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Cross-Validation (CV), and out-of-sample performance-estimation protocols in general, are often employed both for (a) selecting the optimal combination of algorithms and values of hyper-parameters (called a configuration) for producing the final predictive model, and (b) estimating the predictive performance of the final model. However, the cross-validated performance of the best configuration ...

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle cl...

Testing for a unit root in a series obtained by summing a stationary MA(1) process with a parameter close to -1 leads to serious size distortions under the null, on account of the near cancellation of the unit root by the MA component in the driving stationary series. The situation is analysed from the point of view of bootstrap testing, and an exact quantitative account is given of the error i...

The robust lasso-type regularized regression is a useful tool for simultaneous estimation and variable selection even in the presence of outliers. Crucial issues in the robust modeling procedure include the selection of regularization parameters and also a tuning constant in outlier detection. Although the performance of the robust sparse regression strongly depends on the proper choice of thes...

The bootstrap provides a simple and powerful means of assessing the quality of estimators. However, in settings involving large datasets—which are increasingly prevalent— the computation of bootstrap-based quantities can be prohibitively demanding computationally. While variants such as subsampling and the m out of n bootstrap can be used in principle to reduce the cost of bootstrap computation...

Simulation methods, in particular Efron's (1979) bootstrap, are being applied more and more widely in statistical inference. Given data, (X1,* ,Xn), distributed according to P belonging to a hypothesized model P the basic goal is to estimate the distribution Lp of a function Tn (X1, * *Xn,P). The bootstrap presupposes the existence of an estimate P (X1, Xn) and consists of estimating Lp by the ...

Robust model selection procedures control the undue influence that outliers can have on the selection criteria by using both robust point estimators and a bounded loss function when measuring either the goodness-of-fit or the expected prediction error of each model. Furthermore, to avoid favoring over-fitting models, these two measures can be combined with a penalty term for the size of the mod...

This paper develops a novel and e¤ective bootstrap method for simulating asymptotic critical values for tests of equal forecast accuracy and encompassing among many nested models. The bootstrap, which combines elements of …xed regressor and wild bootstrap methods, is simple to use. We …rst derive the asymptotic distributions of tests of equal forecast accuracy and encompassing applied to foreca...