Cited by 10,881Statistical Methods and InferenceStatistical Methods and ApplicationsAdvanced Statistical Methods and Models

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Cross validatory choice and assessment of statistical predictions

Stone, M.·1974·Journal of the Royal Statistical Society Series B: Statistical Methodology

DOI 10.1111/j.2517-6161.1974.tb00994.x

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Abstract

Summary A generalized form of the cross-validation criterion is applied to the choice and assessment of prediction using the data-analytic concept of a prescription. The examples used to illustrate the application are drawn from the problem areas of univariate estimation, linear regression and analysis of variance.

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Crossref · 10.1111/j.2517-6161.1974.tb00994.x

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The Handbook of Social Psychology
Gravatt, A. E., Lindzey, G. E., & Aronson, F. (1969) · The Family Coordinator · doi:10.2307/582242 · Cited by 12,518
Reviews: Lord, Frederic M., and Novick, Melvin R. (with contributions by Allan Birnbaum). Statistical Theories of Mental Test Scores. Reading, Mass.: Addison-Wesley, 1968. xii + 568 pp. $14.95.
Rozeboom, W. W., Lord, F. M., Novick, M. R., & Birnbaum, A. (1969) · American Educational Research Journal · doi:10.2307/1162101 · Cited by 8,012
Optimal Statistical Decisions.
Siegmund, D., & DeGroot, M. H. (1972) · Journal of the American Statistical Association · doi:10.2307/2284675 · Cited by 3,436
A Predictive Approach to the Random Effect Model
Geisser, S. (1974) · Biometrika · doi:10.1093/biomet/61.1.101 · Cited by 3,101
Bayes Estimates for the Linear Model
Lindley, D. V., & Smith, A. F. M. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00885.x · Cited by 2,025
Estimation of Error Rates in Discriminant Analysis
Lachenbruch, P. A., & Mickey, M. R. (1968) · Technometrics · doi:10.1080/00401706.1968.10490530 · Cited by 1,560
The Relationship Between Variable Selection and Data Agumentation and a Method for Prediction
Allen, D. M. (1974) · Technometrics · doi:10.1080/00401706.1974.10489157 · Cited by 1,469
Inference and disputed authorship : The Federalist
Mosteller, F. C., & Wallace, D. K. (1964) · Medical Entomology and Zoology · Cited by 916
Mean Square Error of Prediction as a Criterion for Selecting Variables
Allen, D. M. (1971) · Technometrics · doi:10.1080/00401706.1971.10488811 · Cited by 687
Robust Estimates of Location
Robertson, C., Andrews, D. F., Bickel, P. J., Hanpel, F. R., Huber, P. J., Rogers, W. H., & Tukey, J. W. (1972) · Biometrics · doi:10.2307/2528653 · Cited by 462

Show all 47 references

Research works printed but not linked: 6

Anderson, 1972, Selection of predictor variables in linear multiple regression
Mosteller, 1968, Data analysis, including statistics
Nicholson, 1960, Prediction in future samples
Nimmo-Smith, 1972, Selecting an estimator from a class of estimator types: An embryo data-analytic technique
Simon, 1971, Prediction Methods in Criminology. Home Office Research Study, 7
Stein, 1960, Multiple regression

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Authors: Stone, M.
Affiliations: University College London (GB)
Corresponding author: Stone, M.
Venue: Journal of the Royal Statistical Society Series B: Statistical Methodology
Publisher: Oxford University Press (OUP)
Published: 1974-01-01
Volume: 36
Issue: 2
Pages: 111-133
ISSN: 1369-7412 (ISSN-L)
1467-9868
DOI: 10.1111/j.2517-6161.1974.tb00994.x
OpenAlex: W2112081648
Cited by: 10,881
References: 47
Citations by year: 2026: 812 · 2025: 680 · 2024: 654 · 2023: 758 · 2022: 711 · 2021: 828 · 2020: 638 · 2019: 542 · 2018: 436 · 2017: 376 · 2016: 340 · 2015: 350 · 2014: 314 · 2013: 297 · 2012: 285
FWCI: 17.01
Type: journal article
Language: en
Open access: closed
Licence: academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
Locations: Journal of the Royal Statistical Society Series B (Statistical Methodology) (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
Keywords: cross-validation, statistical forecasting, drug prescribing, model selection, linear regression, analysis of variance

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Cross validatory choice and assessment of statistical predictions

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