World's Best Scientists 2026 revealed!

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Mathematics

D-Index
66
Citations
45029
World Ranking
347
National Ranking
188

Research.com Recognitions

  • 2009 - SIAM Fellow For advances in the analysis of experimental data.
  • 2003 - Wald Memorial Lecturer
  • 2000 - Member of the National Academy of Sciences
  • 1997 - Fellow of the American Academy of Arts and Sciences
  • 1986 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 1980 - Fellow of the American Statistical Association (ASA)

Overview

Grace Wahba is affiliated with the University of Wisconsin-Madison in the United States and conducts research primarily in the field of engineering. Their work spans several subfields, notably mechanical engineering and computational mechanics.

The scientist's research topics focus on advanced numerical analysis techniques, advanced measurement and metrology techniques, and advanced machining processes and optimization. These topics indicate a strong emphasis on both theoretical methods and practical applications within engineering disciplines.

The specific subfields of study include:

  • Mechanical Engineering
  • Computational Mechanics

Their main fields of study consist of:

  • Engineering

The main research topics are:

  • Advanced Numerical Analysis Techniques
  • Advanced Measurement and Metrology Techniques
  • Advanced machining processes and optimization

Grace Wahba has received several honors throughout their career. These include being named a SIAM Fellow in 2009, recognized specifically for advances in the analysis of experimental data. They were the Wald Memorial Lecturer in 2003 and elected as a Member of the National Academy of Sciences in 2000.

Additional distinctions include fellowships in prestigious organizations: the American Academy of Arts and Sciences (1997), the American Association for the Advancement of Science (AAAS) in 1986, and the American Statistical Association (ASA) in 1980.

Best Publications

  • Spline models for observational data

    Grace Wahba

  • Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter

    Gene H. Golub;Michael Heath;Grace Wahba

  • Smoothing Noisy Data with Spline Functions Estimating the Correct Degree of Smoothing by the Method of Generalized Cross-Validation*

    Peter Craven;Grace Wahba

  • Smoothing noisy data with spline functions

    Grace Wahba

  • Some results on Tchebycheffian spline functions

    George S. Kimeldorf;Grace Wahba

  • A Least Squares Estimate of Satellite Attitude

    Grace Wahba

  • A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines

    George S. Kimeldorf;Grace Wahba

  • Multicategory Support Vector Machines

    Yoonkyung Lee;Yi Lin;Grace Wahba

  • Practical Approximate Solutions to Linear Operator Equations When the Data are Noisy

    Grace Wahba

  • Bayesian "Confidence Intervals" for the Cross-validated Smoothing Spline

    Grace Wahba

  • Improper Priors, Spline Smoothing and the Problem of Guarding Against Model Errors in Regression

    Grace Wahba

  • Some New Mathematical Methods for Variational Objective Analysis Using Splines and Cross Validation

    Grace Wahba;James Wendelberger

  • A Comparison of GCV and GML for Choosing the Smoothing Parameter in the Generalized Spline Smoothing Problem

    Grace Wahba

  • A completely automatic french curve: fitting spline functions by cross validation

    G. Wahba;S. Wold

  • Support Vector Machines for Classification in Nonstandard Situations

    Yi Lin;Yoonkyung Lee;Grace Wahba

  • Spline Interpolation and Smoothing on the Sphere

    Grace Wahba

  • A NOTE ON THE LASSO AND RELATED PROCEDURES IN MODEL SELECTION

    Chenlei Leng;Yi Lin;Grace Wahba

  • Smoothing spline ANOVA for exponential families, with application to the Wisconsin Epidemiological Study of Diabetic Retinopathy : the 1994 Neyman Memorial Lecture

    Grace Wahba;Yuedong Wang;Chong Gu;Ronald Klein

  • Automatic Smoothing of the Log Periodogram

    Grace Wahba

  • Minimizing GCV/GML Scores with Multiple Smoothing Parameters via the Newton Method.

    Chong Gu;Grace Wahba

  • Some new mathematical methods for variational objective analysis

    Grace Wahba;Donald R. Johnson

Frequent Co-Authors

Barbara E. K. Klein
Barbara E. K. Klein University of Wisconsin–Madison
Ronald Klein
Ronald Klein University of Wisconsin–Madison
Stephen J. Wright
Stephen J. Wright University of Wisconsin–Madison
Sterling C. Johnson
Sterling C. Johnson University of Wisconsin–Madison
Vikas Singh
Vikas Singh University of Wisconsin–Madison
Michael T. Heath
Michael T. Heath University of Illinois at Urbana-Champaign
Moo K. Chung
Moo K. Chung University of Wisconsin–Madison
Ming Yuan
Ming Yuan Columbia University
Gene H. Golub
Gene H. Golub Stanford University
Douglas M. Bates
Douglas M. Bates University of Wisconsin–Madison

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