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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… Expand Model selection strategies for machine learning algorithms typically involve the numerical optimisation of an appropriate model… Expand Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences… Expand chemists. Commenting on the new material in the second edition (2E), which was published in 1991, Blackwood (1994) noted the… Expand Given a data set, you can fit thousands of models at the push of a button, but how do you choose the best? With so many candidate… Expand The first € price and the £ and $ price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the… Expand Introduction to model selection the univariate regression model the univariate autoregressive model the multivariate regression… Expand We argue that model selection uncertainty should be fully incorporated into statistical inference whenever estimation is… Expand We review accuracy estimation methods and compare the two most common methods crossvalidation and bootstrap. Recent experimental… Expand SUMMARY A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series… Expand