| Spline models for observational data G Wahba Society for industrial and applied mathematics, 1990 | 11101 | 1990 |
| Generalized cross-validation as a method for choosing a good ridge parameter GH Golub, M Heath, G Wahba Technometrics 21 (2), 215-223, 1979 | 5545 | 1979 |
| Smoothing noisy data with spline functions: estimating the correct degree of smoothing by the method of generalized cross-validation P Craven, G Wahba Numerische mathematik 31 (4), 377-403, 1978 | 5506 | 1978 |
| Some results on Tchebycheffian spline functions G Kimeldorf, G Wahba Journal of mathematical analysis and applications 33 (1), 82-95, 1971 | 2085 | 1971 |
| A least squares estimate of satellite attitude G Wahba SIAM review 7 (3), 409-409, 1965 | 1793 | 1965 |
| A correspondence between Bayesian estimation on stochastic processes and smoothing by splines GS Kimeldorf, G Wahba The Annals of Mathematical Statistics 41 (2), 495-502, 1970 | 1402 | 1970 |
| Multicategory support vector machines: Theory and application to the classification of microarray data and satellite radiance data Y Lee, Y Lin, G Wahba Journal of the American Statistical Association 99 (465), 67-81, 2004 | 1154 | 2004 |
| Practical approximate solutions to linear operator equations when the data are noisy G Wahba SIAM journal on numerical analysis 14 (4), 651-667, 1977 | 1142 | 1977 |
| Bayesian “confidence intervals” for the cross‐validated smoothing spline G Wahba Journal of the Royal Statistical Society: Series B (Methodological) 45 (1 …, 1983 | 968 | 1983 |
| Improper priors, spline smoothing and the problem of guarding against model errors in regression G Wahba Journal of the Royal Statistical Society Series B: Statistical Methodology …, 1978 | 896 | 1978 |
| Some new mathematical methods for variational objective analysis using splines and cross validation G Wahba, J Wendelberger Monthly weather review 108 (8), 1122-1143, 1980 | 782 | 1980 |
| A comparison of GCV and GML for choosing the smoothing parameter in the generalized spline smoothing problem G Wahba The annals of statistics, 1378-1402, 1985 | 754 | 1985 |
| Support vector machines, reproducing kernel Hilbert spaces and the randomized GACV G Wahba Advances in Kernel Methods-Support Vector Learning 6, 69-87, 1999 | 734 | 1999 |
| A completely automatic french curve: fitting spline functions by cross validation G Wahba, S Wold Communications in Statistics-Theory and Methods 4 (1), 1-17, 1975 | 673 | 1975 |
| Support vector machines for classification in nonstandard situations Y Lin, Y Lee, G Wahba Machine learning 46 (1), 191-202, 2002 | 577 | 2002 |
| Spline interpolation and smoothing on the sphere G Wahba SIAM Journal on Scientific and Statistical Computing 2 (1), 5-16, 1981 | 573 | 1981 |
| Smoothing noisy data with spline functions G Wahba Numerische mathematik 24 (5), 383-393, 1975 | 509 | 1975 |
| A note on the lasso and related procedures in model selection C Leng, Y Lin, G Wahba Statistica Sinica, 1273-1284, 2006 | 443 | 2006 |
| Smoothing spline ANOVA for exponential families, with application to the Wisconsin Epidemiological Study of Diabetic Retinopathy: the 1994 Neyman Memorial Lecture G Wahba, Y Wang, C Gu, R Klein, B Klein The Annals of Statistics 23 (6), 1865-1895, 1995 | 367 | 1995 |
| Minimizing GCV/GML scores with multiple smoothing parameters via the Newton method C Gu, G Wahba SIAM Journal on Scientific and Statistical Computing 12 (2), 383-398, 1991 | 301 | 1991 |
Chong GuProfessor of Statistics, Purdue UniversityVerified email at stat.purdue.edu
