Overview

We assume no knowledge of reproducing kernel Hilbert spaces, but review some basic concepts, with a view towards demonstrating how this setting allows the building of interesting statistical models that allow the simultaneous analysis of heterogeneous, scattered observations, and other information. The abstract ideas will be illustrated with several specific data analyses, including modeling risk factors for eye diseases and examining historical climate data for signals of greenhouse warming. Recent numerical and statistical methods appropriate for very large data sets will be discussed.

Biography

Grace Wahba is the John Bascom Professor of Statistics and Professor of Biostatistics at the University of Wisconsin, Madison. She is a Fellow of the Institute of Mathematical Statistics, The American Statistical Association, and the American Association for the Advancement of Science, and was recently elected to the American Academy of Arts and Sciences. She received the first Emanuel and Carol Parzen Prize for Statistical Innovation, the COPSS Elizabeth Scott Award, and the International Meetings on Statistical Climatology Achievement Award. Her research involves multivariate function estimation and model building with heterogeneous sources of information with applications in numerical weather prediction, climate, biostatistical model building and risk factor estimation, and supervised machine learning. She is most proud of her many and talented former students.