Grace Wahba
Grace Goldsmith Wahba (born August 3, 1934) is an American statistician and 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.
Grace Wahba | |
|---|---|
Grace Wahba in 1986 | |
| Born | August 3, 1934 |
| Nationality | American |
| Alma mater | Stanford University University of Maryland, College Park Cornell University |
| Known for | generalized cross validation, smoothing splines |
| Scientific career | |
| Fields | Mathematics, statistics, machine learning |
| Institutions | University of Wisconsin–Madison |
| Thesis | Cross Spectral Distribution Theory for Mixed Spectra and Estimation of Prediction Filter Coefficients |
| Doctoral advisor | Emanuel Parzen |
| Doctoral students |
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| Website | http://www.stat.wisc.edu/~wahba/ |
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