The construction of the simultaneous confidence bands for the integrated hazard function is considered. The Nelson–Aalen estimator is used. The simultaneous confidence bands based on bootstrap methods are presented. Two methods of construction of such confidence bands are proposed. The weird bootstrap method is used for resampling. Simulations are made to compare the actual coverage probability...

Gully erosion is one of the advanced forms water erosion. Identifying effective factors and gully predicting important tools to control manage such phenomenon. The main purpose this study evaluate effect four different resampling algorithms including cross-validation (5-fold 10-fold) bootstrapping (Bootstrap Optimism bootstrap) on boosted regression tree (BRT), support vector machine (SVM), ran...

This paper is devoted to thoroughly investigating how to bootstrap the ROC curve, a widely used visual tool for evaluating the accuracy of test/scoring statistics s(X) in the bipartite setup. The issue of confidence bands for the ROC curve is considered and a resampling procedure based on a smooth version of the empirical distribution called the ”smoothed bootstrap” is introduced. Theoretical a...

This paper is devoted to thoroughly investigating how to bootstrap the ROC curve, a widely used visual tool for evaluating the accuracy of test/scoring statistics in the bipartite setup. The issue of confidence bands for the ROC curve is considered and a resampling procedure based on a smooth version of the empirical distribution called the ”smoothed bootstrap” is introduced. Theoretical argume...

This work proposes a resampling technique to approximate the smoothing parameter of Beran’s estimator. It is based on by smoothed bootstrap and minimising approximation mean integrated squared error find bandwidth. The behaviour this method has been tested simulation several models. Bootstrap confidence intervals are also addressed in research their performance analysed study.

Re-sampling methods have long been used in survey sampling, dating back to Mahalanobis (1946). More recently, jackknife and bootstrap resampling methods have also been proposed for small area estimation; in particular for mean squared error (MSE) estimation and for constructing confidence intervals. We present a brief overview of early uses of resampling methods in survey sampling, and then pro...

Sparked by Efron’s seminal paper, the decade of the 1980s was a period of active research on bootstrap methods for independent data— mainly i.i.d. or regression set-ups. By contrast, in the 1990s much research was directed towards resampling dependent data, for example, time series and random fields. Consequently, the availability of valid nonparametric inference procedures based on resampling ...

The chapter gives a review of the literature on bootstrap methods for time series data. It describes various possibilities on how the bootstrap method, initially introduced for independent random variables, can be extended to a wide range of dependent variables in discrete time, including parametric or nonparametric time series models, autoregressive and Markov processes, long range dependent t...

The chapter gives a review of the literature on bootstrap methods for time series data. It describes various possibilities on how the bootstrap method, initially introduced for independent random variables, can be extended to a wide range of dependent variables in discrete time, including parametric or nonparametric time series models, autoregressive and Markov processes, long range dependent t...

The use of remotely sensed imagery to generate land cover models is common today. Validation of these models typically involves the use of an independent set of ground-truth data which are used to calculate an error matrix resulting in estimates of omission, commission, and overall error. However, each estimate of error contains a degree of uncertainty itself due to 1) conceptual bias, 2) locat...