Does Bootstrap Procedure Provide Biased Estimates? An Empirical Examination for a Case of Multiple Regression.

ERIC Educational Resources Information Center

Fan, Xitao

This paper empirically and systematically assessed the performance of bootstrap resampling procedure as it was applied to a regression model. Parameter estimates from Monte Carlo experiments (repeated sampling from population) and bootstrap experiments (repeated resampling from one original bootstrap sample) were generated and compared. Sample…

The PIT-trap—A “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses

PubMed Central

Thibaut, Loïc; Wang, Yi Alice

2017-01-01

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)—common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of “model-free bootstrap”, adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods. PMID:28738071

Efficient bootstrap estimates for tail statistics

NASA Astrophysics Data System (ADS)

Breivik, Øyvind; Aarnes, Ole Johan

2017-03-01

Bootstrap resamples can be used to investigate the tail of empirical distributions as well as return value estimates from the extremal behaviour of the sample. Specifically, the confidence intervals on return value estimates or bounds on in-sample tail statistics can be obtained using bootstrap techniques. However, non-parametric bootstrapping from the entire sample is expensive. It is shown here that it suffices to bootstrap from a small subset consisting of the highest entries in the sequence to make estimates that are essentially identical to bootstraps from the entire sample. Similarly, bootstrap estimates of confidence intervals of threshold return estimates are found to be well approximated by using a subset consisting of the highest entries. This has practical consequences in fields such as meteorology, oceanography and hydrology where return values are calculated from very large gridded model integrations spanning decades at high temporal resolution or from large ensembles of independent and identically distributed model fields. In such cases the computational savings are substantial.

Kepler Planet Detection Metrics: Statistical Bootstrap Test

NASA Technical Reports Server (NTRS)

Jenkins, Jon M.; Burke, Christopher J.

2016-01-01

This document describes the data produced by the Statistical Bootstrap Test over the final three Threshold Crossing Event (TCE) deliveries to NExScI: SOC 9.1 (Q1Q16)1 (Tenenbaum et al. 2014), SOC 9.2 (Q1Q17) aka DR242 (Seader et al. 2015), and SOC 9.3 (Q1Q17) aka DR253 (Twicken et al. 2016). The last few years have seen significant improvements in the SOC science data processing pipeline, leading to higher quality light curves and more sensitive transit searches. The statistical bootstrap analysis results presented here and the numerical results archived at NASAs Exoplanet Science Institute (NExScI) bear witness to these software improvements. This document attempts to introduce and describe the main features and differences between these three data sets as a consequence of the software changes.

Bootstrap investigation of the stability of a Cox regression model.

PubMed

Altman, D G; Andersen, P K

1989-07-01

We describe a bootstrap investigation of the stability of a Cox proportional hazards regression model resulting from the analysis of a clinical trial of azathioprine versus placebo in patients with primary biliary cirrhosis. We have considered stability to refer both to the choice of variables included in the model and, more importantly, to the predictive ability of the model. In stepwise Cox regression analyses of 100 bootstrap samples using 17 candidate variables, the most frequently selected variables were those selected in the original analysis, and no other important variable was identified. Thus there was no reason to doubt the model obtained in the original analysis. For each patient in the trial, bootstrap confidence intervals were constructed for the estimated probability of surviving two years. It is shown graphically that these intervals are markedly wider than those obtained from the original model.

Uncertainty quantification of CO₂ saturation estimated from electrical resistance tomography data at the Cranfield site

DOE PAGES

Yang, Xianjin; Chen, Xiao; Carrigan, Charles R.; ...

2014-06-03

A parametric bootstrap approach is presented for uncertainty quantification (UQ) of CO₂ saturation derived from electrical resistance tomography (ERT) data collected at the Cranfield, Mississippi (USA) carbon sequestration site. There are many sources of uncertainty in ERT-derived CO₂ saturation, but we focus on how the ERT observation errors propagate to the estimated CO₂ saturation in a nonlinear inversion process. Our UQ approach consists of three steps. We first estimated the observational errors from a large number of reciprocal ERT measurements. The second step was to invert the pre-injection baseline data and the resulting resistivity tomograph was used as the priormore » information for nonlinear inversion of time-lapse data. We assigned a 3% random noise to the baseline model. Finally, we used a parametric bootstrap method to obtain bootstrap CO₂ saturation samples by deterministically solving a nonlinear inverse problem many times with resampled data and resampled baseline models. Then the mean and standard deviation of CO₂ saturation were calculated from the bootstrap samples. We found that the maximum standard deviation of CO₂ saturation was around 6% with a corresponding maximum saturation of 30% for a data set collected 100 days after injection began. There was no apparent spatial correlation between the mean and standard deviation of CO₂ saturation but the standard deviation values increased with time as the saturation increased. The uncertainty in CO₂ saturation also depends on the ERT reciprocal error threshold used to identify and remove noisy data and inversion constraints such as temporal roughness. Five hundred realizations requiring 3.5 h on a single 12-core node were needed for the nonlinear Monte Carlo inversion to arrive at stationary variances while the Markov Chain Monte Carlo (MCMC) stochastic inverse approach may expend days for a global search. This indicates that UQ of 2D or 3D ERT inverse problems can be performed on a laptop or desktop PC.« less

Visuospatial bootstrapping: implicit binding of verbal working memory to visuospatial representations in children and adults.

PubMed

Darling, Stephen; Parker, Mary-Jane; Goodall, Karen E; Havelka, Jelena; Allen, Richard J

2014-03-01

When participants carry out visually presented digit serial recall, their performance is better if they are given the opportunity to encode extra visuospatial information at encoding-a phenomenon that has been termed visuospatial bootstrapping. This bootstrapping is the result of integration of information from different modality-specific short-term memory systems and visuospatial knowledge in long term memory, and it can be understood in the context of recent models of working memory that address multimodal binding (e.g., models incorporating an episodic buffer). Here we report a cross-sectional developmental study that demonstrated visuospatial bootstrapping in adults (n=18) and 9-year-old children (n=15) but not in 6-year-old children (n=18). This is the first developmental study addressing visuospatial bootstrapping, and results demonstrate that the developmental trajectory of bootstrapping is different from that of basic verbal and visuospatial working memory. This pattern suggests that bootstrapping (and hence integrative functions such as those associated with the episodic buffer) emerge independent of the development of basic working memory slave systems during childhood. Copyright © 2013 Elsevier Inc. All rights reserved.

Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection

NASA Technical Reports Server (NTRS)

Kumar, Sricharan; Srivistava, Ashok N.

2012-01-01

Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.

Pearson-type goodness-of-fit test with bootstrap maximum likelihood estimation.

PubMed

Yin, Guosheng; Ma, Yanyuan

2013-01-01

The Pearson test statistic is constructed by partitioning the data into bins and computing the difference between the observed and expected counts in these bins. If the maximum likelihood estimator (MLE) of the original data is used, the statistic generally does not follow a chi-squared distribution or any explicit distribution. We propose a bootstrap-based modification of the Pearson test statistic to recover the chi-squared distribution. We compute the observed and expected counts in the partitioned bins by using the MLE obtained from a bootstrap sample. This bootstrap-sample MLE adjusts exactly the right amount of randomness to the test statistic, and recovers the chi-squared distribution. The bootstrap chi-squared test is easy to implement, as it only requires fitting exactly the same model to the bootstrap data to obtain the corresponding MLE, and then constructs the bin counts based on the original data. We examine the test size and power of the new model diagnostic procedure using simulation studies and illustrate it with a real data set.

Factors influencing foraging search efficiency: why do scarce lappet-faced vultures outperform ubiquitous white-backed vultures?

PubMed

Spiegel, Orr; Getz, Wayne M; Nathan, Ran

2013-05-01

The search phase is a critical component of foraging behavior, affecting interspecific competition and community dynamics. Nevertheless, factors determining interspecific variation in search efficiency are still poorly understood. We studied differences in search efficiency between the lappet-faced vulture (Torgos tracheliotus; LFV) and the white-backed vulture (Gyps africanus; WBV) foraging on spatiotemporally unpredictable carcasses in Etosha National Park, Namibia. We used experimental food supply and high-resolution GPS tracking of free-ranging vultures to quantify search efficiency and elucidate the factors underlying the observed interspecific differences using a biased correlated random walk simulation model bootstrapped with the GPS tracking data. We found that LFV's search efficiency was higher than WBV's in both first-to-find, first-to-land, and per-individual-finding rate measures. Modifying species-specific traits in the simulation model allows us to assess the relative role of each factor in LFV's higher efficiency. Interspecific differences in morphology (through the effect on perceptual range and motion ability) and searchers' spatial dispersion (due to different roost arrangements) are in correspondence with the empirically observed advantage of LFV over WBV searchers, whereas differences in other aspects of the movement patterns appear to play a minor role. Our results provide mechanistic explanations for interspecific variation in search efficiency for species using similar resources and foraging modes.

Unbiased Estimates of Variance Components with Bootstrap Procedures

ERIC Educational Resources Information Center

Brennan, Robert L.

2007-01-01

This article provides general procedures for obtaining unbiased estimates of variance components for any random-model balanced design under any bootstrap sampling plan, with the focus on designs of the type typically used in generalizability theory. The results reported here are particularly helpful when the bootstrap is used to estimate standard…

Learning predictive models that use pattern discovery--a bootstrap evaluative approach applied in organ functioning sequences.

PubMed

Toma, Tudor; Bosman, Robert-Jan; Siebes, Arno; Peek, Niels; Abu-Hanna, Ameen

2010-08-01

An important problem in the Intensive Care is how to predict on a given day of stay the eventual hospital mortality for a specific patient. A recent approach to solve this problem suggested the use of frequent temporal sequences (FTSs) as predictors. Methods following this approach were evaluated in the past by inducing a model from a training set and validating the prognostic performance on an independent test set. Although this evaluative approach addresses the validity of the specific models induced in an experiment, it falls short of evaluating the inductive method itself. To achieve this, one must account for the inherent sources of variation in the experimental design. The main aim of this work is to demonstrate a procedure based on bootstrapping, specifically the .632 bootstrap procedure, for evaluating inductive methods that discover patterns, such as FTSs. A second aim is to apply this approach to find out whether a recently suggested inductive method that discovers FTSs of organ functioning status is superior over a traditional method that does not use temporal sequences when compared on each successive day of stay at the Intensive Care Unit. The use of bootstrapping with logistic regression using pre-specified covariates is known in the statistical literature. Using inductive methods of prognostic models based on temporal sequence discovery within the bootstrap procedure is however novel at least in predictive models in the Intensive Care. Our results of applying the bootstrap-based evaluative procedure demonstrate the superiority of the FTS-based inductive method over the traditional method in terms of discrimination as well as accuracy. In addition we illustrate the insights gained by the analyst into the discovered FTSs from the bootstrap samples. Copyright 2010 Elsevier Inc. All rights reserved.

Topical ketoprofen nanogel: artificial neural network optimization, clustered bootstrap validation, and in vivo activity evaluation based on longitudinal dose response modeling.

PubMed

Elkomy, Mohammed H; Elmenshawe, Shahira F; Eid, Hussein M; Ali, Ahmed M A

2016-11-01

This work aimed at investigating the potential of solid lipid nanoparticles (SLN) as carriers for topical delivery of Ketoprofen (KP); evaluating a novel technique incorporating Artificial Neural Network (ANN) and clustered bootstrap for optimization of KP-loaded SLN (KP-SLN); and demonstrating a longitudinal dose response (LDR) modeling-based approach to compare the activity of topical non-steroidal anti-inflammatory drug formulations. KP-SLN was fabricated by a modified emulsion/solvent evaporation method. Box-Behnken design was implemented to study the influence of glycerylpalmitostearate-to-KP ratio, Tween 80, and lecithin concentrations on particle size, entrapment efficiency, and amount of drug permeated through rat skin in 24 hours. Following clustered bootstrap ANN optimization, the optimized KP-SLN was incorporated into an aqueous gel and evaluated for rheology, in vitro release, permeability, skin irritation and in vivo activity using carrageenan-induced rat paw edema model and LDR mathematical model to analyze the time course of anti-inflammatory effect at various application durations. Lipid-to-drug ratio of 7.85 [bootstrap 95%CI: 7.63-8.51], Tween 80 of 1.27% [bootstrap 95%CI: 0.601-2.40%], and Lecithin of 0.263% [bootstrap 95%CI: 0.263-0.328%] were predicted to produce optimal characteristics. Compared with profenid® gel, the optimized KP-SLN gel exhibited slower release, faster permeability, better texture properties, greater efficacy, and similar potency. SLNs are safe and effective permeation enhancers. ANN coupled with clustered bootstrap is a useful method for finding optimal solutions and estimating uncertainty associated with them. LDR models allow mechanistic understanding of comparative in vivo performances of different topical formulations, and help design efficient dermatological bioequivalence assessment methods.

Variable selection under multiple imputation using the bootstrap in a prognostic study

PubMed Central

Heymans, Martijn W; van Buuren, Stef; Knol, Dirk L; van Mechelen, Willem; de Vet, Henrica CW

2007-01-01

Background Missing data is a challenging problem in many prognostic studies. Multiple imputation (MI) accounts for imputation uncertainty that allows for adequate statistical testing. We developed and tested a methodology combining MI with bootstrapping techniques for studying prognostic variable selection. Method In our prospective cohort study we merged data from three different randomized controlled trials (RCTs) to assess prognostic variables for chronicity of low back pain. Among the outcome and prognostic variables data were missing in the range of 0 and 48.1%. We used four methods to investigate the influence of respectively sampling and imputation variation: MI only, bootstrap only, and two methods that combine MI and bootstrapping. Variables were selected based on the inclusion frequency of each prognostic variable, i.e. the proportion of times that the variable appeared in the model. The discriminative and calibrative abilities of prognostic models developed by the four methods were assessed at different inclusion levels. Results We found that the effect of imputation variation on the inclusion frequency was larger than the effect of sampling variation. When MI and bootstrapping were combined at the range of 0% (full model) to 90% of variable selection, bootstrap corrected c-index values of 0.70 to 0.71 and slope values of 0.64 to 0.86 were found. Conclusion We recommend to account for both imputation and sampling variation in sets of missing data. The new procedure of combining MI with bootstrapping for variable selection, results in multivariable prognostic models with good performance and is therefore attractive to apply on data sets with missing values. PMID:17629912

Combining test statistics and models in bootstrapped model rejection: it is a balancing act

PubMed Central

2014-01-01

Background Model rejections lie at the heart of systems biology, since they provide conclusive statements: that the corresponding mechanistic assumptions do not serve as valid explanations for the experimental data. Rejections are usually done using e.g. the chi-square test (χ2) or the Durbin-Watson test (DW). Analytical formulas for the corresponding distributions rely on assumptions that typically are not fulfilled. This problem is partly alleviated by the usage of bootstrapping, a computationally heavy approach to calculate an empirical distribution. Bootstrapping also allows for a natural extension to estimation of joint distributions, but this feature has so far been little exploited. Results We herein show that simplistic combinations of bootstrapped tests, like the max or min of the individual p-values, give inconsistent, i.e. overly conservative or liberal, results. A new two-dimensional (2D) approach based on parametric bootstrapping, on the other hand, is found both consistent and with a higher power than the individual tests, when tested on static and dynamic examples where the truth is known. In the same examples, the most superior test is a 2D χ2vsχ2, where the second χ2-value comes from an additional help model, and its ability to describe bootstraps from the tested model. This superiority is lost if the help model is too simple, or too flexible. If a useful help model is found, the most powerful approach is the bootstrapped log-likelihood ratio (LHR). We show that this is because the LHR is one-dimensional, because the second dimension comes at a cost, and because LHR has retained most of the crucial information in the 2D distribution. These approaches statistically resolve a previously published rejection example for the first time. Conclusions We have shown how to, and how not to, combine tests in a bootstrap setting, when the combination is advantageous, and when it is advantageous to include a second model. These results also provide a deeper insight into the original motivation for formulating the LHR, for the more general setting of nonlinear and non-nested models. These insights are valuable in cases when accuracy and power, rather than computational speed, are prioritized. PMID:24742065

Four Bootstrap Confidence Intervals for the Binomial-Error Model.

ERIC Educational Resources Information Center

Lin, Miao-Hsiang; Hsiung, Chao A.

