Missouri University of Science and Technology

Department of Mathematics and Statistics

 

Colloquia Fall 2008

The weekly "PinkSheet" seminar schedule is available here.

The past: Spring 2008 Fall 2007 Spring 2007 Fall 2006 Spring 2006 Fall 2005 Spring 2005 Fall 2004 Spring 2004 Fall 2003 Spring 2003 Fall 2002 Spring 2002 Fall 2001




Our colloquium talks are held in Room G-5 Rolla Building. We begin with coffee and refreshments at 4:00 pm, followed by the hour-long lecture at 4:15 unless otherwise noted. The entire Missouri S&T community is warmly invited to attend. We especially encourage undergraduate students, graduate students, and faculty from other departments to attend.

Suggestions/nominations for speakers may be made to Martin J. Bohner, Mathematics and Statistics Colloquium chair, at bohner@mst.edu

Monday, August 25, 4:00 PM Note unusual day

 

Horst Behncke, Universität Osnabrück, Osnabrück, Germany / Missouri S&T Faculty Host: Stephen Clark

 

Title: Periodical Cicadas

 

Abstract: In the US one finds 6(3) species of cicadas which have the longest known life cycle - 13 resp. 17 years. By means of a "realistic" model we describe this periodicity, the convergence and evolution of these insects.

 

Friday, September 12, 4:00 PM

 

Hristo Voulov, University of Missouri - Kansas City, Kansas City, Missouri / Missouri S&T Faculty Host: Martin Bohner

 

Title: On the global asymptotic stability of planar dissipative maps

 

Abstract: We consider area-contracting maps in the plane, which are often called dissipative maps. If a smooth map has an asymptotically stable equilibrium point P, the area-contracting property seems necessary at least locally around P. Here we find sufficient conditions for global asymptotic stability of planar dissipative maps. Then, we apply this result to a 15 year old open problem about a second-order rational difference equation, posed by G. Ladas.

Friday, September 26, 4:00 PM

 

Johanna Michor, Courant Institute, New York / Missouri S&T Faculty Host: Stephen Clark

 

Title: Scattering theory for Jacobi operators with quasi-periodic background and applications to the Toda hierarchy

 

Abstract: Jacobi operators, which can be viewed as the discrete analogue of Sturm-Liouville operators, play a fundamental role in the investigation of completely integrable nonlinear lattices, in particular, the Toda lattice. We will develop scattering theory for Jacobi operators which are short range perturbations of (steplike) quasi-periodic finite-gap background operators via the Gel'fand-Levitan-Marchenko approach. Minimal scattering data which determine the perturbed operator uniquely will be presented. This will allow us to solve the associated initial value problem of the Toda hierarchy via the inverse scattering transform. This talk is based on joint work with Iryna Egorova (Kharkov) and Gerald Teschl (Vienna).

 

Friday, October 3, 4:00 PM

 

Dennis Cook, University of Minnesota, Minneaoplis, Minnesota / Missouri S&T Faculty Host: Meggie Wen

 

Title: Dimension Reduction Paradigms for Regression

 

Abstract: Dimension reduction for regression, represented primarily by principal components, is ubiquitous in the applied sciences. This is an old idea that has moved to a position of prominence in recent years because technological advances now allow scientists to routinely formulate regressions in which the number p of predictors is considerably larger than in the past. Although "large" p regressions are perhaps mainly responsible for renewed interest, dimension reduction methodology can be useful regardless of the size of p. Starting with a little history and a definition of "sufficient reductions", we will consider a variety of models for dimension reduction in regression. The models start from one in which maximum likelihood estimation produces principal components, step along a few incremental expansions, and end with forms that have the potential to improve on some standard methodology. This development provides remedies for two concerns that have dogged principal components in regression: principal components are typically computed from the predictors alone and then do not make apparent use of the response, and they are not equivariant under full rank linear transformation of the predictors.