Topic
Resampling
About: Resampling is a research topic. Over the lifetime, 5428 publications have been published within this topic receiving 242291 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this article, the authors examined the situation of resampling of lots and derived the performance measures of a resampled scheme having a single sampling plan for inspection, and discussed the usefulness and limitations of resample of resubmitted lots.
Abstract: Lot resubmissions are allowed in situations where the original inspection results are suspected or when the supplier is allowed to opt for resampling as per the provisions of the contract etc. This paper examines the situation of resampling of lots and derives the performance measures of a resampling scheme having a single sampling plan for inspection. The usefulness and limitations of resampling of resubmitted lots are also discussed
72 citations
01 Jan 2001
TL;DR: In this paper, a new bias correction method for the DIMTEST procedure based on the statistical principle of resampling is introduced. But this method is limited to the case of multidimensionality.
Abstract: Following in the nonparametric item response theory tradition, DIMTEST (Stout, 1987) is an asymptotically justified nonparametric procedure that provides a test of hypothesis of unidimensionality of a test data set This chapter introduces a new bias correction method for the DIMTEST procedure based on the statistical principle of resampling A simulation study shows this new version of DIMTEST has a Type I error rate close to the nominal rate of α = 005 in most cases and very high power to detect multidimensionality in a variety of realistic multidimensional models The result with this new bias correction method is an improved DIMTEST procedure with much wider applicability and good statistical performance
72 citations
TL;DR: This paper investigates the performance of the sampling method based on kernel density estimation (KDE) and concludes that the proposed method would be a valuable tool in problems involving imbalanced class distribution.
72 citations
TL;DR: This article proposes a resampling-based stochastic approximation method that leads to a general parameter estimation approach, maximum mean log-likelihood estimation, which includes the popular maximum (log)-likelihood estimator approach as a special case and is expected to play an important role in analyzing large datasets.
Abstract: The Gaussian geostatistical model has been widely used in modeling of spatial data. However, it is challenging to computationally implement this method because it requires the inversion of a large covariance matrix, particularly when there is a large number of observations. This article proposes a resampling-based stochastic approximation method to address this challenge. At each iteration of the proposed method, a small subsample is drawn from the full dataset, and then the current estimate of the parameters is updated accordingly under the framework of stochastic approximation. Since the proposed method makes use of only a small proportion of the data at each iteration, it avoids inverting large covariance matrices and thus is scalable to large datasets. The proposed method also leads to a general parameter estimation approach, maximum mean log-likelihood estimation, which includes the popular maximum (log)-likelihood estimation (MLE) approach as a special case and is expected to play an important role ...
72 citations
TL;DR: For a broad class of jackknife statistics, it was shown in this article that the Tukey estimator of the variance converges almost surely to its population counterpart, and that the usual invariance principles (relating to the Wiener process approximations) usually filter through jackknifing under no extra regularity conditions.
Abstract: For a broad class of jackknife statistics, it is shown that the Tukey estimator of the variance converges almost surely to its population counterpart. Moreover, the usual invariance principles (relating to the Wiener process approximations) usually filter through jackknifing under no extra regularity conditions. These results are then incorporated in providing a bounded-length (sequential) confidence interval and a preassigned-strength sequential test for a suitable parameter based on jackknife estimators.
72 citations