1992-01-01

Four bootstrap methods are identified for constructing confidence intervals for the binomial-error model. The extent to which similar results are obtained and the theoretical foundation of each method and its relevance and ranges of modeling the true score uncertainty are discussed. (SLD)

Data Validation in the Kepler Science Operations Center Pipeline

NASA Technical Reports Server (NTRS)

Wu, Hayley; Twicken, Joseph D.; Tenenbaum, Peter; Clarke, Bruce D.; Li, Jie; Quintana, Elisa V.; Allen, Christopher; Chandrasekaran, Hema; Jenkins, Jon M.; Caldwell, Douglas A.;

Long multiplet bootstrap

NASA Astrophysics Data System (ADS)

Cornagliotto, Martina; Lemos, Madalena; Schomerus, Volker

2017-10-01

Applications of the bootstrap program to superconformal field theories promise unique new insights into their landscape and could even lead to the discovery of new models. Most existing results of the superconformal bootstrap were obtained form correlation functions of very special fields in short (BPS) representations of the superconformal algebra. Our main goal is to initiate a superconformal bootstrap for long multiplets, one that exploits all constraints from superprimaries and their descendants. To this end, we work out the Casimir equations for four-point correlators of long multiplets of the two-dimensional global N=2 superconformal algebra. After constructing the full set of conformal blocks we discuss two different applications. The first one concerns two-dimensional (2,0) theories. The numerical bootstrap analysis we perform serves a twofold purpose, as a feasibility study of our long multiplet bootstrap and also as an exploration of (2,0) theories. A second line of applications is directed towards four-dimensional N=3 SCFTs. In this context, our results imply a new bound c≥ 13/24 for the central charge of such models, which we argue cannot be saturated by an interacting SCFT.

Weak percolation on multiplex networks

NASA Astrophysics Data System (ADS)

Baxter, Gareth J.; Dorogovtsev, Sergey N.; Mendes, José F. F.; Cellai, Davide

2014-04-01

Bootstrap percolation is a simple but nontrivial model. It has applications in many areas of science and has been explored on random networks for several decades. In single-layer (simplex) networks, it has been recently observed that bootstrap percolation, which is defined as an incremental process, can be seen as the opposite of pruning percolation, where nodes are removed according to a connectivity rule. Here we propose models of both bootstrap and pruning percolation for multiplex networks. We collectively refer to these two models with the concept of "weak" percolation, to distinguish them from the somewhat classical concept of ordinary ("strong") percolation. While the two models coincide in simplex networks, we show that they decouple when considering multiplexes, giving rise to a wealth of critical phenomena. Our bootstrap model constitutes the simplest example of a contagion process on a multiplex network and has potential applications in critical infrastructure recovery and information security. Moreover, we show that our pruning percolation model may provide a way to diagnose missing layers in a multiplex network. Finally, our analytical approach allows us to calculate critical behavior and characterize critical clusters.

Genetic Algorithm Based Framework for Automation of Stochastic Modeling of Multi-Season Streamflows

NASA Astrophysics Data System (ADS)

Srivastav, R. K.; Srinivasan, K.; Sudheer, K.

2009-05-01

Synthetic streamflow data generation involves the synthesis of likely streamflow patterns that are statistically indistinguishable from the observed streamflow data. The various kinds of stochastic models adopted for multi-season streamflow generation in hydrology are: i) parametric models which hypothesize the form of the periodic dependence structure and the distributional form a priori (examples are PAR, PARMA); disaggregation models that aim to preserve the correlation structure at the periodic level and the aggregated annual level; ii) Nonparametric models (examples are bootstrap/kernel based methods), which characterize the laws of chance, describing the stream flow process, without recourse to prior assumptions as to the form or structure of these laws; (k-nearest neighbor (k-NN), matched block bootstrap (MABB)); non-parametric disaggregation model. iii) Hybrid models which blend both parametric and non-parametric models advantageously to model the streamflows effectively. Despite many of these developments that have taken place in the field of stochastic modeling of streamflows over the last four decades, accurate prediction of the storage and the critical drought characteristics has been posing a persistent challenge to the stochastic modeler. This is partly because, usually, the stochastic streamflow model parameters are estimated by minimizing a statistically based objective function (such as maximum likelihood (MLE) or least squares (LS) estimation) and subsequently the efficacy of the models is being validated based on the accuracy of prediction of the estimates of the water-use characteristics, which requires large number of trial simulations and inspection of many plots and tables. Still accurate prediction of the storage and the critical drought characteristics may not be ensured. In this study a multi-objective optimization framework is proposed to find the optimal hybrid model (blend of a simple parametric model, PAR(1) model and matched block bootstrap (MABB) ) based on the explicit objective functions of minimizing the relative bias and relative root mean square error in estimating the storage capacity of the reservoir. The optimal parameter set of the hybrid model is obtained based on the search over a multi- dimensional parameter space (involving simultaneous exploration of the parametric (PAR(1)) as well as the non-parametric (MABB) components). This is achieved using the efficient evolutionary search based optimization tool namely, non-dominated sorting genetic algorithm - II (NSGA-II). This approach helps in reducing the drudgery involved in the process of manual selection of the hybrid model, in addition to predicting the basic summary statistics dependence structure, marginal distribution and water-use characteristics accurately. The proposed optimization framework is used to model the multi-season streamflows of River Beaver and River Weber of USA. In case of both the rivers, the proposed GA-based hybrid model yields a much better prediction of the storage capacity (where simultaneous exploration of both parametric and non-parametric components is done) when compared with the MLE-based hybrid models (where the hybrid model selection is done in two stages, thus probably resulting in a sub-optimal model). This framework can be further extended to include different linear/non-linear hybrid stochastic models at other temporal and spatial scales as well.

Coupling of PIES 3-D Equilibrium Code and NIFS Bootstrap Code with Applications to the Computation of Stellarator Equilibria

NASA Astrophysics Data System (ADS)

Monticello, D. A.; Reiman, A. H.; Watanabe, K. Y.; Nakajima, N.; Okamoto, M.

1997-11-01

The existence of bootstrap currents in both tokamaks and stellarators was confirmed, experimentally, more than ten years ago. Such currents can have significant effects on the equilibrium and stability of these MHD devices. In addition, stellarators, with the notable exception of W7-X, are predicted to have such large bootstrap currents that reliable equilibrium calculations require the self-consistent evaluation of bootstrap currents. Modeling of discharges which contain islands requires an algorithm that does not assume good surfaces. Only one of the two 3-D equilibrium codes that exist, PIES( Reiman, A. H., Greenside, H. S., Compt. Phys. Commun. 43), (1986)., can easily be modified to handle bootstrap current. Here we report on the coupling of the PIES 3-D equilibrium code and NIFS bootstrap code(Watanabe, K., et al., Nuclear Fusion 35) (1995), 335.

Electron transport fluxes in potato plateau regime

NASA Astrophysics Data System (ADS)

Shaing, K. C.; Hazeltine, R. D.

1997-12-01

Electron transport fluxes in the potato plateau regime are calculated from the solutions of the drift kinetic equation and fluid equations. It is found that the bootstrap current density remains finite in the region close to the magnetic axis, although it decreases with increasing collision frequency. This finite amount of the bootstrap current in the relatively collisional regime is important in modeling tokamak startup with 100% bootstrap current.

Bootstrap current in a tokamak

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kessel, C.E.

1994-03-01

The bootstrap current in a tokamak is examined by implementing the Hirshman-Sigmar model and comparing the predicted current profiles with those from two popular approximations. The dependences of the bootstrap current profile on the plasma properties are illustrated. The implications for steady state tokamaks are presented through two constraints; the pressure profile must be peaked and {beta}{sub p} must be kept below a critical value.

Abstract: Inference and Interval Estimation for Indirect Effects With Latent Variable Models.

PubMed

Falk, Carl F; Biesanz, Jeremy C

2011-11-30

Models specifying indirect effects (or mediation) and structural equation modeling are both popular in the social sciences. Yet relatively little research has compared methods that test for indirect effects among latent variables and provided precise estimates of the effectiveness of different methods. This simulation study provides an extensive comparison of methods for constructing confidence intervals and for making inferences about indirect effects with latent variables. We compared the percentile (PC) bootstrap, bias-corrected (BC) bootstrap, bias-corrected accelerated (BC a ) bootstrap, likelihood-based confidence intervals (Neale & Miller, 1997), partial posterior predictive (Biesanz, Falk, and Savalei, 2010), and joint significance tests based on Wald tests or likelihood ratio tests. All models included three reflective latent variables representing the independent, dependent, and mediating variables. The design included the following fully crossed conditions: (a) sample size: 100, 200, and 500; (b) number of indicators per latent variable: 3 versus 5; (c) reliability per set of indicators: .7 versus .9; (d) and 16 different path combinations for the indirect effect (α = 0, .14, .39, or .59; and β = 0, .14, .39, or .59). Simulations were performed using a WestGrid cluster of 1680 3.06GHz Intel Xeon processors running R and OpenMx. Results based on 1,000 replications per cell and 2,000 resamples per bootstrap method indicated that the BC and BC a bootstrap methods have inflated Type I error rates. Likelihood-based confidence intervals and the PC bootstrap emerged as methods that adequately control Type I error and have good coverage rates.

Fish tracking by combining motion based segmentation and particle filtering

NASA Astrophysics Data System (ADS)

Bichot, E.; Mascarilla, L.; Courtellemont, P.

2006-01-01

In this paper, we suggest a new importance sampling scheme to improve a particle filtering based tracking process. This scheme relies on exploitation of motion segmentation. More precisely, we propagate hypotheses from particle filtering to blobs of similar motion to target. Hence, search is driven toward regions of interest in the state space and prediction is more accurate. We also propose to exploit segmentation to update target model. Once the moving target has been identified, a representative model is learnt from its spatial support. We refer to this model in the correction step of the tracking process. The importance sampling scheme and the strategy to update target model improve the performance of particle filtering in complex situations of occlusions compared to a simple Bootstrap approach as shown by our experiments on real fish tank sequences.

Model specification and bootstrapping for multiply imputed data: An application to count models for the frequency of alcohol use

PubMed Central

Comulada, W. Scott

2015-01-01

Stata’s mi commands provide powerful tools to conduct multiple imputation in the presence of ignorable missing data. In this article, I present Stata code to extend the capabilities of the mi commands to address two areas of statistical inference where results are not easily aggregated across imputed datasets. First, mi commands are restricted to covariate selection. I show how to address model fit to correctly specify a model. Second, the mi commands readily aggregate model-based standard errors. I show how standard errors can be bootstrapped for situations where model assumptions may not be met. I illustrate model specification and bootstrapping on frequency counts for the number of times that alcohol was consumed in data with missing observations from a behavioral intervention. PMID:26973439

On the Model-Based Bootstrap with Missing Data: Obtaining a "P"-Value for a Test of Exact Fit

ERIC Educational Resources Information Center

Savalei, Victoria; Yuan, Ke-Hai

2009-01-01

Evaluating the fit of a structural equation model via bootstrap requires a transformation of the data so that the null hypothesis holds exactly in the sample. For complete data, such a transformation was proposed by Beran and Srivastava (1985) for general covariance structure models and applied to structural equation modeling by Bollen and Stine…

A Gateway for Phylogenetic Analysis Powered by Grid Computing Featuring GARLI 2.0

PubMed Central

Bazinet, Adam L.; Zwickl, Derrick J.; Cummings, Michael P.

2014-01-01

We introduce molecularevolution.org, a publicly available gateway for high-throughput, maximum-likelihood phylogenetic analysis powered by grid computing. The gateway features a garli 2.0 web service that enables a user to quickly and easily submit thousands of maximum likelihood tree searches or bootstrap searches that are executed in parallel on distributed computing resources. The garli web service allows one to easily specify partitioned substitution models using a graphical interface, and it performs sophisticated post-processing of phylogenetic results. Although the garli web service has been used by the research community for over three years, here we formally announce the availability of the service, describe its capabilities, highlight new features and recent improvements, and provide details about how the grid system efficiently delivers high-quality phylogenetic results. [garli, gateway, grid computing, maximum likelihood, molecular evolution portal, phylogenetics, web service.] PMID:24789072

A gateway for phylogenetic analysis powered by grid computing featuring GARLI 2.0.

PubMed

Bazinet, Adam L; Zwickl, Derrick J; Cummings, Michael P

2014-09-01

We introduce molecularevolution.org, a publicly available gateway for high-throughput, maximum-likelihood phylogenetic analysis powered by grid computing. The gateway features a garli 2.0 web service that enables a user to quickly and easily submit thousands of maximum likelihood tree searches or bootstrap searches that are executed in parallel on distributed computing resources. The garli web service allows one to easily specify partitioned substitution models using a graphical interface, and it performs sophisticated post-processing of phylogenetic results. Although the garli web service has been used by the research community for over three years, here we formally announce the availability of the service, describe its capabilities, highlight new features and recent improvements, and provide details about how the grid system efficiently delivers high-quality phylogenetic results. © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

Bootstrap imputation with a disease probability model minimized bias from misclassification due to administrative database codes.

PubMed

van Walraven, Carl

2017-04-01

Diagnostic codes used in administrative databases cause bias due to misclassification of patient disease status. It is unclear which methods minimize this bias. Serum creatinine measures were used to determine severe renal failure status in 50,074 hospitalized patients. The true prevalence of severe renal failure and its association with covariates were measured. These were compared to results for which renal failure status was determined using surrogate measures including the following: (1) diagnostic codes; (2) categorization of probability estimates of renal failure determined from a previously validated model; or (3) bootstrap methods imputation of disease status using model-derived probability estimates. Bias in estimates of severe renal failure prevalence and its association with covariates were minimal when bootstrap methods were used to impute renal failure status from model-based probability estimates. In contrast, biases were extensive when renal failure status was determined using codes or methods in which model-based condition probability was categorized. Bias due to misclassification from inaccurate diagnostic codes can be minimized using bootstrap methods to impute condition status using multivariable model-derived probability estimates. Copyright © 2017 Elsevier Inc. All rights reserved.

Topics in Statistical Calibration

DTIC Science & Technology

2014-03-27

on a parametric bootstrap where, instead of sampling directly from the residuals , samples are drawn from a normal distribution. This procedure will...addition to centering them (Davison and Hinkley, 1997). When there are outliers in the residuals , the bootstrap distribution of x̂0 can become skewed or...based and inversion methods using the linear mixed-effects model. Then, a simple parametric bootstrap algorithm is proposed that can be used to either

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shaing, K.C.; Hazeltine, R.D.

Electron transport fluxes in the potato plateau regime are calculated from the solutions of the drift kinetic equation and fluid equations. It is found that the bootstrap current density remains finite in the region close to the magnetic axis, although it decreases with increasing collision frequency. This finite amount of the bootstrap current in the relatively collisional regime is important in modeling tokamak startup with 100{percent} bootstrap current. {copyright} {ital 1997 American Institute of Physics.}

Using Cluster Bootstrapping to Analyze Nested Data With a Few Clusters.

PubMed

Huang, Francis L

2018-04-01

Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials are performed with a low number of clusters (~20 groups). Although multilevel models are often used to analyze nested data, researchers may be concerned of potentially biased results due to having only a few groups under study. Cluster bootstrapping has been suggested as an alternative procedure when analyzing clustered data though it has seen very little use in educational and psychological studies. Using a Monte Carlo simulation that varied the number of clusters, average cluster size, and intraclass correlations, we compared standard errors using cluster bootstrapping with those derived using ordinary least squares regression and multilevel models. Results indicate that cluster bootstrapping, though more computationally demanding, can be used as an alternative procedure for the analysis of clustered data when treatment effects at the group level are of primary interest. Supplementary material showing how to perform cluster bootstrapped regressions using R is also provided.

Generalized Bootstrap Method for Assessment of Uncertainty in Semivariogram Inference

USGS Publications Warehouse

Olea, R.A.; Pardo-Iguzquiza, E.

