On substituting simulation for analysis in model selection, in e.g. choosing the “right” regularisation parameter for sparse regression.
Asymptotically equivalent to generalised Akaike information criteria. (e.g. Ston77) Related to bootstrap in various ways.
The computationally expensive default option when your model doesn’t have any obvious short cuts for complexity regularization. For example, I think that the AIC for penalised reqgression requires penalties twice-differentiable at the optimum. I’m not sure they couldn’t be made to work, however. should investigate.
Present alternatives, especially outside n-fold cross-validation, especially computationally tractable ones. Methods based on statistical learning theory or concentration inequalities win gratitude.
Basic Cross Validation
Generalised Cross Validation
Why the name? It’s specialised cross-validation, AFAICT.
TBD. Hat matrix, smoother matrix. Note comparative computational efficiency. Define hat matrix.
- Ston77: M. Stone (1977) An Asymptotic Equivalence of Choice of Model by Cross-Validation and Akaike’s Criterion. Journal of the Royal Statistical Society. Series B (Methodological) , 39(1), 44–47.
- Li87: Ker-Chau Li (1987) Asymptotic Optimality for \(C_p, C_L\), Cross-Validation and Generalized Cross-Validation: Discrete Index Set. The Annals of Statistics, 15(3), 958–975. DOI
- Andr91: Donald W. K. Andrews (1991) Asymptotic optimality of generalized CL, cross-validation, and generalized cross-validation in regression with heteroskedastic errors. Journal of Econometrics, 47(2), 359–377. DOI
- HaRL04: Peter Hall, Jeff Racine, Qi Li (2004) Cross-validation and the estimation of conditional probability densities. Journal of the American Statistical Association, 99(468), 1015–1026. DOI
- GoHW79: Gene H. Golub, Michael Heath, Grace Wahba (1979) Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter. Technometrics, 21(2), 215–223. DOI
- Wood94: S. Wood (1994) Monotonic Smoothing Splines Fitted by Cross Validation. SIAM Journal on Scientific Computing, 15(5), 1126–1133. DOI