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Bootstrapping (electronics)

About: Bootstrapping (electronics) is a research topic. Over the lifetime, 1210 publications have been published within this topic receiving 39540 citations.


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TL;DR: This work uses computer simulations and a laboratory-generated phylogeny to test bootstrapping results of parsimony analyses, and indicates that any given bootstrap proportion provides an unbiased but highly imprecise measure of repeatability, unless the actual probability of replicating the relevant result is nearly one.
Abstract: Bootstrapping is a common method for assessing confidence in phylogenetic anal? yses. Although bootstrapping was first applied in phylogenetics to assess the repeatability of a given result, bootstrap results are commonly interpreted as a measure of the probability that a phylogenetic estimate represents the true phylogeny. Here we use computer simulations and a laboratory-generated phylogeny to test bootstrapping results of parsimony analyses, both as measures of repeatability (i.e., the probability of repeating a result given a new sample of characters) and accuracy (i.e., the probability that a result represents the true phylogeny). Our results indicate that any given bootstrap proportion provides an unbiased but highly imprecise measure of repeatability, unless the actual probability of replicating the relevant result is nearly one. The imprecision of the estimate is great enough to render the estimate virtually useless as a measure of repeatability. Under conditions thought to be typical of most phylogenetic analyses, however, bootstrap proportions in majority-rule consensus trees provide biased but highly con? servative estimates of the probability of correctly inferring the corresponding clades. Specifically, under conditions of equal rates of change, symmetric phylogenies, and internodal change of 70% usually correspond to a probability of >95% that the corresponding dade is real. However, under conditions of very high rates of internodal change (approaching randomization of the characters among taxa) or highly unequal rates of change among taxa, bootstrap proportions >50% are overestimates of accuracy. (Boot? strapping; accuracy; repeatability; phylogeny; parsimony; precision; statistical analyses; simu? lations.)

4,057 citations

Journal ArticleDOI
TL;DR: A new WHAM implementation is presented, termed g_wham, which is distributed freely with the GROMACS molecular simulation suite, and it is demonstrated that, given sufficient sampling, bootstrapping new trajectories allows for an accurate error estimate.
Abstract: The Weighted Histogram Analysis Method (WHAM) is a standard technique used to compute potentials of mean force (PMFs) from a set of umbrella sampling simulations. Here, we present a new WHAM implementation, termed g_wham, which is distributed freely with the GROMACS molecular simulation suite. g_wham estimates statistical errors using the technique of bootstrap analysis. Three bootstrap methods are supported: (i) bootstrapping new trajectories based on the umbrella histograms, (ii) bootstrapping of complete histograms, and (iii) Bayesian bootstrapping of complete histograms, that is, bootstrapping via the assignment of random weights to the histograms. Because methods ii and iii consider only complete histograms as independent data points, these methods do not require the accurate calculation of autocorrelation times. We demonstrate that, given sufficient sampling, bootstrapping new trajectories allows for an accurate error estimate. In the presence of long autocorrelations, however, (Bayesian) bootstrapp...

1,249 citations

Journal ArticleDOI
TL;DR: In this paper, the wild bootstrap method was used to fit Engel curves in expenditure data analysis, and it was shown that the standard way of bootstrapping this statistic fails.
Abstract: In general, there will be visible differences between a parametric and a nonparametric curve estimate. It is therefore quite natural to compare these in order to decide whether the parametric model could be justified. An asymptotic quantification is the distribution of the integrated squared difference between these curves. We show that the standard way of bootstrapping this statistic fails. We use and analyse a different form of bootstrapping for this task. We call this method the wild bootstrap and apply it to fitting Engel curves in expenditure data analysis.

1,229 citations

Journal ArticleDOI
TL;DR: This paper proposes a general methodology for bootstrapping in frontier models, extending the more restrictive method proposed in Simar & Wilson (1998) by allowing for heterogeneity in the structure of efficiency.
Abstract: The Data Envelopment Analysis method has been extensively used in the literature to provide measures of firms' technical efficiency. These measures allow rankings of firms by their apparent performance. The underlying frontier model is non-parametric since no particular functional form is assumed for the frontier model. Since the observations result from some data-generating process, the statistical properties of the estimated efficiency measures are essential for their interpretations. In the general multi-output multi-input framework, the bootstrap seems to offer the only means of inferring these properties (i.e. to estimate the bias and variance, and to construct confidence intervals). This paper proposes a general methodology for bootstrapping in frontier models, extending the more restrictive method proposed in Simar & Wilson (1998) by allowing for heterogeneity in the structure of efficiency. A numerical illustration with real data is provided to illustrate the methodology.

1,086 citations

Journal ArticleDOI
TL;DR: Computer simulation is used to investigate the behavior of three phylogenetic confidence methods: Bayesian posterior probabilities calculated via Markov chain Monte Carlo sampling (BMCMC-PP), maximum likelihood bootstrap proportion (ML-BP), and maximum parsimony boot strap proportion (MP-BP).
Abstract: Bayesian Markov chain Monte Carlo sampling has become increasingly popular in phylogenetics as a method for both estimating the maximum likelihood topology and for assessing nodal confidence. Despite the growing use of posterior probabilities, the relationship between the Bayesian measure of confidence and the most commonly used confidence measure in phylogenetics, the nonparametric bootstrap proportion, is poorly understood. We used computer simulation to investigate the behavior of three phylogenetic confidence methods: Bayesian posterior probabilities calculated via Markov chain Monte Carlo sampling (BMCMC-PP), maximum likelihood bootstrap proportion (ML-BP), and maximum parsimony bootstrap proportion (MP-BP). We simulated the evolution of DNA sequence on 17-taxon topologies under 18 evolutionary scenarios and examined the performance of these methods in assigning confidence to correct monophyletic and incorrect monophyletic groups, and we examined the effects of increasing character number on support value. BMCMC-PP and ML-BP were often strongly correlated with one another but could provide substantially different estimates of support on short internodes. In contrast, BMCMC-PP correlated poorly with MP-BP across most of the simulation conditions that we examined. For a given threshold value, more correct monophyletic groups were supported by BMCMC-PP than by either ML-BP or MP-BP. When threshold values were chosen that fixed the rate of accepting incorrect monophyletic relationship as true at 5%, all three methods recovered most of the correct relationships on the simulated topologies, although BMCMC-PP and ML-BP performed better than MP-BP. BMCMC-PP was usually a less biased predictor of phylogenetic accuracy than either bootstrapping method. BMCMC-PP provided high support values for correct topological bipartitions with fewer characters than was needed for nonparametric bootstrap.

949 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20222
202131
202029
201954
201838
201754