2011-01-01

The semivariogram and its related function, the covariance, play a central role in classical geostatistics for modeling the average continuity of spatially correlated attributes. Whereas all methods are formulated in terms of the true semivariogram, in practice what can be used are estimated semivariograms and models based on samples. A generalized form of the bootstrap method to properly model spatially correlated data is used to advance knowledge about the reliability of empirical semivariograms and semivariogram models based on a single sample. Among several methods available to generate spatially correlated resamples, we selected a method based on the LU decomposition and used several examples to illustrate the approach. The first one is a synthetic, isotropic, exhaustive sample following a normal distribution, the second example is also a synthetic but following a non-Gaussian random field, and a third empirical sample consists of actual raingauge measurements. Results show wider confidence intervals than those found previously by others with inadequate application of the bootstrap. Also, even for the Gaussian example, distributions for estimated semivariogram values and model parameters are positively skewed. In this sense, bootstrap percentile confidence intervals, which are not centered around the empirical semivariogram and do not require distributional assumptions for its construction, provide an achieved coverage similar to the nominal coverage. The latter cannot be achieved by symmetrical confidence intervals based on the standard error, regardless if the standard error is estimated from a parametric equation or from bootstrap. ?? 2010 International Association for Mathematical Geosciences.

Trends and Correlation Estimation in Climate Sciences: Effects of Timescale Errors

NASA Astrophysics Data System (ADS)

Mudelsee, M.; Bermejo, M. A.; Bickert, T.; Chirila, D.; Fohlmeister, J.; Köhler, P.; Lohmann, G.; Olafsdottir, K.; Scholz, D.

2012-12-01

Trend describes time-dependence in the first moment of a stochastic process, and correlation measures the linear relation between two random variables. Accurately estimating the trend and correlation, including uncertainties, from climate time series data in the uni- and bivariate domain, respectively, allows first-order insights into the geophysical process that generated the data. Timescale errors, ubiquitious in paleoclimatology, where archives are sampled for proxy measurements and dated, poses a problem to the estimation. Statistical science and the various applied research fields, including geophysics, have almost completely ignored this problem due to its theoretical almost-intractability. However, computational adaptations or replacements of traditional error formulas have become technically feasible. This contribution gives a short overview of such an adaptation package, bootstrap resampling combined with parametric timescale simulation. We study linear regression, parametric change-point models and nonparametric smoothing for trend estimation. We introduce pairwise-moving block bootstrap resampling for correlation estimation. Both methods share robustness against autocorrelation and non-Gaussian distributional shape. We shortly touch computing-intensive calibration of bootstrap confidence intervals and consider options to parallelize the related computer code. Following examples serve not only to illustrate the methods but tell own climate stories: (1) the search for climate drivers of the Agulhas Current on recent timescales, (2) the comparison of three stalagmite-based proxy series of regional, western German climate over the later part of the Holocene, and (3) trends and transitions in benthic oxygen isotope time series from the Cenozoic. Financial support by Deutsche Forschungsgemeinschaft (FOR 668, FOR 1070, MU 1595/4-1) and the European Commission (MC ITN 238512, MC ITN 289447) is acknowledged.

External prognostic validations and comparisons of age- and gender-adjusted exercise capacity predictions.

PubMed

Kim, Esther S H; Ishwaran, Hemant; Blackstone, Eugene; Lauer, Michael S

2007-11-06

The purpose of this study was to externally validate the prognostic value of age- and gender-based nomograms and categorical definitions of impaired exercise capacity (EC). Exercise capacity predicts death, but its use in routine clinical practice is hampered by its close correlation with age and gender. For a median of 5 years, we followed 22,275 patients without known heart disease who underwent symptom-limited stress testing. Models for predicted or impaired EC were identified by literature search. Gender-specific multivariable proportional hazards models were constructed. Four methods were used to assess validity: Akaike Information Criterion (AIC), right-censored c-index in 100 out-of-bootstrap samples, the Nagelkerke Index R2, and calculation of calibration error in 100 bootstrap samples. There were 646 and 430 deaths in 13,098 men and 9,177 women, respectively. Of the 7 models tested in men, a model based on a Veterans Affairs cohort (predicted metabolic equivalents [METs] = 18 - [0.15 x age]) had the highest AIC and R2. In women, a model based on the St. James Take Heart Project (predicted METs = 14.7 - [0.13 x age]) performed best. Categorical definitions of fitness performed less well. Even after accounting for age and gender, there was still an important interaction with age, whereby predicted EC was a weaker predictor in older subjects (p for interaction <0.001 in men and 0.003 in women). Several methods describe EC accounting for age and gender-related differences, but their ability to predict mortality differ. Simple cutoff values fail to fully describe EC's strong predictive value.

The economics of bootstrapping space industries - Development of an analytic computer model

NASA Technical Reports Server (NTRS)

Goldberg, A. H.; Criswell, D. R.

1982-01-01

A simple economic model of 'bootstrapping' industrial growth in space and on the Moon is presented. An initial space manufacturing facility (SMF) is assumed to consume lunar materials to enlarge the productive capacity in space. After reaching a predetermined throughput, the enlarged SMF is devoted to products which generate revenue continuously in proportion to the accumulated output mass (such as space solar power stations). Present discounted value and physical estimates for the general factors of production (transport, capital efficiency, labor, etc.) are combined to explore optimum growth in terms of maximized discounted revenues. It is found that 'bootstrapping' reduces the fractional cost to a space industry of transport off-Earth, permits more efficient use of a given transport fleet. It is concluded that more attention should be given to structuring 'bootstrapping' scenarios in which 'learning while doing' can be more fully incorporated in program analysis.

A Large-Scale, Higher-Level, Molecular Phylogenetic Study of the Insect Order Lepidoptera (Moths and Butterflies)

PubMed Central

Regier, Jerome C.; Mitter, Charles; Zwick, Andreas; Bazinet, Adam L.; Cummings, Michael P.; Kawahara, Akito Y.; Sohn, Jae-Cheon; Zwickl, Derrick J.; Cho, Soowon; Davis, Donald R.; Baixeras, Joaquin; Brown, John; Parr, Cynthia; Weller, Susan; Lees, David C.; Mitter, Kim T.

2013-01-01

Background Higher-level relationships within the Lepidoptera, and particularly within the species-rich subclade Ditrysia, are generally not well understood, although recent studies have yielded progress. We present the most comprehensive molecular analysis of lepidopteran phylogeny to date, focusing on relationships among superfamilies. Methodology / Principal Findings 483 taxa spanning 115 of 124 families were sampled for 19 protein-coding nuclear genes, from which maximum likelihood tree estimates and bootstrap percentages were obtained using GARLI. Assessment of heuristic search effectiveness showed that better trees and higher bootstrap percentages probably remain to be discovered even after 1000 or more search replicates, but further search proved impractical even with grid computing. Other analyses explored the effects of sampling nonsynonymous change only versus partitioned and unpartitioned total nucleotide change; deletion of rogue taxa; and compositional heterogeneity. Relationships among the non-ditrysian lineages previously inferred from morphology were largely confirmed, plus some new ones, with strong support. Robust support was also found for divergences among non-apoditrysian lineages of Ditrysia, but only rarely so within Apoditrysia. Paraphyly for Tineoidea is strongly supported by analysis of nonsynonymous-only signal; conflicting, strong support for tineoid monophyly when synonymous signal was added back is shown to result from compositional heterogeneity. Conclusions / Significance Support for among-superfamily relationships outside the Apoditrysia is now generally strong. Comparable support is mostly lacking within Apoditrysia, but dramatically increased bootstrap percentages for some nodes after rogue taxon removal, and concordance with other evidence, strongly suggest that our picture of apoditrysian phylogeny is approximately correct. This study highlights the challenge of finding optimal topologies when analyzing hundreds of taxa. It also shows that some nodes get strong support only when analysis is restricted to nonsynonymous change, while total change is necessary for strong support of others. Thus, multiple types of analyses will be necessary to fully resolve lepidopteran phylogeny. PMID:23554903

More accurate, calibrated bootstrap confidence intervals for correlating two autocorrelated climate time series

NASA Astrophysics Data System (ADS)

Olafsdottir, Kristin B.; Mudelsee, Manfred

2013-04-01

Estimation of the Pearson's correlation coefficient between two time series to evaluate the influences of one time depended variable on another is one of the most often used statistical method in climate sciences. Various methods are used to estimate confidence interval to support the correlation point estimate. Many of them make strong mathematical assumptions regarding distributional shape and serial correlation, which are rarely met. More robust statistical methods are needed to increase the accuracy of the confidence intervals. Bootstrap confidence intervals are estimated in the Fortran 90 program PearsonT (Mudelsee, 2003), where the main intention was to get an accurate confidence interval for correlation coefficient between two time series by taking the serial dependence of the process that generated the data into account. However, Monte Carlo experiments show that the coverage accuracy for smaller data sizes can be improved. Here we adapt the PearsonT program into a new version called PearsonT3, by calibrating the confidence interval to increase the coverage accuracy. Calibration is a bootstrap resampling technique, which basically performs a second bootstrap loop or resamples from the bootstrap resamples. It offers, like the non-calibrated bootstrap confidence intervals, robustness against the data distribution. Pairwise moving block bootstrap is used to preserve the serial correlation of both time series. The calibration is applied to standard error based bootstrap Student's t confidence intervals. The performances of the calibrated confidence intervals are examined with Monte Carlo simulations, and compared with the performances of confidence intervals without calibration, that is, PearsonT. The coverage accuracy is evidently better for the calibrated confidence intervals where the coverage error is acceptably small (i.e., within a few percentage points) already for data sizes as small as 20. One form of climate time series is output from numerical models which simulate the climate system. The method is applied to model data from the high resolution ocean model, INALT01 where the relationship between the Agulhas Leakage and the North Brazil Current is evaluated. Preliminary results show significant correlation between the two variables when there is 10 year lag between them, which is more or less the time that takes the Agulhas Leakage water to reach the North Brazil Current. Mudelsee, M., 2003. Estimating Pearson's correlation coefficient with bootstrap confidence interval from serially dependent time series. Mathematical Geology 35, 651-665.

Bootstrap Percolation on Homogeneous Trees Has 2 Phase Transitions

NASA Astrophysics Data System (ADS)

Fontes, L. R. G.; Schonmann, R. H.

2008-09-01

We study the threshold θ bootstrap percolation model on the homogeneous tree with degree b+1, 2≤ θ≤ b, and initial density p. It is known that there exists a nontrivial critical value for p, which we call p f , such that a) for p> p f , the final bootstrapped configuration is fully occupied for almost every initial configuration, and b) if p< p f , then for almost every initial configuration, the final bootstrapped configuration has density of occupied vertices less than 1. In this paper, we establish the existence of a distinct critical value for p, p c , such that 0< p c < p f , with the following properties: 1) if p≤ p c , then for almost every initial configuration there is no infinite cluster of occupied vertices in the final bootstrapped configuration; 2) if p> p c , then for almost every initial configuration there are infinite clusters of occupied vertices in the final bootstrapped configuration. Moreover, we show that 3) for p< p c , the distribution of the occupied cluster size in the final bootstrapped configuration has an exponential tail; 4) at p= p c , the expected occupied cluster size in the final bootstrapped configuration is infinite; 5) the probability of percolation of occupied vertices in the final bootstrapped configuration is continuous on [0, p f ] and analytic on ( p c , p f ), admitting an analytic continuation from the right at p c and, only in the case θ= b, also from the left at p f .

Small sample mediation testing: misplaced confidence in bootstrapped confidence intervals.

PubMed

Koopman, Joel; Howe, Michael; Hollenbeck, John R; Sin, Hock-Peng

2015-01-01

Bootstrapping is an analytical tool commonly used in psychology to test the statistical significance of the indirect effect in mediation models. Bootstrapping proponents have particularly advocated for its use for samples of 20-80 cases. This advocacy has been heeded, especially in the Journal of Applied Psychology, as researchers are increasingly utilizing bootstrapping to test mediation with samples in this range. We discuss reasons to be concerned with this escalation, and in a simulation study focused specifically on this range of sample sizes, we demonstrate not only that bootstrapping has insufficient statistical power to provide a rigorous hypothesis test in most conditions but also that bootstrapping has a tendency to exhibit an inflated Type I error rate. We then extend our simulations to investigate an alternative empirical resampling method as well as a Bayesian approach and demonstrate that they exhibit comparable statistical power to bootstrapping in small samples without the associated inflated Type I error. Implications for researchers testing mediation hypotheses in small samples are presented. For researchers wishing to use these methods in their own research, we have provided R syntax in the online supplemental materials. (c) 2015 APA, all rights reserved.

Reliability of dose volume constraint inference from clinical data.

PubMed

Lutz, C M; Møller, D S; Hoffmann, L; Knap, M M; Alber, M

2017-04-21

Dose volume histogram points (DVHPs) frequently serve as dose constraints in radiotherapy treatment planning. An experiment was designed to investigate the reliability of DVHP inference from clinical data for multiple cohort sizes and complication incidence rates. The experimental background was radiation pneumonitis in non-small cell lung cancer and the DVHP inference method was based on logistic regression. From 102 NSCLC real-life dose distributions and a postulated DVHP model, an 'ideal' cohort was generated where the most predictive model was equal to the postulated model. A bootstrap and a Cohort Replication Monte Carlo (CoRepMC) approach were applied to create 1000 equally sized populations each. The cohorts were then analyzed to establish inference frequency distributions. This was applied to nine scenarios for cohort sizes of 102 (1), 500 (2) to 2000 (3) patients (by sampling with replacement) and three postulated DVHP models. The Bootstrap was repeated for a 'non-ideal' cohort, where the most predictive model did not coincide with the postulated model. The Bootstrap produced chaotic results for all models of cohort size 1 for both the ideal and non-ideal cohorts. For cohort size 2 and 3, the distributions for all populations were more concentrated around the postulated DVHP. For the CoRepMC, the inference frequency increased with cohort size and incidence rate. Correct inference rates  >[Formula: see text] were only achieved by cohorts with more than 500 patients. Both Bootstrap and CoRepMC indicate that inference of the correct or approximate DVHP for typical cohort sizes is highly uncertain. CoRepMC results were less spurious than Bootstrap results, demonstrating the large influence that randomness in dose-response has on the statistical analysis.

Reliability of dose volume constraint inference from clinical data

NASA Astrophysics Data System (ADS)

Lutz, C. M.; Møller, D. S.; Hoffmann, L.; Knap, M. M.; Alber, M.

2017-04-01

Dose volume histogram points (DVHPs) frequently serve as dose constraints in radiotherapy treatment planning. An experiment was designed to investigate the reliability of DVHP inference from clinical data for multiple cohort sizes and complication incidence rates. The experimental background was radiation pneumonitis in non-small cell lung cancer and the DVHP inference method was based on logistic regression. From 102 NSCLC real-life dose distributions and a postulated DVHP model, an ‘ideal’ cohort was generated where the most predictive model was equal to the postulated model. A bootstrap and a Cohort Replication Monte Carlo (CoRepMC) approach were applied to create 1000 equally sized populations each. The cohorts were then analyzed to establish inference frequency distributions. This was applied to nine scenarios for cohort sizes of 102 (1), 500 (2) to 2000 (3) patients (by sampling with replacement) and three postulated DVHP models. The Bootstrap was repeated for a ‘non-ideal’ cohort, where the most predictive model did not coincide with the postulated model. The Bootstrap produced chaotic results for all models of cohort size 1 for both the ideal and non-ideal cohorts. For cohort size 2 and 3, the distributions for all populations were more concentrated around the postulated DVHP. For the CoRepMC, the inference frequency increased with cohort size and incidence rate. Correct inference rates  >85 % were only achieved by cohorts with more than 500 patients. Both Bootstrap and CoRepMC indicate that inference of the correct or approximate DVHP for typical cohort sizes is highly uncertain. CoRepMC results were less spurious than Bootstrap results, demonstrating the large influence that randomness in dose-response has on the statistical analysis.

In Search of the Ultimate Building Blocks

NASA Astrophysics Data System (ADS)

't Hooft, Gerard

1996-12-01

An apology; 1. The beginning of the journey to the small: cutting paper; 2. To molecules and atoms; 3. The magic mystery of the quanta; 4. Dazzling velocities; 5. The elementary particle zoo before 1970; 6. Life and death; 7. The crazy kaons; 8. The invisible quarks; 9. Fields or bootstraps?; 10. The Yang-Mills bonanza; 11. Superconducting empty space: the Higgs-Kibble machine; 12. Models; 13. Colouring in the strong forces; 14. The magnetic monopole; 15. Gypsy; 16. The brilliance of the standard model; 17. Anomalies; 18. Deceptive perfection; 19. Weighing neutrinos; 20. The great desert; 21. Technicolor; 22. Grand unification; 23. Supergravity; 24. Eleven dimensional space-time; 25. Attaching the super string; 26. Into the black hole; 27. Theories that do not yet exist … ; 28. Dominance of the rule of the smallest.

