Cross‐Validatory Choice and Assessment of Statistical Predictions

By M. Stone. 1976

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Citations in the corpus

Deep learning in neural networks: An overview (2014, methodology): “…based on validation sets (Craven & Wahba, 1979; Eubank, 1988; Golub, Heath, & Wahba, 1979; Hastie & Tibshirani, 1990; Mosteller & Tukey, 1968; Stone, 1974), Akaike’s information criterion and final prediction error (Akaike, 1970, 1973, 1974), or generalized prediction error (Moody, 1992;…”
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics (2020, background): “The Double Estimator (DE) (Stone, 1974; Van Hasselt, 2013) 2017) assumed a certain spatial correlation and extended WE to continuous sets of random variables.”
Aggregating regression procedures to improve performance (2004, methodology): “…by Buckland et al. (1997), cross-validation based ‘stacking’ by Wolpert (1992) and Breiman (1996) (an earlier 1350–7265 # 2004 ISI/BS version is in Stone 1974), a bootstrap based method by LeBlanc and Tibshirani (1996), a stochastic approximation based method by Juditsky and Nemirovski (2000),…”
Using Partial Least Squares in Operations Management Research: A Practical Guideline and Summary of Past Research (2012, methodology): “Stone–Geisser’s Q2 Geisser, 1975; Stone, 1974) is often used to assess predictive releance and can be calculated using the blindfolding procedure, which s available in most PLS software packages.”
Bayesian classification for the selection of in vitro human embryos using morphological and clinical data (2008, methodology): “At each step the model adds the most accurate vari able calculated by estimated accuracy [28] and stops when no improvement is obtained.”
Cross-conformal predictors (2012, methodology): “Motivated by the method of cross-validation [11, 13], this note explores a hybrid method, which we call cross-conformal prediction.”
Functional Principal Component Regression and Functional Partial Least Squares (2007, unknown): “For FPCRC and FPLSC , as discussed earlier, λ is also chosen by CV, resulting in the “double-cross” (Stone 1974).”
Estimation of prediction error by using K-fold cross-validation (2011, methodology): “Although leave-oneout cross-validation is studied in many researches (for example, Stone 1974, 1977; Efron 2004), K-fold cross-validation may be preferred from a computational standpoint.”
Foundations of Linear and Generalized Linear Models (2015, methodology): “Bias corrections are possible, for example, by using cross-validation (Stone 1974) or the jackknife (Zheng and Agresti 2000).”