One situation that arises in the field of functional data analysis is the use of imaging data or other very high dimensional data as predictors in regression models. A motivating example involves using baseline images of a patient's brain to predict the patient's clinical outcome. Interest lies both in making such patient-specific predictions and in understanding the relationship between the imaging data and the outcome. Obtaining meaningful fits in such problems requires some type of dimension reduction but this must be done while taking into account the particular (spatial) structure of the data. This talk will describe some of the general tools that have proven effective in this context, including principal component analysis, penalized splines, and wavelet analysis.
More information about R. Todd Ogden may be found at http://www.columbia.edu/~to166/