About: Resampling is a(n) research topic. Over the lifetime, 5428 publication(s) have been published within this topic receiving 242291 citation(s).
Papers published on a yearly basis
TL;DR: The recently‐developed statistical method known as the “bootstrap” can be used to place confidence intervals on phylogenies and shows significant evidence for a group if it is defined by three or more characters.
Abstract: The recently-developed statistical method known as the "bootstrap" can be used to place confidence intervals on phylogenies. It involves resampling points from one's own data, with replacement, to create a series of bootstrap samples of the same size as the original data. Each of these is analyzed, and the variation among the resulting estimates taken to indicate the size of the error involved in making estimates from the original data. In the case of phylogenies, it is argued that the proper method of resampling is to keep all of the original species while sampling characters with replacement, under the assumption that the characters have been independently drawn by the systematist and have evolved independently. Majority-rule consensus trees can be used to construct a phylogeny showing all of the inferred monophyletic groups that occurred in a majority of the bootstrap samples. If a group shows up 95% of the time or more, the evidence for it is taken to be statistically significant. Existing computer programs can be used to analyze different bootstrap samples by using weights on the characters, the weight of a character being how many times it was drawn in bootstrap sampling. When all characters are perfectly compatible, as envisioned by Hennig, bootstrap sampling becomes unnecessary; the bootstrap method would show significant evidence for a group if it is defined by three or more characters.
01 Jan 2001
TL;DR: In this article, the authors present a case study in least squares fitting and interpretation of a linear model, where they use nonparametric transformations of X and Y to fit a linear regression model.
Abstract: Introduction * General Aspects of Fitting Regression Models * Missing Data * Multivariable Modeling Strategies * Resampling, Validating, Describing, and Simplifying the Model * S-PLUS Software * Case Study in Least Squares Fitting and Interpretation of a Linear Model * Case Study in Imputation and Data Reduction * Overview of Maximum Likelihood Estimation * Binary Logistic Regression * Logistic Model Case Study 1: Predicting Cause of Death * Logistic Model Case Study 2: Survival of Titanic Passengers * Ordinal Logistic Regression * Case Study in Ordinal Regrssion, Data Reduction, and Penalization * Models Using Nonparametic Transformations of X and Y * Introduction to Survival Analysis * Parametric Survival Models * Case Study in Parametric Survival Modeling and Model Approximation * Cox Proportional Hazards Regression Model * Case Study in Cox Regression
01 Jan 1987
TL;DR: The Delta Method and the Influence Function Cross-Validation, Jackknife and Bootstrap Balanced Repeated Replication (half-sampling) Random Subsampling Nonparametric Confidence Intervals as mentioned in this paper.
Abstract: The Jackknife Estimate of Bias The Jackknife Estimate of Variance Bias of the Jackknife Variance Estimate The Bootstrap The Infinitesimal Jackknife The Delta Method and the Influence Function Cross-Validation, Jackknife and Bootstrap Balanced Repeated Replications (Half-Sampling) Random Subsampling Nonparametric Confidence Intervals.
TL;DR: Two alternatives for improving the performance of confidence limits for the indirect effect are evaluated: a method based on the distribution of the product of two normal random variables, and resampling methods.
Abstract: The most commonly used method to test an indirect effect is to divide the estimate of the indirect effect by its standard error and compare the resulting z statistic with a critical value from the standard normal distribution. Confidence limits for the indirect effect are also typically based on critical values from the standard normal distribution. This article uses a simulation study to demonstrate that confidence limits are imbalanced because the distribution of the indirect effect is normal only in special cases. Two alternatives for improving the performance of confidence limits for the indirect effect are evaluated: (a) a method based on the distribution of the product of two normal random variables, and (b) resampling methods. In Study 1, confidence limits based on the distribution of the product are more accurate than methods based on an assumed normal distribution but confidence limits are still imbalanced. Study 2 demonstrates that more accurate confidence limits are obtained using resampling methods, with the bias-corrected bootstrap the best method overall.
23 Jul 2020
TL;DR: The idea of a randomization test has been explored in the context of data analysis for a long time as mentioned in this paper, and it has been applied in a variety of applications in biology, such as single species ecology and community ecology.