Evaluating sufficient similarity for drinking-water disinfection by-product (DBP) mixtures with bootstrap hypothesis test procedures.

PubMed

Feder, Paul I; Ma, Zhenxu J; Bull, Richard J; Teuschler, Linda K; Rice, Glenn

2009-01-01

In chemical mixtures risk assessment, the use of dose-response data developed for one mixture to estimate risk posed by a second mixture depends on whether the two mixtures are sufficiently similar. While evaluations of similarity may be made using qualitative judgments, this article uses nonparametric statistical methods based on the "bootstrap" resampling technique to address the question of similarity among mixtures of chemical disinfectant by-products (DBP) in drinking water. The bootstrap resampling technique is a general-purpose, computer-intensive approach to statistical inference that substitutes empirical sampling for theoretically based parametric mathematical modeling. Nonparametric, bootstrap-based inference involves fewer assumptions than parametric normal theory based inference. The bootstrap procedure is appropriate, at least in an asymptotic sense, whether or not the parametric, distributional assumptions hold, even approximately. The statistical analysis procedures in this article are initially illustrated with data from 5 water treatment plants (Schenck et al., 2009), and then extended using data developed from a study of 35 drinking-water utilities (U.S. EPA/AMWA, 1989), which permits inclusion of a greater number of water constituents and increased structure in the statistical models.

Measuring and Benchmarking Technical Efficiency of Public Hospitals in Tianjin, China: A Bootstrap-Data Envelopment Analysis Approach.

PubMed

Li, Hao; Dong, Siping

2015-01-01

China has long been stuck in applying traditional data envelopment analysis (DEA) models to measure technical efficiency of public hospitals without bias correction of efficiency scores. In this article, we have introduced the Bootstrap-DEA approach from the international literature to analyze the technical efficiency of public hospitals in Tianjin (China) and tried to improve the application of this method for benchmarking and inter-organizational learning. It is found that the bias corrected efficiency scores of Bootstrap-DEA differ significantly from those of the traditional Banker, Charnes, and Cooper (BCC) model, which means that Chinese researchers need to update their DEA models for more scientific calculation of hospital efficiency scores. Our research has helped shorten the gap between China and the international world in relative efficiency measurement and improvement of hospitals. It is suggested that Bootstrap-DEA be widely applied into afterward research to measure relative efficiency and productivity of Chinese hospitals so as to better serve for efficiency improvement and related decision making. © The Author(s) 2015.

Multilingual Phoneme Models for Rapid Speech Processing System Development

DTIC Science & Technology

2006-09-01

processes are used to develop an Arabic speech recognition system starting from monolingual English models, In- ternational Phonetic Association (IPA...clusters. It was found that multilingual bootstrapping methods out- perform monolingual English bootstrapping methods on the Arabic evaluation data initially...International Phonetic Alphabet . . . . . . . . . 7 2.3.2 Multilingual vs. Monolingual Speech Recognition 7 2.3.3 Data-Driven Approaches

Incorporating external evidence in trial-based cost-effectiveness analyses: the use of resampling methods

PubMed Central

2014-01-01

Background Cost-effectiveness analyses (CEAs) that use patient-specific data from a randomized controlled trial (RCT) are popular, yet such CEAs are criticized because they neglect to incorporate evidence external to the trial. A popular method for quantifying uncertainty in a RCT-based CEA is the bootstrap. The objective of the present study was to further expand the bootstrap method of RCT-based CEA for the incorporation of external evidence. Methods We utilize the Bayesian interpretation of the bootstrap and derive the distribution for the cost and effectiveness outcomes after observing the current RCT data and the external evidence. We propose simple modifications of the bootstrap for sampling from such posterior distributions. Results In a proof-of-concept case study, we use data from a clinical trial and incorporate external evidence on the effect size of treatments to illustrate the method in action. Compared to the parametric models of evidence synthesis, the proposed approach requires fewer distributional assumptions, does not require explicit modeling of the relation between external evidence and outcomes of interest, and is generally easier to implement. A drawback of this approach is potential computational inefficiency compared to the parametric Bayesian methods. Conclusions The bootstrap method of RCT-based CEA can be extended to incorporate external evidence, while preserving its appealing features such as no requirement for parametric modeling of cost and effectiveness outcomes. PMID:24888356

Incorporating external evidence in trial-based cost-effectiveness analyses: the use of resampling methods.

PubMed

Sadatsafavi, Mohsen; Marra, Carlo; Aaron, Shawn; Bryan, Stirling

2014-06-03

Cost-effectiveness analyses (CEAs) that use patient-specific data from a randomized controlled trial (RCT) are popular, yet such CEAs are criticized because they neglect to incorporate evidence external to the trial. A popular method for quantifying uncertainty in a RCT-based CEA is the bootstrap. The objective of the present study was to further expand the bootstrap method of RCT-based CEA for the incorporation of external evidence. We utilize the Bayesian interpretation of the bootstrap and derive the distribution for the cost and effectiveness outcomes after observing the current RCT data and the external evidence. We propose simple modifications of the bootstrap for sampling from such posterior distributions. In a proof-of-concept case study, we use data from a clinical trial and incorporate external evidence on the effect size of treatments to illustrate the method in action. Compared to the parametric models of evidence synthesis, the proposed approach requires fewer distributional assumptions, does not require explicit modeling of the relation between external evidence and outcomes of interest, and is generally easier to implement. A drawback of this approach is potential computational inefficiency compared to the parametric Bayesian methods. The bootstrap method of RCT-based CEA can be extended to incorporate external evidence, while preserving its appealing features such as no requirement for parametric modeling of cost and effectiveness outcomes.

A Bootstrap Algorithm for Mixture Models and Interval Data in Inter-Comparisons

DTIC Science & Technology

2001-07-01

parametric bootstrap. The present algorithm will be applied to a thermometric inter-comparison, where data cannot be assumed to be normally distributed. 2 Data...experimental methods, used in each laboratory) often imply that the statistical assumptions are not satisfied, as for example in several thermometric ...triangular). Indeed, in thermometric experiments these three probabilistic models can represent several common stochastic variabilities for

Probabilistic sensitivity analysis incorporating the bootstrap: an example comparing treatments for the eradication of Helicobacter pylori.

PubMed

Pasta, D J; Taylor, J L; Henning, J M

1999-01-01

Decision-analytic models are frequently used to evaluate the relative costs and benefits of alternative therapeutic strategies for health care. Various types of sensitivity analysis are used to evaluate the uncertainty inherent in the models. Although probabilistic sensitivity analysis is more difficult theoretically and computationally, the results can be much more powerful and useful than deterministic sensitivity analysis. The authors show how a Monte Carlo simulation can be implemented using standard software to perform a probabilistic sensitivity analysis incorporating the bootstrap. The method is applied to a decision-analytic model evaluating the cost-effectiveness of Helicobacter pylori eradication. The necessary steps are straightforward and are described in detail. The use of the bootstrap avoids certain difficulties encountered with theoretical distributions. The probabilistic sensitivity analysis provided insights into the decision-analytic model beyond the traditional base-case and deterministic sensitivity analyses and should become the standard method for assessing sensitivity.

Refining search terms for nanotechnology

NASA Astrophysics Data System (ADS)

Porter, Alan L.; Youtie, Jan; Shapira, Philip; Schoeneck, David J.

2008-05-01

The ability to delineate the boundaries of an emerging technology is central to obtaining an understanding of the technology's research paths and commercialization prospects. Nowhere is this more relevant than in the case of nanotechnology (hereafter identified as "nano") given its current rapid growth and multidisciplinary nature. (Under the rubric of nanotechnology, we also include nanoscience and nanoengineering.) Past efforts have utilized several strategies, including simple term search for the prefix nano, complex lexical and citation-based approaches, and bootstrapping techniques. This research introduces a modularized Boolean approach to defining nanotechnology which has been applied to several research and patenting databases. We explain our approach to downloading and cleaning data, and report initial results. Comparisons of this approach with other nanotechnology search formulations are presented. Implications for search strategy development and profiling of the nanotechnology field are discussed.

Bootstrapping the Syntactic Bootstrapper: Probabilistic Labeling of Prosodic Phrases

ERIC Educational Resources Information Center

Gutman, Ariel; Dautriche, Isabelle; Crabbé, Benoît; Christophe, Anne

2015-01-01

The "syntactic bootstrapping" hypothesis proposes that syntactic structure provides children with cues for learning the meaning of novel words. In this article, we address the question of how children might start acquiring some aspects of syntax before they possess a sizeable lexicon. The study presents two models of early syntax…

Quantitative body DW-MRI biomarkers uncertainty estimation using unscented wild-bootstrap.

PubMed

Freiman, M; Voss, S D; Mulkern, R V; Perez-Rossello, J M; Warfield, S K

2011-01-01

We present a new method for the uncertainty estimation of diffusion parameters for quantitative body DW-MRI assessment. Diffusion parameters uncertainty estimation from DW-MRI is necessary for clinical applications that use these parameters to assess pathology. However, uncertainty estimation using traditional techniques requires repeated acquisitions, which is undesirable in routine clinical use. Model-based bootstrap techniques, for example, assume an underlying linear model for residuals rescaling and cannot be utilized directly for body diffusion parameters uncertainty estimation due to the non-linearity of the body diffusion model. To offset this limitation, our method uses the Unscented transform to compute the residuals rescaling parameters from the non-linear body diffusion model, and then applies the wild-bootstrap method to infer the body diffusion parameters uncertainty. Validation through phantom and human subject experiments shows that our method identify the regions with higher uncertainty in body DWI-MRI model parameters correctly with realtive error of -36% in the uncertainty values.

Benchmarking the efficiency of the Chilean water and sewerage companies: a double-bootstrap approach.

PubMed

Molinos-Senante, María; Donoso, Guillermo; Sala-Garrido, Ramon; Villegas, Andrés

2018-03-01

Benchmarking the efficiency of water companies is essential to set water tariffs and to promote their sustainability. In doing so, most of the previous studies have applied conventional data envelopment analysis (DEA) models. However, it is a deterministic method that does not allow to identify environmental factors influencing efficiency scores. To overcome this limitation, this paper evaluates the efficiency of a sample of Chilean water and sewerage companies applying a double-bootstrap DEA model. Results evidenced that the ranking of water and sewerage companies changes notably whether efficiency scores are computed applying conventional or double-bootstrap DEA models. Moreover, it was found that the percentage of non-revenue water and customer density are factors influencing the efficiency of Chilean water and sewerage companies. This paper illustrates the importance of using a robust and reliable method to increase the relevance of benchmarking tools.

Computing Robust, Bootstrap-Adjusted Fit Indices for Use with Nonnormal Data

ERIC Educational Resources Information Center

Walker, David A.; Smith, Thomas J.

2017-01-01

Nonnormality of data presents unique challenges for researchers who wish to carry out structural equation modeling. The subsequent SPSS syntax program computes bootstrap-adjusted fit indices (comparative fit index, Tucker-Lewis index, incremental fit index, and root mean square error of approximation) that adjust for nonnormality, along with the…

Higher curvature gravities, unlike GR, cannot be bootstrapped from their (usual) linearizations

NASA Astrophysics Data System (ADS)

Deser, S.

2017-12-01

We show that higher curvature order gravities, in particular the propagating quadratic curvature models, cannot be derived by self-coupling from their linear, flat space, forms, except through an unphysical version of linearization; only GR can. Separately, we comment on an early version of the self-coupling bootstrap.

A neural network based reputation bootstrapping approach for service selection

NASA Astrophysics Data System (ADS)

Wu, Quanwang; Zhu, Qingsheng; Li, Peng

2015-10-01

With the concept of service-oriented computing becoming widely accepted in enterprise application integration, more and more computing resources are encapsulated as services and published online. Reputation mechanism has been studied to establish trust on prior unknown services. One of the limitations of current reputation mechanisms is that they cannot assess the reputation of newly deployed services as no record of their previous behaviours exists. Most of the current bootstrapping approaches merely assign default reputation values to newcomers. However, by this kind of methods, either newcomers or existing services will be favoured. In this paper, we present a novel reputation bootstrapping approach, where correlations between features and performance of existing services are learned through an artificial neural network (ANN) and they are then generalised to establish a tentative reputation when evaluating new and unknown services. Reputations of services published previously by the same provider are also incorporated for reputation bootstrapping if available. The proposed reputation bootstrapping approach is seamlessly embedded into an existing reputation model and implemented in the extended service-oriented architecture. Empirical studies of the proposed approach are shown at last.

A Pre-Screening Questionnaire to Predict Non-24-Hour Sleep-Wake Rhythm Disorder (N24HSWD) among the Blind

PubMed Central

Flynn-Evans, Erin E.; Lockley, Steven W.

2016-01-01

Study Objectives: There is currently no questionnaire-based pre-screening tool available to detect non-24-hour sleep-wake rhythm disorder (N24HSWD) among blind patients. Our goal was to develop such a tool, derived from gold standard, objective hormonal measures of circadian entrainment status, for the detection of N24HSWD among those with visual impairment. Methods: We evaluated the contribution of 40 variables in their ability to predict N24HSWD among 127 blind women, classified using urinary 6-sulfatoxymelatonin period, an objective marker of circadian entrainment status in this population. We subjected the 40 candidate predictors to 1,000 bootstrapped iterations of a logistic regression forward selection model to predict N24HSWD, with model inclusion set at the p < 0.05 level. We removed any predictors that were not selected at least 1% of the time in the 1,000 bootstrapped models and applied a second round of 1,000 bootstrapped logistic regression forward selection models to the remaining 23 candidate predictors. We included all questions that were selected at least 10% of the time in the final model. We subjected the selected predictors to a final logistic regression model to predict N24SWD over 1,000 bootstrapped models to calculate the concordance statistic and adjusted optimism of the final model. We used this information to generate a predictive model and determined the sensitivity and specificity of the model. Finally, we applied the model to a cohort of 1,262 blind women who completed the survey, but did not collect urine samples. Results: The final model consisted of eight questions. The concordance statistic, adjusted for bootstrapping, was 0.85. The positive predictive value was 88%, the negative predictive value was 79%. Applying this model to our larger dataset of women, we found that 61% of those without light perception, and 27% with some degree of light perception, would be referred for further screening for N24HSWD. Conclusions: Our model has predictive utility sufficient to serve as a pre-screening questionnaire for N24HSWD among the blind. Citation: Flynn-Evans EE, Lockley SW. A pre-screening questionnaire to predict non-24-hour sleep-wake rhythm disorder (N24HSWD) among the blind. J Clin Sleep Med 2016;12(5):703–710. PMID:26951421

A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Hayashi, Kentaro; Yanagihara, Hirokazu

2007-01-01

Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given…

Monte Carlo based statistical power analysis for mediation models: methods and software.

PubMed

Zhang, Zhiyong

2014-12-01

The existing literature on statistical power analysis for mediation models often assumes data normality and is based on a less powerful Sobel test instead of the more powerful bootstrap test. This study proposes to estimate statistical power to detect mediation effects on the basis of the bootstrap method through Monte Carlo simulation. Nonnormal data with excessive skewness and kurtosis are allowed in the proposed method. A free R package called bmem is developed to conduct the power analysis discussed in this study. Four examples, including a simple mediation model, a multiple-mediator model with a latent mediator, a multiple-group mediation model, and a longitudinal mediation model, are provided to illustrate the proposed method.

Measuring and Benchmarking Technical Efficiency of Public Hospitals in Tianjin, China

PubMed Central

Li, Hao; Dong, Siping

2015-01-01

China has long been stuck in applying traditional data envelopment analysis (DEA) models to measure technical efficiency of public hospitals without bias correction of efficiency scores. In this article, we have introduced the Bootstrap-DEA approach from the international literature to analyze the technical efficiency of public hospitals in Tianjin (China) and tried to improve the application of this method for benchmarking and inter-organizational learning. It is found that the bias corrected efficiency scores of Bootstrap-DEA differ significantly from those of the traditional Banker, Charnes, and Cooper (BCC) model, which means that Chinese researchers need to update their DEA models for more scientific calculation of hospital efficiency scores. Our research has helped shorten the gap between China and the international world in relative efficiency measurement and improvement of hospitals. It is suggested that Bootstrap-DEA be widely applied into afterward research to measure relative efficiency and productivity of Chinese hospitals so as to better serve for efficiency improvement and related decision making. PMID:26396090

MultiPhyl: a high-throughput phylogenomics webserver using distributed computing

PubMed Central

Keane, Thomas M.; Naughton, Thomas J.; McInerney, James O.

