Resampling

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

Statistical interim monitoring of the Cardiac Arrhythmia Suppression Trial.

Abstract: The Cardiac Arrythmia Suppression Trial was stopped much earlier than planned. Statistical considerations played a very important role in the decision. Flexible group sequential testing was developed for the trial by implementing a Lan and DeMets procedure with use of the permutation test. We compute P-values from the joint permutation distribution of the test statistics, so we do not need to estimate the sampling distribution which in general is rather difficult to do without strict assumptions. The method also gives an exact test for small samples and allows us to use more complicated or non-Gaussian statistics. We also utilized stochastic curtailment ideas to evaluate various scenarios that might occur during the course of the trial, which assisted the Data and Safety Monitoring Board in making appropriate decisions.

Measuring the Stability of Results From Supervised Statistical Learning

Abstract: Stability is a major requirement to draw reliable conclusions when interpreting results from supervised statistical learning. In this article, we present a general framework for assessing and compa...

A permutation test for the robust sib-pair linkage method.

Abstract: The robust sib-pair method introduced by Haseman & Elston (1972) is one of the most widely circulated allele-sharing methods for linkage analysis. The procedure evaluates linkage by significance testing of a regression coefficient and, hence, a standard t-test has traditionally been applied despite known violations of the statistical assumptions underlying the test. We present a permutation based reference distribution for the estimate of the regression coefficient that is motivated by genetic principles rather than by standard regression testing procedures. The permutation test approximates Mendelian co-segregation under the null hypothesis of no linkage, making it a very natural approach. Theory and simulations show that the conventional t-test approximates the permutation test quite well, even when dependent sib pairs are used for analysis. These results thus indirectly address concerns over the t-test. To illustrate the permutation test using real data we applied the procedure to two lipoprotein systems that have been well characterized.

A Semiparametric Bootstrap Technique for Simulating Extreme Order Statistics

Abstract: We propose a technique for simulating the joint distribution of the j largest order statistics of a very large sample. We assume that the parent population is in the domain of attraction of the Type 1 (Gumbel) extreme value distribution. The bootstrap variates are generated by resampling the normalized spacings of the k largest observed values in the original data where k is larger than j. We compare the bootstrap distribution to the fitted extremal distribution of Weissman. Both distributions have the same means, conditional on the k largest observed values in the data set. If k is large and the normalized spacings behave as independent and identically distributed exponential random variables then the bootstrap variates behave as though sampled from the extremal distribution. We propose several procedures for estimating k and give a numerical example.