The “Minimum Reconstruction Error” Choice of Regularization Parameters: Some More Efficient Methods and Their Application to Deconvolution Problems

This publication has 14 references indexed in Scilit:

• Asymptotic Optimality of the Fast Randomized Versions of GCV and $C_L$ in Ridge Regression and Regularization
  The Annals of Statistics, 1991
• A fast ?Monte-Carlo cross-validation? procedure for large least squares problems with noisy data
  Numerische Mathematik, 1989
• How Far are Automatically Chosen Regression Smoothing Parameters from their Optimum?
  Journal of the American Statistical Association, 1988
• Optimal Regularized Reconstruction in Computerized Tomography
  SIAM Journal on Scientific and Statistical Computing, 1987
• Improved estimates of statistical regularization parameters in fourier differentiation and smoothing
  Numerische Mathematik, 1986
• Asymptotic Optimality of $C_L$ and Generalized Cross-Validation in Ridge Regression with Application to Spline Smoothing
  The Annals of Statistics, 1986
• A note on the computation of the generalized cross-validation function for ill-conditioned least squares problems
  BIT Numerical Mathematics, 1984
• Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
  Technometrics, 1979
• Smoothing noisy data with spline functions
  Numerische Mathematik, 1978
• Algorithms for the regularization of ill-conditioned least squares problems
  BIT Numerical Mathematics, 1977