Resampling

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

Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method.

Abstract: Experimental studies in biomedical research frequently pose analytical problems related to small sample size. In such studies, there are conflicting findings regarding the choice of parametric and nonparametric analysis, especially with non-normal data. In such instances, some methodologists questioned the validity of parametric tests and suggested nonparametric tests. In contrast, other methodologists found nonparametric tests to be too conservative and less powerful and thus preferred using parametric tests. Some researchers have recommended using a bootstrap test; however, this method also has small sample size limitation. We used a pooled method in nonparametric bootstrap test that may overcome the problem related with small samples in hypothesis testing. The present study compared nonparametric bootstrap test with pooled resampling method corresponding to parametric, nonparametric, and permutation tests through extensive simulations under various conditions and using real data examples. The nonparametric pooled bootstrap t-test provided equal or greater power for comparing two means as compared with unpaired t-test, Welch t-test, Wilcoxon rank sum test, and permutation test while maintaining type I error probability for any conditions except for Cauchy and extreme variable lognormal distributions. In such cases, we suggest using an exact Wilcoxon rank sum test. Nonparametric bootstrap paired t-test also provided better performance than other alternatives. Nonparametric bootstrap test provided benefit over exact Kruskal-Wallis test. We suggest using nonparametric bootstrap test with pooled resampling method for comparing paired or unpaired means and for validating the one way analysis of variance test results for non-normal data in small sample size studies. Copyright © 2017 John Wiley & Sons, Ltd.

Estimation of size at sexual maturity: an evaluation of analytical and resampling procedures

Abstract: Size at 50% maturity is commonly evaluated for wild popula- tions, but the uncertainty involved in such computation has been frequently overlooked in the application to marine fisheries. Here we evaluate three pro- cedures to obtain a confidence interval for size at 50% maturity, and in gen- eral for P% maturity: Fieller's analyti- cal method, nonparametric bootstrap, and a Monte Carlo algorithm. The three methods are compared in estimating size at 50% maturity (l 50% ) by using simulated data from an age-structured population, with von Bertalanffy growth and constant natural mortality, for sample sizes of 500 to 10,000 indi- viduals. Performance was assessed by using four criteria: 1) the proportion of times that the confidence interval did contain the true and known size at 50% maturity, 2) bias in estimating l 50% , 3) length and 4) shape of the confidence interval around l 50% . Judging from cri- teria 2-4, the three methods performed equally well, but in criterion 1, the Monte Carlo method outperformed the bootstrap and Fieller methods with a frequency remaining very close to the nominal 95% at all sample sizes. The Monte Carlo method was also robust to variations in natural mortality rate (M), although with lengthier and more asymmetric confidence intervals as M increased. This method was applied to two sets of real data. First, we used data from the squat lobster Pleuron- codes monodon with several levels of proportion mature, so that a confidence interval for the whole maturity curve could be outlined. Second, we compared two samples of the anchovy Engraulis ringens from different localities in cen- tral Chile to test the hypothesis that they differed in size at 50% maturity and concluded that they were not sta- tistically different. statistical uncertainty of the model- based l 50% is ignored (Table 1). In this work, we show three alterna- tive procedures: an analytical method derived from generalized linear models (McCullagh and Nelder, 1989), nonparametric boot- strap (Efron and Tibshirani, 1993), and a Monte Carlo algorithm devel- oped in our study. We show by simu- lation the behavior of the three methods for sample sizes of 500 to 10,000 individuals, concluding that they are similar in terms of bias, length, and shape of confidence inter- vals but that the Monte Carlo method outperforms the other two methods in percentage of times that the confi- dence interval contains the true pa- rameter, which remained close to the nominal 95% at all sample sizes.

On adaptive resampling strategies for sequential Monte Carlo methods

Abstract: Sequential Monte Carlo (SMC) methods are a class of techniques to sample approximately from any sequence of probability distributions using a combination of importance sampling and resampling steps. This paper is concerned with the convergence analysis of a class of SMC methods where the times at which resampling occurs are computed online using criteria such as the effective sample size. This is a popular approach amongst practitioners but there are very few convergence results available for these methods. By combining semigroup techniques with an original coupling argument, we obtain functional central limit theorems and uniform exponential concentration estimates for these algorithms.

PhyloMeasures: a package for computing phylogenetic biodiversity measures and their statistical moments

Abstract: We present PhyloMeasures, a new software package including both a C++ and R version, that provides very fast computation of popular phylogenetic diversity measures. PhyloMeasures introduces two major advances over existing methods. First, it uses efficient algorithms for calculating basic phylogenetic metrics (such as Faith's PD and the mean pairwise distance, MPD) and two-sample measures (such as common branch length, CBL, and the unique fraction) that are designed to perform well even on very large trees. Second, it computes exact richness-standardised versions of these measures (such as the widely used net relatedness index, NRI) by efficiently evaluating analytical expressions for the mean and variance of the basic measures, rather than by the slow and inexact randomization techniques that are the current standard. Together, these lead to massive improvements in performance compared to the current state of the art. For example, running on a standard laptop, PhyloMeasures functions can provide the NRI for 20 samples from a tree of 100 000 tips in about 1.5 s, compared to an estimated 37 d using standard resampling approaches. This will allow analyses on larger data sets than were previously possible.