The bootstrap: a tutorial

Wehrens, Ron; Putter, Hein; Buydens, L.M.C.

doi:10.1016/s0169-7439(00)00102-7

Cited by 416 publications

(269 citation statements)

References 21 publications

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“…The basic premise of this method is that the data set is representative of the population from which it was drawn. Because there is only one data set, bootstrapping simulates what would happen if the population were resampled by randomly resampling the data set (Efron and Tibshirani 1993;Wehrens et al 2000). An illustration of the use of bootstrap resampling to derive confidence intervals for the parameters of a classification model is provided in Worth and Cronin (2000).…”

Section: Assessing Parameter Uncertaintymentioning

confidence: 99%

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

Eriksson

Jaworska

Worth

et al. 2003

Environ Health Perspect

1,155

832

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This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.

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Section: Assessing Parameter Uncertaintymentioning

confidence: 99%

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

Eriksson

Jaworska

Worth

et al. 2003

Environ Health Perspect

1,155

832

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show abstract

“…These were evaluated with confidence intervals calculated from bootstraps with replacement [32]. First, we estimated a smoothed spline for the empirical data and smoothed splines for each of 10 000 permutations of the data.…”

Section: (C) Drivers Of Interaction Turnovermentioning

confidence: 99%

Geographical variation in mutualistic networks: similarity, turnover and partner fidelity

et al. 2015

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Although species and their interactions in unison represent biodiversity and all the ecological and evolutionary processes associated with life, biotic interactions have, contrary to species, rarely been integrated into the concepts of spatial b-diversity. Here, we examine b-diversity of ecological networks by using pollination networks sampled across the Canary Islands. We show that adjacent and distant communities are more and less similar, respectively, in their composition of plants, pollinators and interactions than expected from random distributions. We further show that replacement of species is the major driver of interaction turnover and that this contribution increases with distance. Finally, we quantify that species-specific partner compositions (here called partner fidelity) deviate from random partner use, but vary as a result of ecological and geographical variables. In particular, breakdown of partner fidelity was facilitated by increasing geographical distance, changing abundances and changing linkage levels, but was not related to the geographical distribution of the species. This highlights the importance of space when comparing communities of interacting species and may stimulate a rethinking of the spatial interpretation of interaction networks. Moreover, geographical interaction dynamics and its causes are important in our efforts to anticipate effects of large-scale changes, such as anthropogenic disturbances.

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“…When comparing 2 different neural network classifiers, on a given test set, we used their corresponding outputs to evaluate whether they produced significantly different ROC areas or not. Statistical significance was evaluated using a permutation test 20 where we considered a P b 0.05 as statistically significant.…”

Section: Statisticsmentioning

confidence: 99%

Best leads in the standard electrocardiogram for the emergency detection of acute coronary syndrome

Green

Ohlsson

Forberg

et al. 2007

Journal of Electrocardiology

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The bootstrap: a tutorial

Cited by 416 publications

References 21 publications

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

Geographical variation in mutualistic networks: similarity, turnover and partner fidelity

Best leads in the standard electrocardiogram for the emergency detection of acute coronary syndrome

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