Using resampling methods like cross-validation and bootstrap is a necessity in neural network design, for solving the problem of model structure selection. The bootstrap is a powerful method offering a low variance of the model generalization error estimate. Unfortunately, its computational load may be excessive when used to select among neural networks models of different structures or complex...

This paper analyzes estimation by bootstrap variable-selection in a simple Gaussian model where the dimension of the unknown parameter may exceed that of the data. A naive use of the bootstrap in this problem produces risk estimators for candidate variable-selections that have a strong upward bias. Resampling from a less overrtted model removes the bias and leads to bootstrap variable-selection...

The standard bootstrap (SBS), despite being computationally intensive, is widely used in maximum likelihood phylogenetic analyses. We recently proposed the ultrafast bootstrap approximation (UFBoot) to reduce computing time while achieving more unbiased branch supports than SBS under mild model violations. UFBoot has been steadily adopted as an efficient alternative to SBS and other bootstrap a...

The bootstrap is a nonparametric approach for calculating quantities, such as confidence intervals, directly from data. Since calculating exact bootstrap quantities is believed to be intractable, randomized resampling algorithms are traditionally used. Motivated by the fact that the variability from randomization can lead to inaccurate outputs, we propose a deterministic approach. First, we est...

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We introduce two new bootstraps for exchangeable random graphs. One, the “empirical graphon bootstrap”, is based purely on resampling, while other, “histogram a model-based “sieve” bootstrap. show that both of them accurately approximate sampling distributions motif densities, i.e., normalized counts number times fixed subgraphs appear in network. These densities characterize distribution (infi...

This work deals with global statistical unsupervised segmentation algorithms. In the context of Magnetic Resonance Image (MRI), an accurate and robust segmentation can be achieved by combining both the Hidden Markov Random Field (HMRF) model and the Expectation-Maximization (EM) algorithm. This EM–HMRF approach is accomplished by taking into account spatial information to improve the segmentati...

The resampling-based test, which often relies on permutation or bootstrap procedures, has been widely used for statistical hypothesis testing when the asymptotic distribution of the test statistic is unavailable or unreliable. It requires repeated calculations of the test statistic on a large number of simulated data sets for its significance level assessment, and thus it could become very comp...