JSM 2005 Online Program

Abstract #302315

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:	435
Type:	Invited
Date/Time:	Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor:	IMS
Abstract - #302315
Title:	New Asymptotic Results in Multiscale Image Analysis
Author(s):	Xiaoming Huo*+ and Xuelei Ni
Companies:	Georgia Institute of Technology and Georgia Institute of Technology
Address:	765 Ferst Dr., Atlanta, GA, 30332-0205,
Keywords:	regression ; variable selection ; dimension reduction ; nonlinear ; rate of convergence ; consistency
Abstract:	Multiscale image analyses have been proven to be powerful in both generating efficient algorithms and deriving the fundamental difficulties of some imaging tasks. The existing works include the Multiscale Geometric Detection (MGD) to derive the fundamental detectability of geometric objects under noises and the Multiscale Significance Run Algorithm (MSRA) to detect an underlying curve with unknown smoothness in a random point cloud. Despite the current successes, stronger theoretical results can be derived in many situations. In this paper, we review how stronger results can be derived by considering the limiting distributions of the test statistics adopted in these methods. The new theoretical results offer explanations to the robustness of the proposed algorithms that have been observed in simulation. This is a joint work with Ms. Xuelei Ni.

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