American Statistical Association
New York City
Metropolitan Area Chapter

New York State Psychiatric Institute
at Columbia University Medical Center
Biostatistics Seminar



WAVELET-BASED FUNCTIONAL LINEAR REGRESSION

by

Yihong Zhao, Ph.D. Candidate

Dissertation research under the direction of

R. Todd Ogden, Ph.D.
Department of Biostatistics
Columbia University


Abstract

We introduce an approach for estimating the coefficient function in the functional linear model with scalar responses and functional predictors (e.g., curves, spectra, or images). The problem is transformed to a variable selection problem by transformation into the wavelet domain, taking advantage of the sparse representation allowed by wavelet bases. When the number of measurement points is much larger than the number of samples, we reduce the dimensionality by applying a pre-screening step followed by a variable selection step (e.g., penalized least squares).

Biographical Note

Yihong Zhao received a B.S. in Statistics with a concentration on computing from University of California at Riverside in 2003. She is a Ph.D. candidate under the guidance of Dr. R. Todd Ogden in the Department of Biostatistics at Columbia University. Her primary research interest is in the development of statistical methods for high-dimensional and functional data. She is especially interested in developing methods for linear regression of a scalar response on one-dimensional functional predictor and extending the developed methods to the regression of scalars on the high-dimensional image predictors.


Date: Tuesday, December 8, 2009
Time: 3:00 - 4:00 P.M.
Location: New York State Psychiatric Institute
1051 Riverside Drive
6th Floor Multipurpose Room (6602)
New York, New York
(Directions)

RESERVATIONS ARE NOT REQUIRED

Coffee: 2:45 to 3:00 P.M.
Reception: 4:00 to 4:30 P.M.


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