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 | |
| Website | http://www.stat.wisc.edu/~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.[1] She is a pioneer in methods for smoothing noisy data. Best known for the development of generalized cross-validation[2] and "Wahba's problem",[1] she has developed methods with applications in demographic studies, machine learning, DNA microarrays, risk modeling, medical imaging, and climate prediction.
- ^ a b "Breaking ground with Grace". news.wisc.edu. Retrieved 2018-11-16.
- ^ Craven, Peter; Wahba, Grace (1978-12-01). "Smoothing noisy data with spline functions". Numerische Mathematik. 31 (4): 377–403. doi:10.1007/BF01404567. ISSN 0945-3245. S2CID 14094416.