2007-01-01

With the number of fully sequenced genomes increasing steadily, there is greater interest in performing large-scale phylogenomic analyses from large numbers of individual gene families. Maximum likelihood (ML) has been shown repeatedly to be one of the most accurate methods for phylogenetic construction. Recently, there have been a number of algorithmic improvements in maximum-likelihood-based tree search methods. However, it can still take a long time to analyse the evolutionary history of many gene families using a single computer. Distributed computing refers to a method of combining the computing power of multiple computers in order to perform some larger overall calculation. In this article, we present the first high-throughput implementation of a distributed phylogenetics platform, MultiPhyl, capable of using the idle computational resources of many heterogeneous non-dedicated machines to form a phylogenetics supercomputer. MultiPhyl allows a user to upload hundreds or thousands of amino acid or nucleotide alignments simultaneously and perform computationally intensive tasks such as model selection, tree searching and bootstrapping of each of the alignments using many desktop machines. The program implements a set of 88 amino acid models and 56 nucleotide maximum likelihood models and a variety of statistical methods for choosing between alternative models. A MultiPhyl webserver is available for public use at: http://www.cs.nuim.ie/distributed/multiphyl.php. PMID:17553837

Insight from uncertainty: bootstrap-derived diffusion metrics differentially predict memory function among older adults.

PubMed

Vorburger, Robert S; Habeck, Christian G; Narkhede, Atul; Guzman, Vanessa A; Manly, Jennifer J; Brickman, Adam M

2016-01-01

Diffusion tensor imaging suffers from an intrinsic low signal-to-noise ratio. Bootstrap algorithms have been introduced to provide a non-parametric method to estimate the uncertainty of the measured diffusion parameters. To quantify the variability of the principal diffusion direction, bootstrap-derived metrics such as the cone of uncertainty have been proposed. However, bootstrap-derived metrics are not independent of the underlying diffusion profile. A higher mean diffusivity causes a smaller signal-to-noise ratio and, thus, increases the measurement uncertainty. Moreover, the goodness of the tensor model, which relies strongly on the complexity of the underlying diffusion profile, influences bootstrap-derived metrics as well. The presented simulations clearly depict the cone of uncertainty as a function of the underlying diffusion profile. Since the relationship of the cone of uncertainty and common diffusion parameters, such as the mean diffusivity and the fractional anisotropy, is not linear, the cone of uncertainty has a different sensitivity. In vivo analysis of the fornix reveals the cone of uncertainty to be a predictor of memory function among older adults. No significant correlation occurs with the common diffusion parameters. The present work not only demonstrates the cone of uncertainty as a function of the actual diffusion profile, but also discloses the cone of uncertainty as a sensitive predictor of memory function. Future studies should incorporate bootstrap-derived metrics to provide more comprehensive analysis.

Transformational leadership in the consumer service workgroup: competing models of job satisfaction, change commitment, and cooperative conflict resolution.

PubMed

Yang, Yi-Feng

2014-02-01

This paper discusses the effects of transformational leadership on cooperative conflict resolution (management) by evaluating several alternative models related to the mediating role of job satisfaction and change commitment. Samples of data from customer service personnel in Taiwan were analyzed. Based on the bootstrap sample technique, an empirical study was carried out to yield the best fitting model. The procedure of hierarchical nested model analysis was used, incorporating the methods of bootstrapping mediation, PRODCLIN2, and structural equation modeling (SEM) comparison. The analysis suggests that leadership that promotes integration (change commitment) and provides inspiration and motivation (job satisfaction), in the proper order, creates the means for cooperative conflict resolution.

Examining Competing Models of Transformational Leadership, Leadership Trust, Change Commitment, and Job Satisfaction.

PubMed

Yang, Yi-Feng

2016-08-01

This study discusses the influence of transformational leadership on job satisfaction through assessing six alternative models related to the mediators of leadership trust and change commitment utilizing a data sample (N = 341; M age = 32.5 year, SD = 5.2) for service promotion personnel in Taiwan. The bootstrap sampling technique was used to select the better fitting model. The tool of hierarchical nested model analysis was applied, along with the approaches of bootstrapping mediation, PRODCLIN2, and structural equation modeling comparison. The results overall demonstrate that leadership is important and that leadership role identification (trust) and workgroup cohesiveness (commitment) form an ordered serial relationship. © The Author(s) 2016.

Measuring Efficiency of Tunisian Schools in the Presence of Quasi-Fixed Inputs: A Bootstrap Data Envelopment Analysis Approach

ERIC Educational Resources Information Center

Essid, Hedi; Ouellette, Pierre; Vigeant, Stephane

2010-01-01

The objective of this paper is to measure the efficiency of high schools in Tunisia. We use a statistical data envelopment analysis (DEA)-bootstrap approach with quasi-fixed inputs to estimate the precision of our measure. To do so, we developed a statistical model serving as the foundation of the data generation process (DGP). The DGP is…

New Methods for Estimating Seasonal Potential Climate Predictability

NASA Astrophysics Data System (ADS)

Feng, Xia

This study develops two new statistical approaches to assess the seasonal potential predictability of the observed climate variables. One is the univariate analysis of covariance (ANOCOVA) model, a combination of autoregressive (AR) model and analysis of variance (ANOVA). It has the advantage of taking into account the uncertainty of the estimated parameter due to sampling errors in statistical test, which is often neglected in AR based methods, and accounting for daily autocorrelation that is not considered in traditional ANOVA. In the ANOCOVA model, the seasonal signals arising from external forcing are determined to be identical or not to assess any interannual variability that may exist is potentially predictable. The bootstrap is an attractive alternative method that requires no hypothesis model and is available no matter how mathematically complicated the parameter estimator. This method builds up the empirical distribution of the interannual variance from the resamplings drawn with replacement from the given sample, in which the only predictability in seasonal means arises from the weather noise. These two methods are applied to temperature and water cycle components including precipitation and evaporation, to measure the extent to which the interannual variance of seasonal means exceeds the unpredictable weather noise compared with the previous methods, including Leith-Shukla-Gutzler (LSG), Madden, and Katz. The potential predictability of temperature from ANOCOVA model, bootstrap, LSG and Madden exhibits a pronounced tropical-extratropical contrast with much larger predictability in the tropics dominated by El Nino/Southern Oscillation (ENSO) than in higher latitudes where strong internal variability lowers predictability. Bootstrap tends to display highest predictability of the four methods, ANOCOVA lies in the middle, while LSG and Madden appear to generate lower predictability. Seasonal precipitation from ANOCOVA, bootstrap, and Katz, resembling that for temperature, is more predictable over the tropical regions, and less predictable in extropics. Bootstrap and ANOCOVA are in good agreement with each other, both methods generating larger predictability than Katz. The seasonal predictability of evaporation over land bears considerably similarity with that of temperature using ANOCOVA, bootstrap, LSG and Madden. The remote SST forcing and soil moisture reveal substantial seasonality in their relations with the potentially predictable seasonal signals. For selected regions, either SST or soil moisture or both shows significant relationships with predictable signals, hence providing indirect insight on slowly varying boundary processes involved to enable useful seasonal climate predication. A multivariate analysis of covariance (MANOCOVA) model is established to identify distinctive predictable patterns, which are uncorrelated with each other. Generally speaking, the seasonal predictability from multivariate model is consistent with that from ANOCOVA. Besides unveiling the spatial variability of predictability, MANOCOVA model also reveals the temporal variability of each predictable pattern, which could be linked to the periodic oscillations.

Analyzing hospitalization data: potential limitations of Poisson regression.

PubMed

Weaver, Colin G; Ravani, Pietro; Oliver, Matthew J; Austin, Peter C; Quinn, Robert R

2015-08-01

Poisson regression is commonly used to analyze hospitalization data when outcomes are expressed as counts (e.g. number of days in hospital). However, data often violate the assumptions on which Poisson regression is based. More appropriate extensions of this model, while available, are rarely used. We compared hospitalization data between 206 patients treated with hemodialysis (HD) and 107 treated with peritoneal dialysis (PD) using Poisson regression and compared results from standard Poisson regression with those obtained using three other approaches for modeling count data: negative binomial (NB) regression, zero-inflated Poisson (ZIP) regression and zero-inflated negative binomial (ZINB) regression. We examined the appropriateness of each model and compared the results obtained with each approach. During a mean 1.9 years of follow-up, 183 of 313 patients (58%) were never hospitalized (indicating an excess of 'zeros'). The data also displayed overdispersion (variance greater than mean), violating another assumption of the Poisson model. Using four criteria, we determined that the NB and ZINB models performed best. According to these two models, patients treated with HD experienced similar hospitalization rates as those receiving PD {NB rate ratio (RR): 1.04 [bootstrapped 95% confidence interval (CI): 0.49-2.20]; ZINB summary RR: 1.21 (bootstrapped 95% CI 0.60-2.46)}. Poisson and ZIP models fit the data poorly and had much larger point estimates than the NB and ZINB models [Poisson RR: 1.93 (bootstrapped 95% CI 0.88-4.23); ZIP summary RR: 1.84 (bootstrapped 95% CI 0.88-3.84)]. We found substantially different results when modeling hospitalization data, depending on the approach used. Our results argue strongly for a sound model selection process and improved reporting around statistical methods used for modeling count data. © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Validation of neoclassical bootstrap current models in the edge of an H-mode plasma.

PubMed

Wade, M R; Murakami, M; Politzer, P A

2004-06-11

Analysis of the parallel electric field E(parallel) evolution following an L-H transition in the DIII-D tokamak indicates the generation of a large negative pulse near the edge which propagates inward, indicative of the generation of a noninductive edge current. Modeling indicates that the observed E(parallel) evolution is consistent with a narrow current density peak generated in the plasma edge. Very good quantitative agreement is found between the measured E(parallel) evolution and that expected from neoclassical theory predictions of the bootstrap current.

Uncertainty Estimation using Bootstrapped Kriging Predictions for Precipitation Isoscapes

NASA Astrophysics Data System (ADS)

Ma, C.; Bowen, G. J.; Vander Zanden, H.; Wunder, M.

2017-12-01

Isoscapes are spatial models representing the distribution of stable isotope values across landscapes. Isoscapes of hydrogen and oxygen in precipitation are now widely used in a diversity of fields, including geology, biology, hydrology, and atmospheric science. To generate isoscapes, geostatistical methods are typically applied to extend predictions from limited data measurements. Kriging is a popular method in isoscape modeling, but quantifying the uncertainty associated with the resulting isoscapes is challenging. Applications that use precipitation isoscapes to determine sample origin require estimation of uncertainty. Here we present a simple bootstrap method (SBM) to estimate the mean and uncertainty of the krigged isoscape and compare these results with a generalized bootstrap method (GBM) applied in previous studies. We used hydrogen isotopic data from IsoMAP to explore these two approaches for estimating uncertainty. We conducted 10 simulations for each bootstrap method and found that SBM results in more kriging predictions (9/10) compared to GBM (4/10). Prediction from SBM was closer to the original prediction generated without bootstrapping and had less variance than GBM. SBM was tested on different datasets from IsoMAP with different numbers of observation sites. We determined that predictions from the datasets with fewer than 40 observation sites using SBM were more variable than the original prediction. The approaches we used for estimating uncertainty will be compiled in an R package that is under development. We expect that these robust estimates of precipitation isoscape uncertainty can be applied in diagnosing the origin of samples ranging from various type of waters to migratory animals, food products, and humans.

A bootstrap estimation scheme for chemical compositional data with nondetects

USGS Publications Warehouse

Palarea-Albaladejo, J; Martín-Fernández, J.A; Olea, Ricardo A.

2014-01-01

The bootstrap method is commonly used to estimate the distribution of estimators and their associated uncertainty when explicit analytic expressions are not available or are difficult to obtain. It has been widely applied in environmental and geochemical studies, where the data generated often represent parts of whole, typically chemical concentrations. This kind of constrained data is generically called compositional data, and they require specialised statistical methods to properly account for their particular covariance structure. On the other hand, it is not unusual in practice that those data contain labels denoting nondetects, that is, concentrations falling below detection limits. Nondetects impede the implementation of the bootstrap and represent an additional source of uncertainty that must be taken into account. In this work, a bootstrap scheme is devised that handles nondetects by adding an imputation step within the resampling process and conveniently propagates their associated uncertainly. In doing so, it considers the constrained relationships between chemical concentrations originated from their compositional nature. Bootstrap estimates using a range of imputation methods, including new stochastic proposals, are compared across scenarios of increasing difficulty. They are formulated to meet compositional principles following the log-ratio approach, and an adjustment is introduced in the multivariate case to deal with nonclosed samples. Results suggest that nondetect bootstrap based on model-based imputation is generally preferable. A robust approach based on isometric log-ratio transformations appears to be particularly suited in this context. Computer routines in the R statistical programming language are provided. 

A risk-adjusted financial model to estimate the cost of a video-assisted thoracoscopic surgery lobectomy programme.

PubMed

Brunelli, Alessandro; Tentzeris, Vasileios; Sandri, Alberto; McKenna, Alexandra; Liew, Shan Liung; Milton, Richard; Chaudhuri, Nilanjan; Kefaloyannis, Emmanuel; Papagiannopoulos, Kostas

2016-05-01

To develop a clinically risk-adjusted financial model to estimate the cost associated with a video-assisted thoracoscopic surgery (VATS) lobectomy programme. Prospectively collected data of 236 VATS lobectomy patients (August 2012-December 2013) were analysed retrospectively. Fixed and variable intraoperative and postoperative costs were retrieved from the Hospital Accounting Department. Baseline and surgical variables were tested for a possible association with total cost using a multivariable linear regression and bootstrap analyses. Costs were calculated in GBP and expressed in Euros (EUR:GBP exchange rate 1.4). The average total cost of a VATS lobectomy was €11 368 (range €6992-€62 535). Average intraoperative (including surgical and anaesthetic time, overhead, disposable materials) and postoperative costs [including ward stay, high dependency unit (HDU) or intensive care unit (ICU) and variable costs associated with management of complications] were €8226 (range €5656-€13 296) and €3029 (range €529-€51 970), respectively. The following variables remained reliably associated with total costs after linear regression analysis and bootstrap: carbon monoxide lung diffusion capacity (DLCO) <60% predicted value (P = 0.02, bootstrap 63%) and chronic obstructive pulmonary disease (COPD; P = 0.035, bootstrap 57%). The following model was developed to estimate the total costs: 10 523 + 1894 × COPD + 2376 × DLCO < 60%. The comparison between predicted and observed costs was repeated in 1000 bootstrapped samples to verify the stability of the model. The two values were not different (P > 0.05) in 86% of the samples. A hypothetical patient with COPD and DLCO less than 60% would cost €4270 more than a patient without COPD and with higher DLCO values (€14 793 vs €10 523). Risk-adjusting financial data can help estimate the total cost associated with VATS lobectomy based on clinical factors. This model can be used to audit the internal financial performance of a VATS lobectomy programme for budgeting, planning and for appropriate bundled payment reimbursements. © The Author 2015. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Bootstrapping language acquisition.

PubMed

Abend, Omri; Kwiatkowski, Tom; Smith, Nathaniel J; Goldwater, Sharon; Steedman, Mark

2017-07-01

The semantic bootstrapping hypothesis proposes that children acquire their native language through exposure to sentences of the language paired with structured representations of their meaning, whose component substructures can be associated with words and syntactic structures used to express these concepts. The child's task is then to learn a language-specific grammar and lexicon based on (probably contextually ambiguous, possibly somewhat noisy) pairs of sentences and their meaning representations (logical forms). Starting from these assumptions, we develop a Bayesian probabilistic account of semantically bootstrapped first-language acquisition in the child, based on techniques from computational parsing and interpretation of unrestricted text. Our learner jointly models (a) word learning: the mapping between components of the given sentential meaning and lexical words (or phrases) of the language, and (b) syntax learning: the projection of lexical elements onto sentences by universal construction-free syntactic rules. Using an incremental learning algorithm, we apply the model to a dataset of real syntactically complex child-directed utterances and (pseudo) logical forms, the latter including contextually plausible but irrelevant distractors. Taking the Eve section of the CHILDES corpus as input, the model simulates several well-documented phenomena from the developmental literature. In particular, the model exhibits syntactic bootstrapping effects (in which previously learned constructions facilitate the learning of novel words), sudden jumps in learning without explicit parameter setting, acceleration of word-learning (the "vocabulary spurt"), an initial bias favoring the learning of nouns over verbs, and one-shot learning of words and their meanings. The learner thus demonstrates how statistical learning over structured representations can provide a unified account for these seemingly disparate phenomena. Copyright © 2017 Elsevier B.V. All rights reserved.

