Resampling

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

Confidence Intervals for Regression Quantiles

Abstract: Several methods to construct confidence intervals for regression quan-tile estimators (Koenker and Bassett (1978)) are reviewed Direct estimation of the asymptotic covariance matrix requires an estimate of the reciprocal of the error density (sparsity function) at the quantite of interest; some recent work on bandwidth selection for this problem will be discussed Several versions of the bootstrap for quantile regression will be described as well as a recent proposal by Parzen, Wei, and Ying (1992) for resampling from the (approximately pivotal) estimating equation Finally, we will describe a new approach based on inversion of a rank test suggested by Gutenbrunner, Jureckova, Koenker, and Portnoy (1993) and introduced in Huskova(1994) The latter approach has several advantages: it may be computed relatively efficiently, it is consistent under certain heteroskedastic conditions and it circumvents any explicit estimation of the sparsity function A small monte-carlo experiment is employed to compare the competing methods

Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach

Abstract: The area under the receiver operating characteristic curve is often used as a summary index of the diagnostic ability in evaluating biomarkers when the clinical outcome (truth) is binary. When the clinical outcome is right-censored survival time, the C index, motivated as an extension of area under the receiver operating characteristic curve, has been proposed by Harrell as a measure of concordance between a predictive biomarker and the right-censored survival outcome. In this work, we investigate methods for statistical comparison of two diagnostic or predictive systems, of which they could either be two biomarkers or two fixed algorithms, in terms of their C indices. We adopt a U-statistics-based C estimator that is asymptotically normal and develop a nonparametric analytical approach to estimate the variance of the C estimator and the covariance of two C estimators. A z-score test is then constructed to compare the two C indices. We validate our one-shot nonparametric method via simulation studies in terms of the type I error rate and power. We also compare our one-shot method with resampling methods including the jackknife and the bootstrap. Simulation results show that the proposed one-shot method provides almost unbiased variance estimations and has satisfactory type I error control and power. Finally, we illustrate the use of the proposed method with an example from the Framingham Heart Study.

Data analytic methods for the analysis of multilevel questions: A comparison of intraclass correlation coefficients, rwg(j), hierarchical linear modeling, within- and between-analysis, and random group resampling

Abstract: Researchers investigating organizations and leadership in particular are increasingly being called upon to theorize multilevel models and to utilize multilevel data analytic techniques. However, the literature provides relatively little guidance for researchers to identify which of the multilevel methodologies are appropriate for their particular questions. In this final article, the statistical procedures used in the multilevel data analyses in the previous articles of this special issue are compared. Specifically, intraclass correlation coefficients (ICCs), rwg(j), hierarchical linear modeling (HLM), within- and between-analysis (WABA), and random group resampling (RGR) are examined and their results and conclusions discussed. Following comparisons of these methods, recommendations for their use are presented.