It is known that bootstrapping maximum for estimating the endpoint of a distribution function is inconsistent and subsample bootstrap method is needed. Under an extreme value condition, some other estimators for the endpoint have been studied in the literature, which are preferrable to the maximum in regular cases. In this paper, we show that the full sample bootstrap method is consistent for t...

The bootstrap is a nonparametric approach for calculating quantities, such as confidence intervals, directly from data. Since calculating exact bootstrap quantities is believed to be intractable, randomized resampling algorithms are traditionally used. Motivated by the fact that the variability from randomization can lead to inaccurate outputs, we propose a deterministic approach. First, we est...

The Producer Price Index (PPI) collects price data from domestic producers of commodities and publishes monthly indexes on average price changes received by those producers at all stages of processing. PPI samples employ a two-stage design where establishments are selected in the first stage and unique items are selected in the second stage. In this paper we review the research results from the...

Using resampling methods like cross-validation and bootstrap is a necessity in neural network design, for solving the problem of model structure selection. The bootstrap is a powerful method offering a low variance of the model generalization error estimate. Unfortunately, its computational load may be excessive when used to select among neural networks models of different structures or complex...

This paper describes how the bootstrap approach to statistics can be applied to the evaluation of IR effectiveness metrics. More specifically, we describe straightforward methods for comparing the discriminative power of IR metrics based on Bootstrap Hypothesis Tests. Unlike the somewhat ad hoc Swap Method proposed by Voorhees and Buckley, our Bootstrap Sensitivity Methods estimate the overall ...

When the data do not come from the assumed parametric model, the usual asymptotic chisquared distribution under the null hypothesis, remains valid for “robustified” Wald and score test statistics. In this paper we compare the performance of this chi-squared approximation to that of a semiparametric bootstrap method. The bootstrap approximation is based on a onestep bootstrap estimator reflectin...

A local linear method for estimating the conditional ROC curve under the presence of continuous and categorical covariates has been introduced in this paper. A data driven smoothing parameter has been proposed via the bootstrap method. The methods have been illustrated with real data coming from a discrimination problem emerging in the context of a computer-aided diagnosis system. The bootstrap...

Classification and prediction of protein domain structural class is one of the important topics in the molecular biology. We introduce the Bagging (Bootstrap aggregating), one of the bootstrap methods, for classifying and predicting protein structural classes. By a bootstrap aggregating procedure, the Bagging can improve a weak classifier, for instance the random tree method, to a significant s...

Background: Nonlinear relationships are common in the environmental discipline. Spreadsheet packages such as Microsoft Excel come with an add-on for nonlinear regression, but parameter uncertainty estimates are not yet available. The purpose of this paper is to use Monte Carlo and bootstrap methods to estimate nonlinear parameter uncertainties with a Microsoft Excel spreadsheet. As an example, ...