Reanalysis of cancer mortality in Japanese A-bomb survivors exposed to low doses of radiation: bootstrap and simulation methods

PubMed Central

2009-01-01

Background The International Commission on Radiological Protection (ICRP) recommended annual occupational dose limit is 20 mSv. Cancer mortality in Japanese A-bomb survivors exposed to less than 20 mSv external radiation in 1945 was analysed previously, using a latency model with non-linear dose response. Questions were raised regarding statistical inference with this model. Methods Cancers with over 100 deaths in the 0 - 20 mSv subcohort of the 1950-1990 Life Span Study are analysed with Poisson regression models incorporating latency, allowing linear and non-linear dose response. Bootstrap percentile and Bias-corrected accelerated (BCa) methods and simulation of the Likelihood Ratio Test lead to Confidence Intervals for Excess Relative Risk (ERR) and tests against the linear model. Results The linear model shows significant large, positive values of ERR for liver and urinary cancers at latencies from 37 - 43 years. Dose response below 20 mSv is strongly non-linear at the optimal latencies for the stomach (11.89 years), liver (36.9), lung (13.6), leukaemia (23.66), and pancreas (11.86) and across broad latency ranges. Confidence Intervals for ERR are comparable using Bootstrap and Likelihood Ratio Test methods and BCa 95% Confidence Intervals are strictly positive across latency ranges for all 5 cancers. Similar risk estimates for 10 mSv (lagged dose) are obtained from the 0 - 20 mSv and 5 - 500 mSv data for the stomach, liver, lung and leukaemia. Dose response for the latter 3 cancers is significantly non-linear in the 5 - 500 mSv range. Conclusion Liver and urinary cancer mortality risk is significantly raised using a latency model with linear dose response. A non-linear model is strongly superior for the stomach, liver, lung, pancreas and leukaemia. Bootstrap and Likelihood-based confidence intervals are broadly comparable and ERR is strictly positive by bootstrap methods for all 5 cancers. Except for the pancreas, similar estimates of latency and risk from 10 mSv are obtained from the 0 - 20 mSv and 5 - 500 mSv subcohorts. Large and significant cancer risks for Japanese survivors exposed to less than 20 mSv external radiation from the atomic bombs in 1945 cast doubt on the ICRP recommended annual occupational dose limit. PMID:20003238

Bootstrapping non-commutative gauge theories from L∞ algebras

NASA Astrophysics Data System (ADS)

Blumenhagen, Ralph; Brunner, Ilka; Kupriyanov, Vladislav; Lüst, Dieter

2018-05-01

Non-commutative gauge theories with a non-constant NC-parameter are investigated. As a novel approach, we propose that such theories should admit an underlying L∞ algebra, that governs not only the action of the symmetries but also the dynamics of the theory. Our approach is well motivated from string theory. We recall that such field theories arise in the context of branes in WZW models and briefly comment on its appearance for integrable deformations of AdS5 sigma models. For the SU(2) WZW model, we show that the earlier proposed matrix valued gauge theory on the fuzzy 2-sphere can be bootstrapped via an L∞ algebra. We then apply this approach to the construction of non-commutative Chern-Simons and Yang-Mills theories on flat and curved backgrounds with non-constant NC-structure. More concretely, up to the second order, we demonstrate how derivative and curvature corrections to the equations of motion can be bootstrapped in an algebraic way from the L∞ algebra. The appearance of a non-trivial A∞ algebra is discussed, as well.

A combined approach of generalized additive model and bootstrap with small sample sets for fault diagnosis in fermentation process of glutamate.

PubMed

Liu, Chunbo; Pan, Feng; Li, Yun

2016-07-29

Glutamate is of great importance in food and pharmaceutical industries. There is still lack of effective statistical approaches for fault diagnosis in the fermentation process of glutamate. To date, the statistical approach based on generalized additive model (GAM) and bootstrap has not been used for fault diagnosis in fermentation processes, much less the fermentation process of glutamate with small samples sets. A combined approach of GAM and bootstrap was developed for the online fault diagnosis in the fermentation process of glutamate with small sample sets. GAM was first used to model the relationship between glutamate production and different fermentation parameters using online data from four normal fermentation experiments of glutamate. The fitted GAM with fermentation time, dissolved oxygen, oxygen uptake rate and carbon dioxide evolution rate captured 99.6 % variance of glutamate production during fermentation process. Bootstrap was then used to quantify the uncertainty of the estimated production of glutamate from the fitted GAM using 95 % confidence interval. The proposed approach was then used for the online fault diagnosis in the abnormal fermentation processes of glutamate, and a fault was defined as the estimated production of glutamate fell outside the 95 % confidence interval. The online fault diagnosis based on the proposed approach identified not only the start of the fault in the fermentation process, but also the end of the fault when the fermentation conditions were back to normal. The proposed approach only used a small sample sets from normal fermentations excitements to establish the approach, and then only required online recorded data on fermentation parameters for fault diagnosis in the fermentation process of glutamate. The proposed approach based on GAM and bootstrap provides a new and effective way for the fault diagnosis in the fermentation process of glutamate with small sample sets.

Exploration of the factor structure of the Kirton Adaption-Innovation Inventory using bootstrapping estimation.

PubMed

Im, Subin; Min, Soonhong

2013-04-01

Exploratory factor analyses of the Kirton Adaption-Innovation Inventory (KAI), which serves to measure individual cognitive styles, generally indicate three factors: sufficiency of originality, efficiency, and rule/group conformity. In contrast, a 2005 study by Im and Hu using confirmatory factor analysis supported a four-factor structure, dividing the sufficiency of originality dimension into two subdimensions, idea generation and preference for change. This study extends Im and Hu's (2005) study of a derived version of the KAI by providing additional evidence of the four-factor structure. Specifically, the authors test the robustness of the parameter estimates to the violation of normality assumptions in the sample using bootstrap methods. A bias-corrected confidence interval bootstrapping procedure conducted among a sample of 356 participants--members of the Arkansas Household Research Panel, with middle SES and average age of 55.6 yr. (SD = 13.9)--showed that the four-factor model with two subdimensions of sufficiency of originality fits the data significantly better than the three-factor model in non-normality conditions.

Nonequilibrium critical dynamics of the two-dimensional Ashkin-Teller model at the Baxter line

NASA Astrophysics Data System (ADS)

Fernandes, H. A.; da Silva, R.; Caparica, A. A.; de Felício, J. R. Drugowich

2017-04-01

We investigate the short-time universal behavior of the two-dimensional Ashkin-Teller model at the Baxter line by performing time-dependent Monte Carlo simulations. First, as preparatory results, we obtain the critical parameters by searching the optimal power-law decay of the magnetization. Thus, the dynamic critical exponents θm and θp, related to the magnetic and electric order parameters, as well as the persistence exponent θg, are estimated using heat-bath Monte Carlo simulations. In addition, we estimate the dynamic exponent z and the static critical exponents β and ν for both order parameters. We propose a refined method to estimate the static exponents that considers two different averages: one that combines an internal average using several seeds with another, which is taken over temporal variations in the power laws. Moreover, we also performed the bootstrapping method for a complementary analysis. Our results show that the ratio β /ν exhibits universal behavior along the critical line corroborating the conjecture for both magnetization and polarization.

Limitations of bootstrap current models

DOE PAGES

Belli, Emily A.; Candy, Jefferey M.; Meneghini, Orso; ...

2014-03-27

We assess the accuracy and limitations of two analytic models of the tokamak bootstrap current: (1) the well-known Sauter model and (2) a recent modification of the Sauter model by Koh et al. For this study, we use simulations from the first-principles kinetic code NEO as the baseline to which the models are compared. Tests are performed using both theoretical parameter scans as well as core- to-edge scans of real DIII-D and NSTX plasma profiles. The effects of extreme aspect ratio, large impurity fraction, energetic particles, and high collisionality are studied. In particular, the error in neglecting cross-species collisional couplingmore » – an approximation inherent to both analytic models – is quantified. Moreover, the implications of the corrections from kinetic NEO simulations on MHD equilibrium reconstructions is studied via integrated modeling with kinetic EFIT.« less

The CUORE slow monitoring systems

NASA Astrophysics Data System (ADS)

Gladstone, L.; Biare, D.; Cappelli, L.; Cushman, J. S.; Del Corso, F.; Fujikawa, B. K.; Hickerson, K. P.; Moggi, N.; Pagliarone, C. E.; Schmidt, B.; Wagaarachchi, S. L.; Welliver, B.; Winslow, L. A.

2017-09-01

CUORE is a cryogenic experiment searching primarily for neutrinoless double decay in 130Te. It will begin data-taking operations in 2016. To monitor the cryostat and detector during commissioning and data taking, we have designed and developed Slow Monitoring systems. In addition to real-time systems using LabVIEW, we have an alarm, analysis, and archiving website that uses MongoDB, AngularJS, and Bootstrap software. These modern, state of the art software packages make the monitoring system transparent, easily maintainable, and accessible on many platforms including mobile devices.

Simplified Estimation and Testing in Unbalanced Repeated Measures Designs.

PubMed

Spiess, Martin; Jordan, Pascal; Wendt, Mike

2018-05-07

In this paper we propose a simple estimator for unbalanced repeated measures design models where each unit is observed at least once in each cell of the experimental design. The estimator does not require a model of the error covariance structure. Thus, circularity of the error covariance matrix and estimation of correlation parameters and variances are not necessary. Together with a weak assumption about the reason for the varying number of observations, the proposed estimator and its variance estimator are unbiased. As an alternative to confidence intervals based on the normality assumption, a bias-corrected and accelerated bootstrap technique is considered. We also propose the naive percentile bootstrap for Wald-type tests where the standard Wald test may break down when the number of observations is small relative to the number of parameters to be estimated. In a simulation study we illustrate the properties of the estimator and the bootstrap techniques to calculate confidence intervals and conduct hypothesis tests in small and large samples under normality and non-normality of the errors. The results imply that the simple estimator is only slightly less efficient than an estimator that correctly assumes a block structure of the error correlation matrix, a special case of which is an equi-correlation matrix. Application of the estimator and the bootstrap technique is illustrated using data from a task switch experiment based on an experimental within design with 32 cells and 33 participants.

Associations between dietary and lifestyle risk factors and colorectal cancer in the Scottish population.

PubMed

Theodoratou, Evropi; Farrington, Susan M; Tenesa, Albert; McNeill, Geraldine; Cetnarskyj, Roseanne; Korakakis, Emmanouil; Din, Farhat V N; Porteous, Mary E; Dunlop, Malcolm G; Campbell, Harry

2014-01-01

Colorectal cancer (CRC) accounts for 9.7% of all cancer cases and for 8% of all cancer-related deaths. Established risk factors include personal or family history of CRC as well as lifestyle and dietary factors. We investigated the relationship between CRC and demographic, lifestyle, food and nutrient risk factors through a case-control study that included 2062 patients and 2776 controls from Scotland. Forward and backward stepwise regression was applied and the stability of the models was assessed in 1000 bootstrap samples. The variables that were automatically selected to be included by the forward or backward stepwise regression and whose selection was verified by bootstrap sampling in the current study were family history, dietary energy, 'high-energy snack foods', eggs, juice, sugar-sweetened beverages and white fish (associated with an increased CRC risk) and NSAIDs, coffee and magnesium (associated with a decreased CRC risk). Application of forward and backward stepwise regression in this CRC study identified some already established as well as some novel potential risk factors. Bootstrap findings suggest that examination of the stability of regression models by bootstrap sampling is useful in the interpretation of study findings. 'High-energy snack foods' and high-energy drinks (including sugar-sweetened beverages and fruit juices) as risk factors for CRC have not been reported previously and merit further investigation as such snacks and beverages are important contributors in European and North American diets.

Transport modeling of the DIII-D high $${{\\beta}_{p}}$$ scenario and extrapolations to ITER steady-state operation

DOE PAGES

McClenaghan, Joseph; Garofalo, Andrea M.; Meneghini, Orso; ...

2017-08-03

In this study, transport modeling of a proposed ITER steady-state scenario based on DIII-D high poloidal-beta (more » $${{\\beta}_{p}}$$ ) discharges finds that ITB formation can occur with either sufficient rotation or a negative central shear q-profile. The high $${{\\beta}_{p}}$$ scenario is characterized by a large bootstrap current fraction (80%) which reduces the demands on the external current drive, and a large radius internal transport barrier which is associated with excellent normalized confinement. Modeling predictions of the electron transport in the high $${{\\beta}_{p}}$$ scenario improve as $${{q}_{95}}$$ approaches levels similar to typical existing models of ITER steady-state and the ion transport is turbulence dominated. Typical temperature and density profiles from the non-inductive high $${{\\beta}_{p}}$$ scenario on DIII-D are scaled according to 0D modeling predictions of the requirements for achieving a $Q=5$ steady-state fusion gain in ITER with 'day one' heating and current drive capabilities. Then, TGLF turbulence modeling is carried out under systematic variations of the toroidal rotation and the core q-profile. A high bootstrap fraction, high $${{\\beta}_{p}}$$ scenario is found to be near an ITB formation threshold, and either strong negative central magnetic shear or rotation in a high bootstrap fraction are found to successfully provide the turbulence suppression required to achieve $Q=5$.« less

Why do workaholics experience depression? A study with Chinese University teachers.

PubMed

Nie, Yingzhi; Sun, Haitao

2016-10-01

This study focuses on the relationships of workaholism to job burnout and depression of university teachers. The direct and indirect (via job burnout) effects of workaholism on depression were investigated in 412 Chinese university teachers. Structural equation modeling and bootstrap method were used. Results revealed that workaholism, job burnout, and depression significantly correlated with each other. Structural equation modeling and bootstrap test indicated the partial mediation role of job burnout on the relationship between workaholism and depression. The findings shed some light on how workaholism influenced depression and provided valuable evidence for prevention of depression in work. © The Author(s) 2015.

Detecting temporal trends in species assemblages with bootstrapping procedures and hierarchical models

USGS Publications Warehouse

Gotelli, Nicholas J.; Dorazio, Robert M.; Ellison, Aaron M.; Grossman, Gary D.

2010-01-01

Quantifying patterns of temporal trends in species assemblages is an important analytical challenge in community ecology. We describe methods of analysis that can be applied to a matrix of counts of individuals that is organized by species (rows) and time-ordered sampling periods (columns). We first developed a bootstrapping procedure to test the null hypothesis of random sampling from a stationary species abundance distribution with temporally varying sampling probabilities. This procedure can be modified to account for undetected species. We next developed a hierarchical model to estimate species-specific trends in abundance while accounting for species-specific probabilities of detection. We analysed two long-term datasets on stream fishes and grassland insects to demonstrate these methods. For both assemblages, the bootstrap test indicated that temporal trends in abundance were more heterogeneous than expected under the null model. We used the hierarchical model to estimate trends in abundance and identified sets of species in each assemblage that were steadily increasing, decreasing or remaining constant in abundance over more than a decade of standardized annual surveys. Our methods of analysis are broadly applicable to other ecological datasets, and they represent an advance over most existing procedures, which do not incorporate effects of incomplete sampling and imperfect detection.

A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification.

PubMed

Jiang, Wenyu; Simon, Richard

2007-12-20

This paper first provides a critical review on some existing methods for estimating the prediction error in classifying microarray data where the number of genes greatly exceeds the number of specimens. Special attention is given to the bootstrap-related methods. When the sample size n is small, we find that all the reviewed methods suffer from either substantial bias or variability. We introduce a repeated leave-one-out bootstrap (RLOOB) method that predicts for each specimen in the sample using bootstrap learning sets of size ln. We then propose an adjusted bootstrap (ABS) method that fits a learning curve to the RLOOB estimates calculated with different bootstrap learning set sizes. The ABS method is robust across the situations we investigate and provides a slightly conservative estimate for the prediction error. Even with small samples, it does not suffer from large upward bias as the leave-one-out bootstrap and the 0.632+ bootstrap, and it does not suffer from large variability as the leave-one-out cross-validation in microarray applications. Copyright (c) 2007 John Wiley & Sons, Ltd.

