Jackknife and bootstrap bias corrections are based on a diierencing argument which does not necessarily respect the sign of the true parameter value. Depending on sampling variability they can over-correct, producing a nal estimator that is negative when one knows on physical grounds that it should be positive. To overcome this problem we suggest a simple, alternative bootstrap approach, based ...

In this study, we used an empirical example based on 100 mitochondrial genomes from higher teleost fishes to compare the accuracy of parsimony-based jackknife values with Bayesian support values. Phylogenetic analyses of 366 partitions, using differential taxon and character sampling from the entire data matrix of 100 taxa and 7,990 characters, were performed for both phylogenetic methods. The ...

Component-based software development needs to formalize a process of generation, evaluation and selection of Composite COTS-based Software Systems (CCSS), enabling software architects to make early decisions; the Azimut approach and its associated software tool were proposed to tackle this problem. This article presents an experimental study conduced to compare Azimut approach with a Systematiz...

An estimator is said to be of order s > 0 if its bias has magnitude n−s, where n is the sample size. We give delta estimators and jackknife estimators of order four for smooth functions of the parameters of a multinomial distribution. An unbiased estimator is given for its density function. We also give a jackknife estimator of any order for smooth functions of the binomial parameter. The jackk...

We provide computationally attractive methods to obtain jackknife-based cluster-robust variance matrix estimators (CRVEs) for linear regression models estimated by least squares. also propose several new variants of the wild cluster bootstrap, which involve these CRVEs, bootstrap data-generating processes, or both. Extensive simulation experiments suggest that can much more reliable inferences ...