The Predictive Sample Reuse Method with Applications

By S. Geisser. 1975

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In Statistics and Probability

Citations in the corpus

True Few-Shot Learning with Language Models (2021, methodology): “Cross-Validation (CV) [22–24] is one of the most widely used methods for estimating generalization loss [25].”
Stochastic variational inference (2012, methodology): “We evaluate how well a model ﬁts the data with the predictive distribution (Geisser, 1975).”
Time-varying model averaging (2020, unknown): “The jackknife (or CV) criterion is widely used in selecting regression models (e.g., Allen (1974), Stone (1974) nd Geisser (1975)), and the asymptotic optimality of model selection using the CV criterion is established by Li (1987) or homoskedastic regression and by Andrews (1991) for…”
An Empirical Evaluation of Explanations for State Repression (2014, methodology): “…is a well-developed and widely accepted method for assessing the relative predictive performance of statistical models (see, e.g., Efron 1983; Geisser 1975; Hastie, Tibshirani, and Friedman 2008; Picard and Cook 1984), though its use is relatively rare in political science.3 The…”
Jackknife model averaging (2012, methodology): “(Delete-one) cross-validation for selection of regression models was introduced by Allen (1974), Stone (1974), Geisser (1974) and Wahba and Wold (1975), and its optimality demonstrated by Li (1987) for homoskedastic regression and Andrews (1991) for heteroskedastic regression.”
Structural Equation Modeling in Information Systems Research Using Partial Least Squares (2010, methodology): “Stone (1974), Geisser (1975), Fornell and Cha (1994) The different criteria for assessing a PLS model on the structural level are summarized in Table 6.”
Inferring Welfare Maximizing Treatment Assignment Under Budget Constraints (2012, methodology): “, Geisser (1975)) — where the sample is split into V disjoint subsamples randomly; then each of the V subsamples is used for validation and the result averaged across them.”
Multiple predicting K-fold cross-validation for model selection (2018, methodology): “To reduce computational cost of LOOCV, Geisser (1975) proposed K -fold CV.”
A survey of cross-validation procedures for model selection (2009, unknown): “Leave-one-out (LOO, Stone, 1974; Allen, 1974; Geisser, 1975) is the most classical exhaustive CV procedure, corresponding to the choice n t = n 1 : Each data point is successively “left out” from the sample and used for validation.”
Predictive inference with the jackknife+ (2019, background): “Several papers from this period include leave-one-out methods for assessing or calibrating predictive accuracy, similar to the predictive interval constructed in (1.2) above, for example, Stone [24], Geisser [10], Butler and Rothman [4], generally using the term “cross-validation” to refer to this a”
The first and second data releases of the Kilo-Degree Survey (2015, methodology): “In the method used here the validation is embedded into the training phase, by means of the standard leave-one-out k-fold cross validation mechanism (Geisser 1975).”
Geostatistical modelling of air temperature in a mountainous region of Northern Spain (2007, methodology): “To compare the performance of the five geostatistical methods, the results of the leave-one-out crossvalidation were analysed (Stone, 1974; Geisser, 1975), as well as the distribution of the prediction errors in the estimation points.”