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Fall 2007 Distinguished Lectures

October 5, 2007 GSB 3.130 3:15 - 4:15 pm "Sparcity in Statistical Modeling" by Dr. Michael West Duke University Dr. West’s research and teaching activities are in a number of areas of Bayesian statistics, computational and mathematical sciences, especially on complex modeling in higher-dimensional problems. Core areas of modeling research relate to multivariate analysis, high-dimensional inference and computation, time series modeling, among others. Key collaborative activities include multidisciplinary projects in a number of biomedical and genomic areas.

November 9,2007 GSB 3.138 3:15 - 4:15pm "Information Theory and Statistics" by Dr. Arnold Zellner University of Chicago Dr. Arnold Zellner received his Ph.D. in Economics from the University of California in 1957. Currently, Dr. Zellner is Professor Emeritus in the Graduate School of Business at the University of Chicago, and Adjunct Professor at the University of California, Berkeley. He is a member of many professional societies such as: Econometric Society, American Economic Association, American Statistical Association, American Association for the Advancement of Science, International Statistical Institute, American Academy of Arts & Sciences, Institute of Mathematical Statistics, International Society for Bayesian Analysis, & International Institute of Forecasters. Dr. Zellner has chaired and served on many Ph.D. committees of graduate students in Econometrics, Macroeconomics, Time Series Analysis, and Bayesian Inference & Decision Theory. He has also chaired Econometrics Preliminary Committees and organized new econometrics research workshops and courses.

Fall 2007 | Spring 2008

Friday, October 5, 2007
GSB 3.130
3:15 - 4:15 pm

"Sparcity in Statistical Modeling"

Dr. Mike West, Arts & Sciences Professor of Statistical Science and Director of Graduate Studies, Department of Statistical Science (Duke University) Sparsity in Statistical Modeling
Abstract:
The concepts and methods of sparsity modeling are key to problems of model specification, variable selection and multivariate structure assessment in statistical science. Sparsity also provides the foundation for scaling statistical models from the viewpoints of both scientific parsimony and computational accessibility/feasibility. My talk will overview a range of developments in the application of sparsity modeling in modern, model-based analysis of multivariate data arising in problems with very many parameters. I will touch on specific contexts including high-dimensional latent factor analysis and graphical models, speaking to both modeling concepts and computational questions, and drawing on applied studies in areas including pathway genomics and financial time series analysis.

Friday, November 9, 2007
GSB 3.138
3:15 - 4:15 pm

"Information Theory and Statistics"

Dr. Arnold Zellner, H.G.B. Alexander Distinguished Service Professor Emeritus of Economics and Statistics, Graduate School of Business (University of Chicago)Information Theory and Statistics
Abstract:
After describing some aspects of information theory, it will be shown how it has been employed not only to produce models for observations and prior densities for their parameters but also to derive optimal learning models, including Bayes' theorem. These optimal learning models have the property that input information equals output information and thus they are 100% efficient, as recognized in the literature. Some of these learning models permit inverse inference to be performed without the use of a prior density and/or a likelihood function. Examples and references illustrating the derivations and uses of such models will be provided and discussed. By having a set of optimal learning models, including Bayes' theorem, on the shelf to help solve a variety of inference and decision problems, statisticians and other scientists will be more effective in their work.