaged shifted histogram, discrete penalized-likelihood estimator, adaptive estimators, oversmoothed estimators, and modal and robust regression estimators.

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