Citation: 'bootstrap resampling' in the IUPAC Compendium of Chemical Terminology, 5th ed.; International Union of Pure and Applied Chemistry; 2025. Online version 5.0.0, 2025. 10.1351/goldbook.11417 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms. Requests for commercial usage of the compendium should be directed to IUPAC.

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Resampling Methods

Gareth James, Daniela Witten, Trevor Hastie et al. · Springer texts in statistics · 2013

Cited by 40

Image resampling

Neil Anthony Dodgson · CL Technical Reports · 2021

Image resampling is the process of geometrically transforming digital images. This report considers several aspects of the process. We begin by decomposing the resampling process into three simpler sub-processes: reconstruction of a continuous intensity surface from a discrete image, transformation of that continuous surface, and sampling of the transformed surface to produce a new discrete image. We then consider the sampling process, and the subsidiary problem of intensity quantisation. Both these are well understood, and we present a summary of existing work, laying a foundation for the central body of the report where the sub-process of reconstruction is studied. The work on reconstruction divides into four parts, two general and two specific: 1. Piecewise local polynomials: the most studied group of reconstructors. We examine these, and the criteria used in their design. One new derivation is of two piecewise local quadratic reconstructors. 2. Infinite extent reconstructors: we consider these and their local approximations, the problem of finite image size, the resulting edge effects, and the solutions to these problems. Amongst the reconstructors discussed are the interpolating cubic B-spline and the interpolating Bezier cubic. We derive the filter kernels for both of these, and prove that they are the same. Given this kernel we demonstrate how the interpolating cubic B-spline can be extended from a one-dimensional to a two-dimensional reconstructor, providing a considerable speed improvement over the existing method of extension. 3. Fast Fourier transform reconstruction: it has long been known that the fast Fourier transform (FFT) can be used to generate an approximation to perfect scaling of a sample set. Donald Fraser (in 1987) took this result and generated a hybrid FFT reconstructor which can be used for general transformations, not just scaling. We modify Fraser’s method to tackle two major problems: its large time and storage requirements, and the edge effects it causes in the reconstructed intensity surface. 4. A priori knowledge reconstruction: first considering what can be done if we know how the original image was sampled, and then considering what can be done with one particular class of image coupled with one particular type of sampling. In this latter case we find that exact reconstruction of the image is possible. This is a surprising result as this class of images cannot be exactly reconstructed using classical sampling theory. The final section of the report draws all of the strands together to discuss transformations and the resampling process as a whole. Of particular note here is work on how the quality of different reconstruction and resampling methods can be assessed.

Cited by 1Open access

Resampling methods.

Annie Kim (3437381), Inkyung Jung (94394) · Figshare · 2023

<div><p>Class imbalance is a major problem in classification, wherein the decision boundary is easily biased toward the majority class. A data-level solution (resampling) is one possible solution to this problem. However, several studies have shown that resampling methods can deteriorate the classification performance. This is because of the overgeneralization problem, which occurs when samples produced by the oversampling technique that should be represented in the minority class domain are introduced into the majority-class domain. This study shows that the overgeneralization problem is aggravated in complex data settings and introduces two alternate approaches to mitigate it. The first approach involves incorporating a filtering method into oversampling. The second approach is to apply undersampling. The main objective of this study is to provide guidance on selecting optimal resampling methods in imbalanced and complex datasets to improve classification performance. Simulation studies and real data analyses were performed to compare the resampling results in various scenarios with different complexities, imbalances, and sample sizes. In the case of noncomplex datasets, undersampling was found to be optimal. However, in the case of complex datasets, applying a filtering method to delete misallocated examples was optimal. In conclusion, this study can aid researchers in selecting the optimal method for resampling complex datasets.</p></div>

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Bootstrap Resampling

Helmers, Roelof, Nieuwland, G.Y. · Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands · 1996

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Resampling Methods

Wanzhu Tu · Wiley StatsRef: Statistics Reference Online · 2014

Abstract This article presents an overview of existing resampling methods and their applications in parameter estimations and statistical inference. It describes the essential concepts and basic operating procedures of the most frequently used resampling methods, including the jackknife, bootstrap, permutation test, and cross‐validation. This article also discusses the interconnections among these methods. Finally, it briefly reviews the currently available software for implementation of the most commonly used resampling procedures.

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Resampling Methods

Robert Gentleman · The American Statistician · 2003

Cited by 9

Resampling methods

James D. Malley, Karen G. Malley, Sinisa Pajevic · Cambridge University Press eBooks · 2011

In this chapter we discuss several basic computational tools, methods that have broad use in the data analysis community for a wide range of purposes. This is the class of sample reuse schemes, also known as resampling methods. These include cross-validation and the leave-one-out method.

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Resampling Methods

Gareth James, Daniela Witten, Trevor Hastie et al. · Springer texts in statistics · 2023

Cited by 20