American Statistical Association
New York City
Metropolitan Area Chapter

Mailman School of Public Health
Columbia University
Department of Biostatistics Colloquium



FUNCTIONAL GENERALIZED LINEAR REGRESSION

by

Harrison Zhou, Ph.D.
Department of Statistics
Yale University
www.stat.yale.edu/~hz68/


Abstract

The functional generalized linear model unifies various statistical models, including functional linear regression, functional logistic regression for binary response, and functional Poisson regression for count data under one framework. We will discuss both prediction and slope estimation by functional principal components analysis. Optimal convergence rates are obtained via an asymptotic equivalence result. An application of functional logistic regression to predict US recessions will be discussed, which seems to outperform some classical predictors in economics.

This is a joint work with Wei Dou and David Pollard.

Biographical Note

Harrison Zhou received his Ph.D. in 2004 from Cornell University. He is now an associate professor at Yale University. He is the winner of the 2009 Noether Young Scholar Award and a 2007 NSF CAREER Award.


Date: Thursday, January 21, 2010
Time: 4:00 - 5:00 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York

RESERVATIONS ARE NOT REQUIRED

Informal tea at 3:40 P.M.


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