by J. S. Marron, J. Ahn and H. Zhang.
Summary: There have been some exciting new methodologies developed for addressing the classical statistical problem of classification (also known as discrimination). Most work has been done outside of the statistical community, in a new branch of Computer Science called "Machine Learning". This talk will present an overview, from an intuitive statistical viewpoint, of two major ideas in this area, the Support Vector Machine and Kernel Embedding methods. Special attention will be paid to High Dimension, Low Sample Size contexts. Note that classical statistical multivariate analysis is useless in HDLSS settings, because the first step of "sphering the data" fails due to singularity of the covariance matrix. Performance of these methods will be illustrated in the context of examples from medical image analysis, gene expression micro-array analysis, and chemometrics, where HDLSS problems are endemic.
To view talk, click here.
Given at Midwestern Biopharmaceutical Meeting, Muncie, Indiana, May
by F. Godtliebsen, J. S. Marron and P. Chaudhuri.
Summary: Understand what features in a smooth are "really there", as opposed to being artifacts of sampling variability or background noise. Main setting are two dimensional image analysis and density estimation.
To Download Full Talk, Click: (PDF version (0.98 MB) | Postscript Version (11.4 MB)) .
- UNC Department of Computer Science, May 1999
- Weierstrass Institute for Stochastics, Berlin, May 1999
- National Center for Atmospheric Research, Boulder, Co, July 1999.
The Title Page:
by J. S. Marron
Summary: Give some personal views about funding directions (past and future). Also grind some related personal axes about the field of statistics and it general direction.
To Download Full Talk, Click: (PDF version (69.6 KB) | Postscript Version (181 KB)) .
- Panel Discussion on "Modeling of Processes in Science and Industry"
50th Anniversary Celebration, Department of Statistics at Virginia Tech, Blacksburg, Va.
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Marron's Home Page