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Resampling

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


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Book
01 Jan 1992
TL;DR: In this paper, the authors present a non-Edgeworth view of the Bootstrap and propose a method of importance sampling for estimating bias, variance, and skewness.
Abstract: 1: Principles of Bootstrap Methodology.- 2: Principles of Edgeworth Expansion.- 3: An Edgeworth View of the Bootstrap.- 4: Bootstrap Curve Estimation.- 5: Details of Mathematical Rigour.- Appendix I: Number and Sizes of Atoms of Nonparametric Bootstrap Distribution.- Appendix II: Monte Carlo Simulation.- II.1 Introduction.- II.2 Uniform Resampling.- II.3 Linear Approximation.- II.4 Centring Method.- II.5 Balanced Resampling.- II.6 Antithetic Resampling.- II.7 Importance Resampling.- II.7.1 Introduction.- II.7.2 Concept of Importance Resampling.- II.7.3 Importance Resampling for Approximating Bias, Variance, Skewness, etc..- II.7.4 Importance Resampling for a Distribution Function.- II.8 Quantile Estimation.- Appendix III: Confidence Pictures.- Appendix IV: A Non-Standard Example: Quantite Error Estimation.- IV. 1 Introduction.- IV.2 Definition of the Mean Squared Error Estimate.- IV.3 Convergence Rate of the Mean Squared Error Estimate.- IV.4 Edgeworth Expansions for the Studentized Bootstrap Quantile Estimate.- Appendix V: A Non-Edgeworth View of the Bootstrap.- References.- Author Index.

2,306 citations

Journal ArticleDOI
TL;DR: An integrated approach to fitting psychometric functions, assessing the goodness of fit, and providing confidence intervals for the function’s parameters and other estimates derived from them, for the purposes of hypothesis testing is described.
Abstract: The psychometric function relates an observer’s performance to an independent variable, usually some physical quantity of a stimulus in a psychophysical task. This paper, together with its companion paper (Wichmann & Hill, 2001), describes an integrated approach to (1) fitting psychometric functions, (2) assessing the goodness of fit, and (3) providing confidence intervals for the function’s parameters and other estimates derived from them, for the purposes of hypothesis testing. The present paper deals with the first two topics, describing a constrained maximum-likelihood method of parameter estimation and developing several goodness-of-fit tests. Using Monte Carlo simulations, we deal with two specific difficulties that arise when fitting functions to psychophysical data. First, we note that human observers are prone to stimulus-independent errors (orlapses). We show that failure to account for this can lead to serious biases in estimates of the psychometric function’s parameters and illustrate how the problem may be overcome. Second, we note that psychophysical data sets are usually rather small by the standards required by most of the commonly applied statistical tests. We demonstrate the potential errors of applying traditionalX2 methods to psychophysical data and advocate use of Monte Carlo resampling techniques that do not rely on asymptotic theory. We have made available the software to implement our methods.

2,263 citations

Journal ArticleDOI
TL;DR: Resampling-Based Adjustments: Basic Concepts and Practical Applications.
Abstract: Resampling-Based Adjustments: Basic Concepts. Continuous Data Applications: Univariate Analysis. Continuous Data Applications: Multivariate Analysis. Binary Data Applications. Further Topics. Practical Applications. Appendices. References. List of Algorithms. List of Examples. Indexes.

2,098 citations

Book
Phillip I. Good1
22 Dec 2012
TL;DR: This book provides a step-by-step manual on the application of permutation tests in biology, medicine, science, and engineering and shows how the problems of missing and censored data, nonresponders, after thefact covariates, and outliers may be handled.
Abstract: This book provides a step-by-step manual on the application of permutation tests in biology, medicine, science, and engineering. Its intuitive and informal style will ideally suit it as a text for students and researchers coming to these methods for the first time. In particular, it shows how the problems of missing and censored data, nonresponders, after-the-fact covariates, and outliers may be handled.

1,780 citations

Book
01 Jan 1991
TL;DR: This book discusses the construction of tests in non-standard situations testing for randomness of species co-occurences on islands examining time change in niche ovelap probing multivariate data with random skewers other examples.
Abstract: Part 1 Randomization tests and confidence intervals: the idea of a randomization test examples of a randomization test aspects of randomization testing raised by the examples confidence intervals from randomization. Part 2 Monte Carlo and other computer intensive methods: Monte Carlo tests jackknifing bootstrapping bootstrap tests of significance and confidence intervals. Part 3 Some general considerations: power determining how many randomizations are needed determining a randomization distribution exactly the computer generation of pseudo-random numbers generating random permutations. Part 4 One and two sample tests: the paired comparisons design the one sample randomization test the two sample randomization test the comparison of two samples on multiple measurements. Part 5 Analysis of variance: one factor analysis of variance Bartlett's test for constant variance examples of more complicated types of analysis of variance discussion computer program. Part 6 Regrssion analysis: simple regression testing for a non-zero beta value confidence limits for beta multiple linear regression randomizing X variable values. Part 7 Distance matrices and spatial data: testing for association between distance matrices Mantel's test determining significance by sampling randomization distribution confidence limits for a matrix regression coefficient problems involving more than two matrices. Part 8 Other analyses on spatial data: the study of spatial point patterns Mead's randomization test a test based on nearest neighbour distances testing for an association between two point patterns the Besag-Diggle test tests using distances between points. Part 9 Time series: randomization and time series randomization tests for serial correlation randomization tests for trend randomization tests for periodicity irregularly spaced series tests on times of occurence discussion of procedures for irregular series bootstrap and Monte Carlo tests. Part 10 Multivariate data: univariate and multivariate tests sample means and covariance matrices comparison on sample means vectors chi-squared analyses for count data principal component analysis and other one sample methods discriminate function analysis. Part 11 Ad hoc methods: the construction of tests in non-standard situations testing for randomness of species co-occurences on islands examining time change in niche ovelap probing multivariate data with random skewers other examples. Part 12 Conclusion: randomization methods bootstrap and Monte Carlo methods.

1,705 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20251
20242
2023377
2022759
2021275
2020279