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Review

2010

Review

2010

Used to estimate the risk of an estimator or to perform model selection, cross-validation is a widespread strategy because of its… Expand

Highly Cited

2010

Highly Cited

2010

Model selection strategies for machine learning algorithms typically involve the numerical optimisation of an appropriate model… Expand

Highly Cited

2006

Highly Cited

2006

Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences… Expand

Review

2003

Review

2003

chemists. Commenting on the new material in the second edition (2E), which was published in 1991, Blackwood (1994) noted the… Expand

Highly Cited

2001

Highly Cited

2001

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

Highly Cited

1998

Highly Cited

1998

The first € price and the £ and $ price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the… Expand

Highly Cited

1998

Highly Cited

1998

Introduction to model selection the univariate regression model the univariate autoregressive model the multivariate regression… Expand

Highly Cited

1997

Highly Cited

1997

We argue that model selection uncertainty should be fully incorporated into statistical inference whenever estimation is… Expand

Review

1995

Review

1995

We review accuracy estimation methods and compare the two most common methods crossvalidation and bootstrap. Recent experimental… Expand

Highly Cited

1989

Highly Cited

1989

SUMMARY A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series… Expand