Prof. Grace Wahba, I.J. Schoenberg-Hilldale Emerita Professor of Statistics
University of Wisconsin-Madison
We are concerned with the use of personal density functions or personal sample densities as subject attributes in prediction and classification models. The situation is particularly interesting when it is desired to combine other attributes with the personal densities in a prediction or classification model.
The procedure is (for each subject) to embed their sample density into a Reproducing Kernel Hilbert Space (RKHS), use this embedding to estimate pairwise distances between densities, use Regularized Kernel Estimation (RKE) with the pairwise distances to embed the subject (training) densities into a Euclidean space, and use the Euclidean coordinates as attributes in a Smoothing Spline ANOVA (SSANOVA) model. Elementary expository introductions to RKHS, RKE and SSANOVA occupy most of this talk.
Dr. Grace Wahba is an American statistician and now-retired I. J. Schoenberg-Hilldale Professor of Statistics at the University of Wisconsin–Madison. She is a pioneer in methods for smoothing noisy data. Best known for the development of generalized cross-validation and “Wahba’s problem,” she has developed methods with applications in demographic studies, machine learning, DNA microarrays, risk modeling, medical imaging, and climate prediction.
Dr. Wahba is a member of the National Academy of Sciences and a fellow of several academic societies including the American Academy of Arts and Sciences, the American Association for the Advancement of Science, the American Statistical Association, and the Institute of Mathematical Statistics. Over the years she has received a selection of notable awards in the statistics community:
– R. A. Fisher Lectureship, COPSS, August 2014
– Gottfried E. Noether Senior Researcher Award, Joint Statistics Meetings, August 2009
– Committee of Presidents of Statistical Societies Elizabeth Scott Award, 1996
– First Emanuel and Carol Parzen Prize for Statistical Innovation, 1994