Model Selection Via Multifold Cross Validation
نویسندگان
چکیده
منابع مشابه
Density estimation via cross-validation: Model selection point of view
The problem of model selection by cross-validation is addressed in the density estimation framework. Extensively used in practice, cross-validation (CV) remains poorly understood, especially in the non-asymptotic setting which is the main concern of this work. A recurrent problem with CV is the computation time it involves. This drawback is overcome here thanks to closed-form expressions for th...
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In response to Zhu and Rower (1996), a recent communication (Goutte, 1997) established that leave-one-out cross validation is not subject to the "no-free-lunch" criticism. Despite this optimistic conclusion, we show here that cross validation has very poor performances for the selection of linear models as compared to classic statistical tests. We conclude that the statistical tests are prefera...
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Used to estimate the risk of an estimator or to perform model selection, cross-validation is a widespread strategy because of its simplicity and its apparent universality. Many results exist on the model selection performances of cross-validation procedures. This survey intends to relate these results to the most recent advances of model selection theory, with a particular emphasis on distingui...
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A model selection criterion is often formulated by constructing an approximately unbiased estimator of an expected discrepancy, a measure that gauges the separation between the true model and a fitted approximating model. The expected discrepancy reflects how well, on average, the fitted approximating model predicts “new” data generated under the true model. A related measure, the estimated dis...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 1993
ISSN: 0090-5364
DOI: 10.1214/aos/1176349027