• There is a vast literature on robust estimators, but in some situations it is still not easy to make inferences, such as confidence regions and hypothesis testing. This is mainly due to the following facts. On one hand, in most situations, it is difficult to derive the exact distribution of the estimator. On the other one, even if its asymptotic behaviour is known, in many cases, the converge...

Ever since its introduction, the bootstrap has provided both a powerful set of solutions for practical statisticians, and a rich source of theoretical and methodological problems for statistics. In this article, some recent developments in bootstrap methodology are reviewed and discussed. After a brief introduction to the bootstrap, we consider the following topics at varying levels of detail: ...

Robust estimators of the prediction error of a linear model are proposed. The estimators are based on the resampling techniques cross-validation and bootstrap. The robustness of the prediction error estimators is obtained by robustly estimating the regression parameters of the linear model and by trimming the largest prediction errors. To avoid the recalculation of timeconsuming robust regressi...

We consider the problem of resampling or bootstrapping a point process to get con dence intervals for the reduced second moment function We propose a resampling scheme for spatial data which we call the marked point method This is a variant of the block of blocks bootstrap rst introduced by K unsch A simulation study with a Poisson a clustered and a regular point process on the unit square in R...

Kernel smoothing in nonparametric autoregressive schemes ooers a powerful tool in modelling time series. In this paper it is shown that the bootstrap can be used for estimating the distribution of kernel smoothers. This can be done by mimicking the stochastic nature of the whole process in the bootstrap resampling or by generating a simple regression model. Consistency of these bootstrap proced...

Bootstrapping is a famous statistical tool that involves resampling procedure to select sample from a population. In this study, we applied randomx bootstrap in binary logistic regression for published data set namely Umaru Impact data. We conducted bootstrap for the coefficient by using SAS (Statistical Analysis System). We observe the distribution of the estimated coefficients with different ...

The contemporary development of bootstrap methods, from the time of Efron’s early articles to the present day, is well documented and widely appreciated. Likewise, the relationship of bootstrap techniques to certain early work on permutation testing, the jackknife and cross-validation is well understood. Less known, however, are the connections of the bootstrap to research on survey sampling fo...

The bootstrap method is a nonparametric statistical that provides through resampling the input data set to obtain new normally distributed. Due various factors, deep geological are difficult many set, and in most cases, they not Therefore, it necessary introduce tool will enable obtaining with which analyses can be done. was applied field "A", reservoir "L" located western part of Sava Depressi...

A resampling scheme for clustering with similarity to bootstrap aggregation (bagging) is presented. Bagging is used to improve the quality of pathbased clustering, a data clustering method that can extract elongated structures from data in a noise robust way. The results of an agglomerative optimization method are influenced by small fluctuations of the input data. To increase the reliability o...

The recently-developed statistical method known as the "bootstrap" can be used to place confidence intervals on phylogenies. It involves resampling points from one's own data, with replacement, to create a series of bootstrap samples of the same size as the original data. Each of these is analyzed, and the variation among the resulting estimates taken to indicate the size of the error involved ...