Resampling

About: Resampling is a research topic. Over the lifetime, 5428 publications have been published within this topic receiving 242291 citations.

Resampling Techniques in the Analysis of Non-binormal ROC Data

Abstract: The methods most commonly used for analyzing receiver operating characteristic (ROC) data incorporate "binormal" assumptions about the latent frequency distributions of test results. Although these assumptions have proved robust to a wide variety of actual frequency distributions, some data sets do not "fit" the binormal model. In such cases, resampling techniques such as the jackknife and the bootstrap provide versatile, distribution-indepen dent, and more appropriate methods for hypothesis testing. This article describes the ap plication of resampling techniques to ROC data for which the binormal assumptions are not appropriate, and suggests that the bootstrap may be especially helpful in determining con fidence intervals from small data samples. The widespread availability of ever-faster com puters has made resampling methods increasingly accessible and convenient tools for data analysis. Key words: receiver operating characteristic; ROC; resampling; jackknife; bootstrap; diagnostic testing; diagnostic...

Stationary and nonstationary generalized extreme value modelling of extreme precipitation over a mountainous area under climate change

Abstract: The generalized extreme value (GEV) distribution is often fitted to environmental time series of extreme values such as annual maxima of daily precipitation. We study two methodological issues here. First, we compare criteria for selecting the best model among 16 GEV models that allow nonstationary scale and location parameters. Simulation results showed that both the corrected Akaike information criterion and Bayesian information criterion (BIC) always detected nonstationarity, but the BIC selected the correct model more often except in very small samples. Second, we examined confidence intervals (CIs) for model parameters and other quantities such as the return levels that are usually required for hydrological and climatological time series. Four bootstrap CIs—normal, percentile, basic and bias-corrected and accelerated—constructed by random-t resampling, fixed-t resampling and the parametric bootstrap methods were compared. CIs for parameters of the stationary model do not present major differences. CIs for the more extreme quantiles tend to become very wide for all bootstrap methods. For nonstationary GEV models with linear time dependence of location or log-linear time dependence of scale, CI coverage probabilities are reasonably accurate for the parameters. For the extreme percentiles, the bias-corrected and accelerated method is best overall, and the fixed-t method also has good average coverage probabilities. A case study is presented of annual maximum daily precipitation over the mountainous Mesochora catchment in Greece. Analysis of historical data and data generated under two climate scenarios (control run and climate change) supported a stationary GEV model reducing to the Gumbel distribution. Copyright © 2013 John Wiley & Sons, Ltd.

Power comparison of non-parametric tests: Small-sample properties from Monte Carlo

Abstract: SUMMARY Non-parametric tests that deal with two samples include scores tests (such as the Wilcoxon rank sum test, normal scores test, logistic scores test, Cauchy scores test, etc.) and Fisher’s randomization test. Because the non-parametric tests generally require a large amount of computational work, there are few studies on small-sample properties, although asymptotic properties with regard to various aspects were studied in the past. In this paper, the non-parametric tests are compared with the t-test through Monte Carlo experiments. Also, we consider testing structural changes as an application in economics.

Blind Sampling Rate Offset Estimation and Compensation in Wireless Acoustic Sensor Networks with Application to Beamforming

Abstract: Beamforming methods for speech enhancement in wireless acoustic sensor networks (WASNs) have recently attracted the attention of the research community. One of the major obstacles in implementing speech processing algorithms in WASN is the sampling rate offsets between the nodes. As nodes utilize individual clock sources, sampling rate offsets are inevitable and may cause severe performance degradation. In this paper, a blind procedure for estimating the sampling rate offsets is derived. The procedure is applicable to speech-absent time segments with slow time-varying interference statistics. The proposed procedure is based on the phase drift of the coherence between two signals sampled at different sampling rates. Resampling the signals with Lagrange polynomials interpolation method compensates for the sampling rate offsets. An extensive experimental study, utilizing the transfer function generalized sidelobe canceller (TFGSC), exemplifies the problem and its solution.

Invited Discussion Paper Resampling Methods in Sample Surveys

Abstract: This article reviews the applications of three resampling methods, the jackknife, the balanced repeated replication, and the bootstrap, in sample surveys The sampling design under consideration is a stratified multistage sampling design We discuss the implementation of the resampling methods; for example, the construction of balanced repeated replications and approximated balanced repeated replication estimators; four modified bootstrap algorithms to generate bootstrap samples; and three different ways of applying the resampling methods in the presence of imputed missing values Asymptotic properties of the resampling estimators are discussed for two types of important survey estimators, functions of weighted averages and sample quantiles