Cross-validation and bootstrap, or resampling methods in general, are examined for applying them to optimizing the number of units of competitive associative nets called CAN2. There are a number of resampling methods available, but the performance depends on the neural network to be applied and functions to be learned. So, we apply several resampling methods to the CAN2 which has been shown eff...

Bootstrap principle is briefly reviewed. Hall’s (1989) antithetic variates method for bootstrap is discussed and extended to more than two antithetic resampling processes. We illustrate the theory with a simulation study. The numerical results show that increasing the number of antithetic resampling processes produces significant smaller variances of the bootstrap estimator over the paired case.

In an extension of the work of Liu and Singh (1992), we consider resampling estimates for the variance of the least squares estimator in linear regression models. Second order terms in asymptotic expansions of these estimates are derived. By comparing the second order terms, certain generalised bootstrap schemes are seen to be theoretically better than other resampling techniques under very gen...

The present paper establishes conditional and unconditional central limit theorems for various resampling procedures for the t-statistic. The results work under fairly general conditions and the underlying random variables need not to be independent. Specific examples are then the 're(n) (double) bootstrap out of k(n) observations, the Bayesian bootstrap and two-sample t-type permutation statis...

SUMMARY Pvclust is an add-on package for a statistical software R to assess the uncertainty in hierarchical cluster analysis. Pvclust can be used easily for general statistical problems, such as DNA microarray analysis, to perform the bootstrap analysis of clustering, which has been popular in phylogenetic analysis. Pvclust calculates probability values (p-values) for each cluster using bootstr...

Bootstrap techniques (also called resampling computation techniques) have introduced new advances in modeling and model evaluation [10]. Using resampling methods to construct a series of new samples which are based on the original data set, allows to estimate the stability of the parameters. Properties such as convergence and asymptotic normality can be checked for any particular observed data ...

Resampling-based multiple testing procedures are widely used in genomic studies to identify differentially expressed genes and to conduct genome-wide association studies. However, the power and stability properties of these popular resampling-based multiple testing procedures have not been extensively evaluated. Our study focuses on investigating the power and stability of seven resampling-base...

Bootstrap is an empirical non-parametric statistical technique based on data resampling that has been used to quantify uncertainties of diffusion tensor MRI (DTI) parameters, useful in tractography and in assessing DTI methods. The current bootstrap method (repetition bootstrap) used for DTI analysis performs resampling within the data sharing common diffusion gradients, requiring multiple acqu...