JSM 2005 Online Program

Abstract #304709

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:	229
Type:	Contributed
Date/Time:	Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Section on Nonparametric Statistics
Abstract - #304709
Title:	Pattern Recognition Using Nonparametric Kernel Density Estimation Techniques
Author(s):	Abhishek Lall*+ and Cecil Hallum
Companies:	Sam Houston State University and Sam Houston State University
Address:	Dept of Mathematics and Statistics, Huntsville, TX, 77341,
Keywords:	kernel density estimation ; statistical modeling ; best linear estimator
Abstract:	When it comes to statistical modeling of images, the probability density functions (PDFs) associated with various patterns are likely to vary from image to image and will not, in general, have a known parametric form. This paper utilizes general nonparametric kernel density estimation techniques for building these statistical representations. The PDF is estimated directly from the use of data without any assumptions about the underlying distributions. An estimator, which is a linear combination of several frequently used kernel functions, is investigated to arrive at an improved kernel density estimator. Using the Generalized Gauss-Markov theorem and resorting to the matrix generalized inverse, the proposed estimator is shown to be the weighted Best Linear Estimator.

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