This paper establishes an invariance principle applicable for the asymptotic analysis of sieve bootstrap in time series+ The sieve bootstrap is based on the approximation of a linear process by a finite autoregressive process of order increasing with the sample size, and resampling from the approximated autoregression+ In this context, we prove an invariance principle for the bootstrap samples ...

In recent years, increasing attention has been devoted to the problem of the stability of multivariable regression models, understood as the resistance of the model to small changes in the data on which it has been fitted. Resampling techniques, mainly based on the bootstrap, have been developed to address this issue. In particular, the approaches based on the idea of "inclusion frequency" cons...

Once a regression has been fitted to data, it is usually necessary to add confidence intervals to indicate the accuracy of the fitted regression line. This can easily be done for individual explanatory variable values. However sometimes confidence limits are needed simultaneously for the whole range of explanatory variable values of interest. In other words the problem is to construct a confide...

Breast cancer is one of the most important medical problems. In this paper, we report the results of using neural networks for breast cancer diagnosis. The theoretical advantage is that posterior probabilities of malignancy can be estimated directly, and coupled with resampling techniques such as the bootstrap, distributions of the probabilities can also be obtained. These allow a researcher mu...

This paper describes the use of bootstrap and permutation methods for lhe problem of testing homogeneity of variances when means are not assumed equal or known. The melhods are new in this context, and nontrivial, since lhe composite null hypothesis involves nuisance mean parameters. They allow the use of normal-:'theory test statistics such as F = sUs~ without the normality assumption which is...

Quantitative assessment of performance in image understanding tasks with real data is di cult since the data is complex and the di erent computational modules most often interact. Employing modern statistical techniques we have developed a set of numerical tools which provide rigorous performance measures derived solely from the given input. Covariance matrices and con dence intervals are compu...

The classic methods used in estimating the parameters linear regression need to fulfill some assumptions. If assumptions are not fulfilled, conclusion is questionable. Resampling one of ways avoid such problems. study aims compare resampling techniques regression. original data clean, without any influential observations, outliers and leverage points. ordinary least square method was as primary...

The concept of the autoregressive (AR) sieve bootstrap is investigated for the case of spatial processes in Z. This procedure fits AR models of increasing order to the given data and, via resampling of the residuals, generates bootstrap replicates of the sample. The paper explores the range of validity of this resampling procedure and provides a general check criterion which allows to decide wh...

Multi-level simultaneous component analysis (MLSCA) was designed for the exploratory analysis of hierarchically ordered data. MLSCA specifies a component model for each level in the data, where appropriate constraints express possible similarities between groups of objects at a certain level, yielding four MLSCA variants. The present paper discusses different bootstrap strategies for estimating...

In this paper we study a bootstrap strategy for estimating the variance of a mean taken over large multifactor crossed random effects data sets. We apply bootstrap reweighting independently to the levels of each factor, giving each observation the product of its factor weights. No exact bootstrap exists for this problem (McCullagh, 2000). We show that the proposed bootstrap is mildly conservati...