We provide simple formulas that can be used to calculate ideal bootstrap or exact recombination estimates of group statistics from experimental data. When resampling is done over small numbers of groups, the ideal bootstrap is more accurate than the exact recombinant estimate, however the former is biased. For large sample sizes there is no discernable difference between the two approaches and ...

In this talk, I present some theoretical and empirical properties of the uniform and biased-bootstrap for generalized method of moments (GMM) models. The version of the biased-bootstrap used in this paper is a form of weighted bootstrap with weights chosen to satisfy some constraints imposed by the model. A typical biased-bootstrap resample is obtained by resampling from a member within a pseud...

We develop some asymptotic theory for applications of block bootstrap resampling schemes to multivariate integrated and cointegrated time series. It is proved that a multivariate, continuous-path block bootstrap scheme applied to a full rank integrated process, succeeds in estimating consistently the distribution of the least squares estimators in both, the regression and the spurious regressio...

This paper assesses the finite sample refinements of the block bootstrap and the Non-Parametric Bootstrap for conditional moment models. The study recononsiders inference in the generalized method of moments estimation of the consumption asset pricing model of Singleton (1986). These dependent bootstrap resampling schemes are proposed as an alternative to the asymptotic approximation in small s...

The bootstrap methodology for functional data and functional estimation target is considered. A Monte Carlo study analyzing the performance of the bootstrap confidence bands (obtained with different resampling methods) of several functional estimators is presented. Some of these estimators (e.g., the trimmed functional mean) rely on the use of depth notions for functional data and do not have r...

Behavioral Partial-Least Squares (PLS) is often used to analyze ill-posed functional Magnetic Resonance Imaging (f MRI) datasets, for which the number of variables are far larger than the number of observations. This procedure generates a latent variable (LV) brain map, showing brain regions that are most correlated with behavioral measures. The strength of the behavioral relationship is measur...

S U M M A R Y A new kind of stacking scheme, based on the hypothesis testing of signal significance and coherence, is proposed. The significance of stacked data is evaluated by running two kinds of bootstrap resampling, one for standard bootstrap and the other for preparing noise stacks by scrambling relative time-shifts between traces. This dual bootstrap procedure allows us to formulate a two...

MANY students of statistics, especially those who are not studying mathematics, have difficulty with the mathematical formulation of statistical methods. The theory of statistics is largely based upon the theory of probability distributions and sampling distributions of parameters such as the mean or test statistics such as chi-squared. Because each approach uses a different formula, many stude...