Bootstrap approach and Stochastic EM algorithm combination applied for the improvement of the multisource and multi-sensor image fusion process; was presented in this research. Improvement concerned not only image quality and reducing processing execution time as mentioned in our previous Bootstrap EM algorithm (BEM), but also regarding initialization dependence as well as fixed classes’ number...

We have recently developed a distance metric for efficiently estimating the number of substitutions per site between unaligned genome sequences. These substitution rates are called "anchor distances" and can be used for phylogeny reconstruction. Most phylogenies come with bootstrap support values, which are computed by resampling with replacement columns of homologous residues from the original...

We introduce a nonparametric bootstrap approach for Quasi-Likelihood Ratio type tests of nonlinear restrictions. Our method applies to extremum estimators, such as quasimaximum likelihood and generalized method of moments estimators. Unlike existing parametric bootstrap procedures for Quasi-Likelihood Ratio type tests, our procedure constructs bootstrap samples in a fully nonparametric way. We ...

We examine bootstrap percolation on a regular (b + 1)-ary tree with initial law given by Bernoulli(p). The sites are updated according to the usual rule: a vacant site becomes occupied if it has at least θ occupied neighbors, occupied sites remain occupied forever. It is known that, when b > θ ≥ 2, the limiting density q = q(p) of occupied sites exhibits a jump at some pT = pT(b, θ) ∈ (0, 1) fr...

Normality is a common assumption for many quality control charts. One should expect misleading results once this assumption is violated. In order to avoid this pitfall, we need to evaluate this assumption prior to the use of control charts which require normality assumption. However, in certain cases either this assumption is overlooked or it is hard to check. Robust control charts and bootstra...

The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one’s data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstrap provides approximations to distributions of statistics, coverage probabilities of confidence intervals, and rejection probabilities of hypothesis tests that...

The bootstrap is a resampling method for statistical inference. It is commonly used to estimate confidence intervals, but it can also be used to estimate bias and variance of an estimator or calibrate hypothesis tests. A short of papers illustrative of the diversity of recent environmentric applications of the bootstrap includes toxicology [2], fisheries surveys [27], groundwater and air poluti...

The astonishing increase in computer performance over the past two decades has made it possible for economists to base many statistical inferences on simulated, or bootstrap, distributions rather than on distributions obtained from asymptotic theory. In this paper, I review some of the basic ideas of bootstrap inference. The paper discusses Monte Carlo tests, several types of bootstrap test, an...

Bootstrap tilting conndence intervals could be the method of choice in many applications for reasons of both speed and accuracy. With the right implementation , tilting intervals are 37 times as fast as bootstrap BC-a limits, in terms of the number of bootstrap samples needed for comparable simulation accuracy. Thus 100 bootstrap samples might suuce instead of 3700. Tilting limits have other de...