Fast, Exact Bootstrap Principal Component Analysis for p > 1 million

PubMed Central

Fisher, Aaron; Caffo, Brian; Schwartz, Brian; Zipunnikov, Vadim

2015-01-01

Many have suggested a bootstrap procedure for estimating the sampling variability of principal component analysis (PCA) results. However, when the number of measurements per subject (p) is much larger than the number of subjects (n), calculating and storing the leading principal components from each bootstrap sample can be computationally infeasible. To address this, we outline methods for fast, exact calculation of bootstrap principal components, eigenvalues, and scores. Our methods leverage the fact that all bootstrap samples occupy the same n-dimensional subspace as the original sample. As a result, all bootstrap principal components are limited to the same n-dimensional subspace and can be efficiently represented by their low dimensional coordinates in that subspace. Several uncertainty metrics can be computed solely based on the bootstrap distribution of these low dimensional coordinates, without calculating or storing the p-dimensional bootstrap components. Fast bootstrap PCA is applied to a dataset of sleep electroencephalogram recordings (p = 900, n = 392), and to a dataset of brain magnetic resonance images (MRIs) (p ≈ 3 million, n = 352). For the MRI dataset, our method allows for standard errors for the first 3 principal components based on 1000 bootstrap samples to be calculated on a standard laptop in 47 minutes, as opposed to approximately 4 days with standard methods. PMID:27616801

Speeding Up Non-Parametric Bootstrap Computations for Statistics Based on Sample Moments in Small/Moderate Sample Size Applications

PubMed Central

Chaibub Neto, Elias

2015-01-01

In this paper we propose a vectorized implementation of the non-parametric bootstrap for statistics based on sample moments. Basically, we adopt the multinomial sampling formulation of the non-parametric bootstrap, and compute bootstrap replications of sample moment statistics by simply weighting the observed data according to multinomial counts instead of evaluating the statistic on a resampled version of the observed data. Using this formulation we can generate a matrix of bootstrap weights and compute the entire vector of bootstrap replications with a few matrix multiplications. Vectorization is particularly important for matrix-oriented programming languages such as R, where matrix/vector calculations tend to be faster than scalar operations implemented in a loop. We illustrate the application of the vectorized implementation in real and simulated data sets, when bootstrapping Pearson’s sample correlation coefficient, and compared its performance against two state-of-the-art R implementations of the non-parametric bootstrap, as well as a straightforward one based on a for loop. Our investigations spanned varying sample sizes and number of bootstrap replications. The vectorized bootstrap compared favorably against the state-of-the-art implementations in all cases tested, and was remarkably/considerably faster for small/moderate sample sizes. The same results were observed in the comparison with the straightforward implementation, except for large sample sizes, where the vectorized bootstrap was slightly slower than the straightforward implementation due to increased time expenditures in the generation of weight matrices via multinomial sampling. PMID:26125965

Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data.

PubMed

Moss, Robert; Zarebski, Alexander; Dawson, Peter; McCaw, James M

2016-07-01

Accurate forecasting of seasonal influenza epidemics is of great concern to healthcare providers in temperate climates, as these epidemics vary substantially in their size, timing and duration from year to year, making it a challenge to deliver timely and proportionate responses. Previous studies have shown that Bayesian estimation techniques can accurately predict when an influenza epidemic will peak many weeks in advance, using existing surveillance data, but these methods must be tailored both to the target population and to the surveillance system. Our aim was to evaluate whether forecasts of similar accuracy could be obtained for metropolitan Melbourne (Australia). We used the bootstrap particle filter and a mechanistic infection model to generate epidemic forecasts for metropolitan Melbourne (Australia) from weekly Internet search query surveillance data reported by Google Flu Trends for 2006-14. Optimal observation models were selected from hundreds of candidates using a novel approach that treats forecasts akin to receiver operating characteristic (ROC) curves. We show that the timing of the epidemic peak can be accurately predicted 4-6 weeks in advance, but that the magnitude of the epidemic peak and the overall burden are much harder to predict. We then discuss how the infection and observation models and the filtering process may be refined to improve forecast robustness, thereby improving the utility of these methods for healthcare decision support. © 2016 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.

GeoDataspaces: Simplifying Data Management Tasks with Globus

NASA Astrophysics Data System (ADS)

Malik, T.; Chard, K.; Tchoua, R. B.; Foster, I.

2014-12-01

Data and its management are central to modern scientific enterprise. Typically, geoscientists rely on observations and model output data from several disparate sources (file systems, RDBMS, spreadsheets, remote data sources). Integrated data management solutions that provide intuitive semantics and uniform interfaces, irrespective of the kind of data source are, however, lacking. Consequently, geoscientists are left to conduct low-level and time-consuming data management tasks, individually, and repeatedly for discovering each data source, often resulting in errors in handling. In this talk we will describe how the EarthCube GeoDataspace project is improving this situation for seismologists, hydrologists, and space scientists by simplifying some of the existing data management tasks that arise when developing computational models. We will demonstrate a GeoDataspace, bootstrapped with "geounits", which are self-contained metadata packages that provide complete description of all data elements associated with a model run, including input/output and parameter files, model executable and any associated libraries. Geounits link raw and derived data as well as associating provenance information describing how data was derived. We will discuss challenges in establishing geounits and describe machine learning and human annotation approaches that can be used for extracting and associating ad hoc and unstructured scientific metadata hidden in binary formats with data resources and models. We will show how geounits can improve search and discoverability of data associated with model runs. To support this model, we will describe efforts related towards creating a scalable metadata catalog that helps to maintain, search and discover geounits within the Globus network of accessible endpoints. This talk will focus on the issue of creating comprehensive personal inventories of data assets for computational geoscientists, and describe a publishing mechanism, which can be used to feed into national, international, or thematic discovery portals.

An Estimate of Avian Mortality at Communication Towers in the United States and Canada

PubMed Central

Longcore, Travis; Rich, Catherine; Mineau, Pierre; MacDonald, Beau; Bert, Daniel G.; Sullivan, Lauren M.; Mutrie, Erin; Gauthreaux, Sidney A.; Avery, Michael L.; Crawford, Robert L.; Manville, Albert M.; Travis, Emilie R.; Drake, David

2012-01-01

Avian mortality at communication towers in the continental United States and Canada is an issue of pressing conservation concern. Previous estimates of this mortality have been based on limited data and have not included Canada. We compiled a database of communication towers in the continental United States and Canada and estimated avian mortality by tower with a regression relating avian mortality to tower height. This equation was derived from 38 tower studies for which mortality data were available and corrected for sampling effort, search efficiency, and scavenging where appropriate. Although most studies document mortality at guyed towers with steady-burning lights, we accounted for lower mortality at towers without guy wires or steady-burning lights by adjusting estimates based on published studies. The resulting estimate of mortality at towers is 6.8 million birds per year in the United States and Canada. Bootstrapped subsampling indicated that the regression was robust to the choice of studies included and a comparison of multiple regression models showed that incorporating sampling, scavenging, and search efficiency adjustments improved model fit. Estimating total avian mortality is only a first step in developing an assessment of the biological significance of mortality at communication towers for individual species or groups of species. Nevertheless, our estimate can be used to evaluate this source of mortality, develop subsequent per-species mortality estimates, and motivate policy action. PMID:22558082

An estimate of avian mortality at communication towers in the United States and Canada.

PubMed

Longcore, Travis; Rich, Catherine; Mineau, Pierre; MacDonald, Beau; Bert, Daniel G; Sullivan, Lauren M; Mutrie, Erin; Gauthreaux, Sidney A; Avery, Michael L; Crawford, Robert L; Manville, Albert M; Travis, Emilie R; Drake, David

2012-01-01

Avian mortality at communication towers in the continental United States and Canada is an issue of pressing conservation concern. Previous estimates of this mortality have been based on limited data and have not included Canada. We compiled a database of communication towers in the continental United States and Canada and estimated avian mortality by tower with a regression relating avian mortality to tower height. This equation was derived from 38 tower studies for which mortality data were available and corrected for sampling effort, search efficiency, and scavenging where appropriate. Although most studies document mortality at guyed towers with steady-burning lights, we accounted for lower mortality at towers without guy wires or steady-burning lights by adjusting estimates based on published studies. The resulting estimate of mortality at towers is 6.8 million birds per year in the United States and Canada. Bootstrapped subsampling indicated that the regression was robust to the choice of studies included and a comparison of multiple regression models showed that incorporating sampling, scavenging, and search efficiency adjustments improved model fit. Estimating total avian mortality is only a first step in developing an assessment of the biological significance of mortality at communication towers for individual species or groups of species. Nevertheless, our estimate can be used to evaluate this source of mortality, develop subsequent per-species mortality estimates, and motivate policy action.

A Critical Meta-Analysis of Lens Model Studies in Human Judgment and Decision-Making

PubMed Central

Kaufmann, Esther; Reips, Ulf-Dietrich; Wittmann, Werner W.

2013-01-01

Achieving accurate judgment (‘judgmental achievement’) is of utmost importance in daily life across multiple domains. The lens model and the lens model equation provide useful frameworks for modeling components of judgmental achievement and for creating tools to help decision makers (e.g., physicians, teachers) reach better judgments (e.g., a correct diagnosis, an accurate estimation of intelligence). Previous meta-analyses of judgment and decision-making studies have attempted to evaluate overall judgmental achievement and have provided the basis for evaluating the success of bootstrapping (i.e., replacing judges by linear models that guide decision making). However, previous meta-analyses have failed to appropriately correct for a number of study design artifacts (e.g., measurement error, dichotomization), which may have potentially biased estimations (e.g., of the variability between studies) and led to erroneous interpretations (e.g., with regards to moderator variables). In the current study we therefore conduct the first psychometric meta-analysis of judgmental achievement studies that corrects for a number of study design artifacts. We identified 31 lens model studies (N = 1,151, k = 49) that met our inclusion criteria. We evaluated overall judgmental achievement as well as whether judgmental achievement depended on decision domain (e.g., medicine, education) and/or the level of expertise (expert vs. novice). We also evaluated whether using corrected estimates affected conclusions with regards to the success of bootstrapping with psychometrically-corrected models. Further, we introduce a new psychometric trim-and-fill method to estimate the effect sizes of potentially missing studies correct psychometric meta-analyses for effects of publication bias. Comparison of the results of the psychometric meta-analysis with the results of a traditional meta-analysis (which only corrected for sampling error) indicated that artifact correction leads to a) an increase in values of the lens model components, b) reduced heterogeneity between studies, and c) increases the success of bootstrapping. We argue that psychometric meta-analysis is useful for accurately evaluating human judgment and show the success of bootstrapping. PMID:24391781

A critical meta-analysis of lens model studies in human judgment and decision-making.

PubMed

Kaufmann, Esther; Reips, Ulf-Dietrich; Wittmann, Werner W

2013-01-01

Achieving accurate judgment ('judgmental achievement') is of utmost importance in daily life across multiple domains. The lens model and the lens model equation provide useful frameworks for modeling components of judgmental achievement and for creating tools to help decision makers (e.g., physicians, teachers) reach better judgments (e.g., a correct diagnosis, an accurate estimation of intelligence). Previous meta-analyses of judgment and decision-making studies have attempted to evaluate overall judgmental achievement and have provided the basis for evaluating the success of bootstrapping (i.e., replacing judges by linear models that guide decision making). However, previous meta-analyses have failed to appropriately correct for a number of study design artifacts (e.g., measurement error, dichotomization), which may have potentially biased estimations (e.g., of the variability between studies) and led to erroneous interpretations (e.g., with regards to moderator variables). In the current study we therefore conduct the first psychometric meta-analysis of judgmental achievement studies that corrects for a number of study design artifacts. We identified 31 lens model studies (N = 1,151, k = 49) that met our inclusion criteria. We evaluated overall judgmental achievement as well as whether judgmental achievement depended on decision domain (e.g., medicine, education) and/or the level of expertise (expert vs. novice). We also evaluated whether using corrected estimates affected conclusions with regards to the success of bootstrapping with psychometrically-corrected models. Further, we introduce a new psychometric trim-and-fill method to estimate the effect sizes of potentially missing studies correct psychometric meta-analyses for effects of publication bias. Comparison of the results of the psychometric meta-analysis with the results of a traditional meta-analysis (which only corrected for sampling error) indicated that artifact correction leads to a) an increase in values of the lens model components, b) reduced heterogeneity between studies, and c) increases the success of bootstrapping. We argue that psychometric meta-analysis is useful for accurately evaluating human judgment and show the success of bootstrapping.

Progress Toward Steady State Tokamak Operation Exploiting the high bootstrap current fraction regime

NASA Astrophysics Data System (ADS)

Ren, Q.

2015-11-01

Recent DIII-D experiments have advanced the normalized fusion performance of the high bootstrap current fraction tokamak regime toward reactor-relevant steady state operation. The experiments, conducted by a joint team of researchers from the DIII-D and EAST tokamaks, developed a fully noninductive scenario that could be extended on EAST to a demonstration of long pulse steady-state tokamak operation. Fully noninductive plasmas with extremely high values of the poloidal beta, βp >= 4 , have been sustained at βT >= 2 % for long durations with excellent energy confinement quality (H98y,2 >= 1 . 5) and internal transport barriers (ITBs) generated at large minor radius (>= 0 . 6) in all channels (Te, Ti, ne, VTf). Large bootstrap fraction (fBS ~ 80 %) has been obtained with high βp. ITBs have been shown to be compatible with steady state operation. Because of the unusually large ITB radius, normalized pressure is not limited to low βN values by internal ITB-driven modes. βN up to ~4.3 has been obtained by optimizing the plasma-wall distance. The scenario is robust against several variations, including replacing some on-axis with off-axis neutral beam injection (NBI), adding electron cyclotron (EC) heating, and reducing the NBI torque by a factor of 2. This latter observation is particularly promising for extension of the scenario to EAST, where maximum power is obtained with balanced NBI injection, and to a reactor, expected to have low rotation. However, modeling of this regime has provided new challenges to state-of-the-art modeling capabilities: quasilinear models can dramatically underpredict the electron transport, and the Sauter bootstrap current can be insufficient. The analysis shows first-principle NEO is in good agreement with experiments for the bootstrap current calculation and ETG modes with a larger saturated amplitude or EM modes may provide the missing electron transport. Work supported in part by the US DOE under DE-FC02-04ER54698, DE-AC52-07NA27344, DE-AC02-09CH11466, and the NMCFP of China under 2015GB110000 and 2015GB102000.

Coefficient Omega Bootstrap Confidence Intervals: Nonnormal Distributions

ERIC Educational Resources Information Center

Padilla, Miguel A.; Divers, Jasmin

2013-01-01

The performance of the normal theory bootstrap (NTB), the percentile bootstrap (PB), and the bias-corrected and accelerated (BCa) bootstrap confidence intervals (CIs) for coefficient omega was assessed through a Monte Carlo simulation under conditions not previously investigated. Of particular interests were nonnormal Likert-type and binary items.…

Tests of Independence for Ordinal Data Using Bootstrap.