Abstract: Preface to the Second Edition Preface to the First Edition Randomization The Idea of a Randomization Test Examples of Randomization Tests Aspects of Randomization Testing Raised by the Examples Sampling the Randomization Distribution or Systematic Enumeration Equivalent Test Statistics Significance Levels for Classical and Randomization Tests Limitations of Randomization Tests Confidence Limits by Randomization Applications of Randomization in Biology Single Species Ecology Genetics, Evolution and Natural Selection Community Ecology Randomization and Observational Studies Chapter Summary The Jackknife The Jackknife Estimator Applications of Jackknifing in Biology Single Species Analyses Genetics, Evolution and Natural Selection Community Ecology Chapter Summary The Bootstrap Resampling with Replacement Standard Bootstrap Confidence Limits Simple Percentile Confidence Limits Bias Corrected Percentile Confidence Limits Accelerated Bias Corrected Percentile Limits Other Methods for Constructing Confidence Intervals Transformations to Improve Bootstrap Intervals Parametric Confidence Intervals A Better Estimate of Bias Bootstrap Tests of Significance Balanced Bootstrap Sampling Applications of Bootstrapping in Biology Single Species Ecology Genetics, Evolution and Natural Selection Community Ecology Further Reading Chapter Summary Monte Carlo Methods Monte Carlo Tests Generalized Monte Carlo Tests Implicit Statistical Models Applications of Monte Carlo Methods in Biology Single Species Ecology Chapter Summary Some General Considerations Questions about Computer-Intensive Methods Power Number of Random Sets of Data Needed for a Test Determining a Randomization Distribution Exactly The number of replications for confidence intervals More Efficient Bootstrap Sampling Methods The Generation of Pseudo-Random Numbers The Generation of Random Permutations Chapter Summary One and Two Sample Tests The Paired Comparisons Design The One Sample Randomization Test The Two Sample Randomization Test Bootstrap Tests Randomizing Residuals Comparing the Variation in Two Samples A Simulation Study The Comparison of Two Samples on Multiple Measurements Further Reading Chapter Summary Exercises Analysis of Variance One Factor Analysis of Variance Tests for Constant Variance Testing for Mean Differences Using Residuals Examples of More Complicated Types of Analysis of Variance Procedures for Handling Unequal Group Variances Other Aspects of Analysis of Variance Further Reading Chapter Summary Exercises Regression Analysis Simple Linear Regression Randomizing Residuals Testing for a Non-Zero B Value Confidence Limits for B Multiple Linear Regression Alternative Randomization Methods with Multiple Regression Bootstrapping and Jackknifing with Regression Further Reading Chapter Summary Exercises Distance Matrices and Spatial Data Testing for Association between Distance Matrices The Mantel Test Sampling the Randomization Distribution Confidence Limits for Regression Coefficients The Multiple Mantel Test Other Approaches with More than Two Matrices Further Reading Chapter Summary Exercises Other Analyses on Spatial Data Spatial Data Analysis The Study of Spatial Point Patterns Mead's Randomization Test Tests for Randomness Based on Distances Testing for an Association between Two Point Patterns The Besag-Diggle Test Tests Using Distances between Points Testing for Random Marking Further Reading Chapter Summary Exercises Time Series Randomization and Time Series Randomization Tests for Serial Correlation Randomization T ests for Trend Randomization Tests for Periodicity Irregularly Spaced Series Tests on Times of Occurrence Discussion on Procedures for Irregular Series Bootstrap and Monte Carlo Tests Further Reading Chapter Summary Exercises Multivariate Data Univariate and Multivariate Tests Sample Means and Covariance Matrices Comparison of Sample Mean Vectors Chi-Squared Analyses for Count Data Principle Component Analysis and Other One Sample Methods Discriminant Function Analysis Further Reading Chapter Summary Exercises Survival and Growth Data Bootstrapping Survival Data Bootstrapping for Variable Selection Bootstrapping for Model Selection Group Comparisons Growth Data Further Reading Chapter Summary Exercises Non-Standard Situations The Construction of Tests in Non-Standard Situations Species Co-Occurrences on Islands An Alternative Generalized Monte Carlo Test Examining Time Changes in Niche Overlap Probing Multivariate Data with Random Skewers Ant Species Sizes in Europe Chapter Summary Bayesian Methods The Bayesian Approach to Data Analysis The Gibbs Sampler and Related Methods Biological Applications Further Reading Chapter Summary Exercises Conclusion and Final Comments Randomization Bootstrapping Monte Carlo Methods in General Classical versus Bayesian Inference Appendix Software for Computer Intensive Statistics References Index
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