Resampling

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

Resampling methods for input modeling

Abstract: Stochastic simulation models are used to predict the behavior of real systems whose components have random variation. The simulation model generates artificial random quantities based on the nature of the random variation in the real system. Very often, the probability distributions occurring in the real system are unknown, and must be estimated using finite samples. This paper shows three methods for incorporating the error due to input distributions that are based on finite samples, when calculating confidence intervals for output parameters.

A general approach to analyzing epidemiologic data that contain misclassification errors.

Abstract: Misclassification is a common source of bias and reduced efficiency in the analysis of discrete data. Several methods have been proposed to adjust for misclassification using information on error rates (i) gathered by resampling the study population, (ii) gathered by sampling a separate population, or (iii) assumed a priori. We present unified methods for incorporating these types of information into analyses based on log-linear models and maximum likelihood estimation. General variance expressions are developed. Examples from epidemiologic studies are used to demonstrate the proposed methodology.

Permutation tests for factorially designed neuroimaging experiments

Abstract: Permutation methods for analysis of functional neuroimaging data acquired as factorially designed experiments are described and validated. The F ratio was estimated for main effects and interactions at each voxel in standard space. Critical values corresponding to probability thresholds were derived from a null distribution sampled by appropriate permutation of observations. Spatially informed, cluster-level test statistics were generated by applying a preliminary probability threshold to the voxel F maps and then computing the sum of voxel statistics in each of the resulting three-dimensional clusters, i.e., cluster "mass." Using simulations comprising two between- or within-subject factors each with two or three levels, contaminated by Gaussian and non-normal noise, the voxel-wise permutation test was compared to the standard parametric F test and to the performance of the spatially informed statistic using receiver operating characteristic (ROC) curves. Validity of the permutation-testing algorithm and software is endorsed by almost identical performance of parametric and permutation tests of the voxel-level F statistic. Permutation testing of suprathreshold voxel cluster mass, however, was found to provide consistently superior sensitivity to detect simulated signals than either of the voxel-level tests. The methods are also illustrated by application to an experimental dataset designed to investigate effects of antidepressant drug treatment on brain activation by implicit sad facial affect perception in patients with major depression. Antidepressant drug effects in left amygdala and ventral striatum were detected by this software for an interaction between time (within-subject factor) and group (between-subject factor) in a representative two-way factorial design.

Asymptotics, Nonparametrics, and Time Series

Abstract: Some examples of empirical fourier analysis in scientific problems modeling and inference for periodically correlated time series modeling time series of count data seasonal and cyclical long memory nonparametric specification procedures for time series parameter estimation and model selection for multistep prediction of a time series - a review nonlinear estimation for time series observed on arrays some contributions to multivariate nonlinear time series and to bilinear models optimal testing for semiparametric AR models - from Gaussian Lagrange multipliers to autoregression rank scores and adaptive tests statistical analysis based on functionals of nonparametric spectral density estimators efficient estimation in a semiparametric additive regression model with ARMA errors efficient estimation in Markov chain models - an introduction nonparametric functional estimation - an overview minimum distance and nonparametric dispersion functions estimators of changes on inverse estimation approaches for semiparametric Bayesian regression consistency issues in Bayesian nonparametrics breakdown theory for estimators based on bootstrap and other resampling schemes on second-order properties of the stationary bootstrap method for studentized statistics convergence to equilibrium of random dynamical systems generated by IID monotone maps, with applications to economics chi-squared tests of goodness-of-fit for dependent observations positive and negative dependence with some statistical applications second-order information loss due to nuisance parameters - a simple measure. Appendix: publications of Madan Lal Puri.