WebGrace Wahba (née Goldsmith, born August 3, 1934), I. J. Schoenberg-Hilldale Professor of Statistics at the University of Wisconsin-Madison (Emerita), is a pioneer in methods for smoothing noisy data. Her research combines theoretical analysis, computation and methodology motivated by innovative scientific applications. WebGrace Wahba is the I. J. Schoenberg-Hilldale Professor of Statistics at the University of Wisconsin–Madison. Education She was educated at Cornell (Bachelor 1956), University of Maryland, College Park (Master of Arts 1962) and Stanford (Doctor of Philosophy 1966), and worked in industry for several years before receiving her doctorate in 1966 ...
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WebMay 14, 2024 · Wahba Lecture: Michael Jordan. May 14, 2024. Michael I. Jordan. Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive ... WebProfessor of Biostatistics and Medical Informatics, and Professor of Computer Sciences (by courtesy). Department of Statistics. University of Wisconsin-Madison. Medical Science Center. 1300 University Ave. Madison, WI 53706 USA. e-mail wahba "at" stat.wisc.edu (replace "at" with the "at" symbol). clicks pinelands contact number
Conference on Nonparametric Statistics for Big Data
WebSep 1, 1990 · Grace Wahba. SIAM, Sep 1, 1990 - Mathematics - 181 pages. 1 Review. Reviews aren't verified, but Google checks for and removes fake content when it's identified. This book serves well as an... WebJan 14, 2024 · Interview with Grace Wahba, The P zer Distinguished Statistician Colloquium Series, Uni-versity of Connecticut, Storrs, CT, September 26{27, 2024. Dimensionality Reduction for Exponential Family Data, Computational Strategies for Large-Scale Statistical Data Analysis Workshop, International WebNov 15, 2024 · We present solutions to the penalized least squares and penalized likelihood for nonparametric regression and support vector machines for classification as a solution to the penalized hinge loss. We discuss extensions of the representer theorem for regression with functional data. clicks pittsburgh