ERIC Educational Resources Information Center

Chan, Wai; Yung, Yiu-Fai; Bentler, Peter M.; Tang, Man-Lai

1998-01-01

Two bootstrap tests are proposed to test the independence hypothesis in a two-way cross table. Monte Carlo studies are used to compare the traditional asymptotic test with these bootstrap methods, and the bootstrap methods are found superior in two ways: control of Type I error and statistical power. (SLD)

Integrated modeling of plasma ramp-up in DIII-D ITER-like and high bootstrap current scenario discharges

NASA Astrophysics Data System (ADS)

Wu, M. Q.; Pan, C. K.; Chan, V. S.; Li, G. Q.; Garofalo, A. M.; Jian, X.; Liu, L.; Ren, Q. L.; Chen, J. L.; Gao, X.; Gong, X. Z.; Ding, S. Y.; Qian, J. P.; Cfetr Physics Team

2018-04-01

Time-dependent integrated modeling of DIII-D ITER-like and high bootstrap current plasma ramp-up discharges has been performed with the equilibrium code EFIT, and the transport codes TGYRO and ONETWO. Electron and ion temperature profiles are simulated by TGYRO with the TGLF (SAT0 or VX model) turbulent and NEO neoclassical transport models. The VX model is a new empirical extension of the TGLF turbulent model [Jian et al., Nucl. Fusion 58, 016011 (2018)], which captures the physics of multi-scale interaction between low-k and high-k turbulence from nonlinear gyro-kinetic simulation. This model is demonstrated to accurately model low Ip discharges from the EAST tokamak. Time evolution of the plasma current density profile is simulated by ONETWO with the experimental current ramp-up rate. The general trend of the predicted evolution of the current density profile is consistent with that obtained from the equilibrium reconstruction with Motional Stark effect constraints. The predicted evolution of βN , li , and βP also agrees well with the experiments. For the ITER-like cases, the predicted electron and ion temperature profiles using TGLF_Sat0 agree closely with the experimental measured profiles, and are demonstrably better than other proposed transport models. For the high bootstrap current case, the predicted electron and ion temperature profiles perform better in the VX model. It is found that the SAT0 model works well at high IP (>0.76 MA) while the VX model covers a wider range of plasma current ( IP > 0.6 MA). The results reported in this paper suggest that the developed integrated modeling could be a candidate for ITER and CFETR ramp-up engineering design modeling.

The CUORE slow monitoring systems

DOE PAGES

Gladstone, L.; Biare, D.; Cappelli, L.; ...

2017-09-20

CUORE is a cryogenic experiment searching primarily for neutrinoless double decay inmore » $$^{130}$$Te. It will begin data-taking operations in 2016. To monitor the cryostat and detector during commissioning and data taking, we have designed and developed Slow Monitoring systems. In addition to real-time systems using LabVIEW, we have an alarm, analysis, and archiving website that uses MongoDB, AngularJS, and Bootstrap software. These modern, state of the art software packages make the monitoring system transparent, easily maintainable, and accessible on many platforms including mobile devices.« less

The CUORE slow monitoring systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gladstone, L.; Biare, D.; Cappelli, L.

CUORE is a cryogenic experiment searching primarily for neutrinoless double decay inmore » $$^{130}$$Te. It will begin data-taking operations in 2016. To monitor the cryostat and detector during commissioning and data taking, we have designed and developed Slow Monitoring systems. In addition to real-time systems using LabVIEW, we have an alarm, analysis, and archiving website that uses MongoDB, AngularJS, and Bootstrap software. These modern, state of the art software packages make the monitoring system transparent, easily maintainable, and accessible on many platforms including mobile devices.« less

Using the Descriptive Bootstrap to Evaluate Result Replicability (Because Statistical Significance Doesn't)

ERIC Educational Resources Information Center

Spinella, Sarah

2011-01-01

As result replicability is essential to science and difficult to achieve through external replicability, the present paper notes the insufficiency of null hypothesis statistical significance testing (NHSST) and explains the bootstrap as a plausible alternative, with a heuristic example to illustrate the bootstrap method. The bootstrap relies on…

Conformal Bootstrap in Mellin Space

NASA Astrophysics Data System (ADS)

Gopakumar, Rajesh; Kaviraj, Apratim; Sen, Kallol; Sinha, Aninda

2017-02-01

We propose a new approach towards analytically solving for the dynamical content of conformal field theories (CFTs) using the bootstrap philosophy. This combines the original bootstrap idea of Polyakov with the modern technology of the Mellin representation of CFT amplitudes. We employ exchange Witten diagrams with built-in crossing symmetry as our basic building blocks rather than the conventional conformal blocks in a particular channel. Demanding consistency with the operator product expansion (OPE) implies an infinite set of constraints on operator dimensions and OPE coefficients. We illustrate the power of this method in the ɛ expansion of the Wilson-Fisher fixed point by reproducing anomalous dimensions and, strikingly, obtaining OPE coefficients to higher orders in ɛ than currently available using other analytic techniques (including Feynman diagram calculations). Our results enable us to get a somewhat better agreement between certain observables in the 3D Ising model and the precise numerical values that have been recently obtained.

Accounting for Uncertainty in Decision Analytic Models Using Rank Preserving Structural Failure Time Modeling: Application to Parametric Survival Models.

PubMed

Bennett, Iain; Paracha, Noman; Abrams, Keith; Ray, Joshua

2018-01-01

Rank Preserving Structural Failure Time models are one of the most commonly used statistical methods to adjust for treatment switching in oncology clinical trials. The method is often applied in a decision analytic model without appropriately accounting for additional uncertainty when determining the allocation of health care resources. The aim of the study is to describe novel approaches to adequately account for uncertainty when using a Rank Preserving Structural Failure Time model in a decision analytic model. Using two examples, we tested and compared the performance of the novel Test-based method with the resampling bootstrap method and with the conventional approach of no adjustment. In the first example, we simulated life expectancy using a simple decision analytic model based on a hypothetical oncology trial with treatment switching. In the second example, we applied the adjustment method on published data when no individual patient data were available. Mean estimates of overall and incremental life expectancy were similar across methods. However, the bootstrapped and test-based estimates consistently produced greater estimates of uncertainty compared with the estimate without any adjustment applied. Similar results were observed when using the test based approach on a published data showing that failing to adjust for uncertainty led to smaller confidence intervals. Both the bootstrapping and test-based approaches provide a solution to appropriately incorporate uncertainty, with the benefit that the latter can implemented by researchers in the absence of individual patient data. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Nonparametric bootstrap analysis with applications to demographic effects in demand functions.

PubMed

Gozalo, P L

1997-12-01

"A new bootstrap proposal, labeled smooth conditional moment (SCM) bootstrap, is introduced for independent but not necessarily identically distributed data, where the classical bootstrap procedure fails.... A good example of the benefits of using nonparametric and bootstrap methods is the area of empirical demand analysis. In particular, we will be concerned with their application to the study of two important topics: what are the most relevant effects of household demographic variables on demand behavior, and to what extent present parametric specifications capture these effects." excerpt

Effects of magnetic islands on bootstrap current in toroidal plasmas

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dong, G.; Lin, Z.

The effects of magnetic islands on electron bootstrap current in toroidal plasmas are studied using gyrokinetic simulations. The magnetic islands cause little changes of the bootstrap current level in the banana regime because of trapped electron effects. In the plateau regime, the bootstrap current is completely suppressed at the island centers due to the destruction of trapped electron orbits by collisions and the flattening of pressure profiles by the islands. In the collisional regime, small but finite bootstrap current can exist inside the islands because of the pressure gradients created by large collisional transport across the islands. Lastly, simulation resultsmore » show that the bootstrap current level increases near the island separatrix due to steeper local density gradients.« less

Effects of magnetic islands on bootstrap current in toroidal plasmas

DOE PAGES

Dong, G.; Lin, Z.

2016-12-19

The effects of magnetic islands on electron bootstrap current in toroidal plasmas are studied using gyrokinetic simulations. The magnetic islands cause little changes of the bootstrap current level in the banana regime because of trapped electron effects. In the plateau regime, the bootstrap current is completely suppressed at the island centers due to the destruction of trapped electron orbits by collisions and the flattening of pressure profiles by the islands. In the collisional regime, small but finite bootstrap current can exist inside the islands because of the pressure gradients created by large collisional transport across the islands. Lastly, simulation resultsmore » show that the bootstrap current level increases near the island separatrix due to steeper local density gradients.« less

Variabilities in probabilistic seismic hazard maps for natural and induced seismicity in the central and eastern United States

USGS Publications Warehouse

Mousavi, S. Mostafa; Beroza, Gregory C.; Hoover, Susan M.

2018-01-01

Probabilistic seismic hazard analysis (PSHA) characterizes ground-motion hazard from earthquakes. Typically, the time horizon of a PSHA forecast is long, but in response to induced seismicity related to hydrocarbon development, the USGS developed one-year PSHA models. In this paper, we present a display of the variability in USGS hazard curves due to epistemic uncertainty in its informed submodel using a simple bootstrapping approach. We find that variability is highest in low-seismicity areas. On the other hand, areas of high seismic hazard, such as the New Madrid seismic zone or Oklahoma, exhibit relatively lower variability simply because of more available data and a better understanding of the seismicity. Comparing areas of high hazard, New Madrid, which has a history of large naturally occurring earthquakes, has lower forecast variability than Oklahoma, where the hazard is driven mainly by suspected induced earthquakes since 2009. Overall, the mean hazard obtained from bootstrapping is close to the published model, and variability increased in the 2017 one-year model relative to the 2016 model. Comparing the relative variations caused by individual logic-tree branches, we find that the highest hazard variation (as measured by the 95% confidence interval of bootstrapping samples) in the final model is associated with different ground-motion models and maximum magnitudes used in the logic tree, while the variability due to the smoothing distance is minimal. It should be pointed out that this study is not looking at the uncertainty in the hazard in general, but only as it is represented in the USGS one-year models.

Bootstrap study of genome-enabled prediction reliabilities using haplotype blocks across Nordic Red cattle breeds.

PubMed

Cuyabano, B C D; Su, G; Rosa, G J M; Lund, M S; Gianola, D

2015-10-01

This study compared the accuracy of genome-enabled prediction models using individual single nucleotide polymorphisms (SNP) or haplotype blocks as covariates when using either a single breed or a combined population of Nordic Red cattle. The main objective was to compare predictions of breeding values of complex traits using a combined training population with haplotype blocks, with predictions using a single breed as training population and individual SNP as predictors. To compare the prediction reliabilities, bootstrap samples were taken from the test data set. With the bootstrapped samples of prediction reliabilities, we built and graphed confidence ellipses to allow comparisons. Finally, measures of statistical distances were used to calculate the gain in predictive ability. Our analyses are innovative in the context of assessment of predictive models, allowing a better understanding of prediction reliabilities and providing a statistical basis to effectively calibrate whether one prediction scenario is indeed more accurate than another. An ANOVA indicated that use of haplotype blocks produced significant gains mainly when Bayesian mixture models were used but not when Bayesian BLUP was fitted to the data. Furthermore, when haplotype blocks were used to train prediction models in a combined Nordic Red cattle population, we obtained up to a statistically significant 5.5% average gain in prediction accuracy, over predictions using individual SNP and training the model with a single breed. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Tablet potency of Tianeptine in coated tablets by near infrared spectroscopy: model optimisation, calibration transfer and confidence intervals.

PubMed

Boiret, Mathieu; Meunier, Loïc; Ginot, Yves-Michel

2011-02-20

A near infrared (NIR) method was developed for determination of tablet potency of active pharmaceutical ingredient (API) in a complex coated tablet matrix. The calibration set contained samples from laboratory and production scale batches. The reference values were obtained by high performance liquid chromatography (HPLC) and partial least squares (PLS) regression was used to establish a model. The model was challenged by calculating tablet potency of two external test sets. Root mean square errors of prediction were respectively equal to 2.0% and 2.7%. To use this model with a second spectrometer from the production field, a calibration transfer method called piecewise direct standardisation (PDS) was used. After the transfer, the root mean square error of prediction of the first test set was 2.4% compared to 4.0% without transferring the spectra. A statistical technique using bootstrap of PLS residuals was used to estimate confidence intervals of tablet potency calculations. This method requires an optimised PLS model, selection of the bootstrap number and determination of the risk. In the case of a chemical analysis, the tablet potency value will be included within the confidence interval calculated by the bootstrap method. An easy to use graphical interface was developed to easily determine if the predictions, surrounded by minimum and maximum values, are within the specifications defined by the regulatory organisation. Copyright © 2010 Elsevier B.V. All rights reserved.

What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum

PubMed Central

Hesterberg, Tim C.

2015-01-01

Bootstrapping has enormous potential in statistics education and practice, but there are subtle issues and ways to go wrong. For example, the common combination of nonparametric bootstrapping and bootstrap percentile confidence intervals is less accurate than using t-intervals for small samples, though more accurate for larger samples. My goals in this article are to provide a deeper understanding of bootstrap methods—how they work, when they work or not, and which methods work better—and to highlight pedagogical issues. Supplementary materials for this article are available online. [Received December 2014. Revised August 2015] PMID:27019512

Bootstrapping in a language of thought: a formal model of numerical concept learning.

PubMed

Piantadosi, Steven T; Tenenbaum, Joshua B; Goodman, Noah D

2012-05-01

In acquiring number words, children exhibit a qualitative leap in which they transition from understanding a few number words, to possessing a rich system of interrelated numerical concepts. We present a computational framework for understanding this inductive leap as the consequence of statistical inference over a sufficiently powerful representational system. We provide an implemented model that is powerful enough to learn number word meanings and other related conceptual systems from naturalistic data. The model shows that bootstrapping can be made computationally and philosophically well-founded as a theory of number learning. Our approach demonstrates how learners may combine core cognitive operations to build sophisticated representations during the course of development, and how this process explains observed developmental patterns in number word learning. Copyright © 2011 Elsevier B.V. All rights reserved.

Monthly water quality forecasting and uncertainty assessment via bootstrapped wavelet neural networks under missing data for Harbin, China.

PubMed

Wang, Yi; Zheng, Tong; Zhao, Ying; Jiang, Jiping; Wang, Yuanyuan; Guo, Liang; Wang, Peng

2013-12-01

In this paper, bootstrapped wavelet neural network (BWNN) was developed for predicting monthly ammonia nitrogen (NH(4+)-N) and dissolved oxygen (DO) in Harbin region, northeast of China. The Morlet wavelet basis function (WBF) was employed as a nonlinear activation function of traditional three-layer artificial neural network (ANN) structure. Prediction intervals (PI) were constructed according to the calculated uncertainties from the model structure and data noise. Performance of BWNN model was also compared with four different models: traditional ANN, WNN, bootstrapped ANN, and autoregressive integrated moving average model. The results showed that BWNN could handle the severely fluctuating and non-seasonal time series data of water quality, and it produced better performance than the other four models. The uncertainty from data noise was smaller than that from the model structure for NH(4+)-N; conversely, the uncertainty from data noise was larger for DO series. Besides, total uncertainties in the low-flow period were the biggest due to complicated processes during the freeze-up period of the Songhua River. Further, a data missing-refilling scheme was designed, and better performances of BWNNs for structural data missing (SD) were observed than incidental data missing (ID). For both ID and SD, temporal method was satisfactory for filling NH(4+)-N series, whereas spatial imputation was fit for DO series. This filling BWNN forecasting method was applied to other areas suffering "real" data missing, and the results demonstrated its efficiency. Thus, the methods introduced here will help managers to obtain informed decisions.

Reduced ion bootstrap current drive on NTM instability

NASA Astrophysics Data System (ADS)

Qu, Hongpeng; Wang, Feng; Wang, Aike; Peng, Xiaodong; Li, Jiquan

2018-05-01

The loss of bootstrap current inside magnetic island plays a dominant role in driving the neoclassical tearing mode (NTM) instability in tokamak plasmas. In this work, we investigate the finite-banana-width (FBW) effect on the profile of ion bootstrap current in the island vicinity via an analytical approach. The results show that even if the pressure gradient vanishes inside the island, the ion bootstrap current can partly survive due to the FBW effect. The efficiency of the FBW effect is higher when the island width becomes smaller. Nevertheless, even when the island width is comparable to the ion FBW, the unperturbed ion bootstrap current inside the island cannot be largely recovered by the FBW effect, and thus the current loss still exists. This suggests that FBW effect alone cannot dramatically reduce the ion bootstrap current drive on NTMs.

On critical exponents without Feynman diagrams

NASA Astrophysics Data System (ADS)

Sen, Kallol; Sinha, Aninda

2016-11-01

In order to achieve a better analytic handle on the modern conformal bootstrap program, we re-examine and extend the pioneering 1974 work of Polyakov’s, which was based on consistency between the operator product expansion and unitarity. As in the bootstrap approach, this method does not depend on evaluating Feynman diagrams. We show how this approach can be used to compute the anomalous dimensions of certain operators in the O(n) model at the Wilson-Fisher fixed point in 4-ɛ dimensions up to O({ɛ }2). AS dedicates this work to the loving memory of his mother.

Bootstrapping 3D fermions

DOE PAGES

Iliesiu, Luca; Kos, Filip; Poland, David; ...

2016-03-17

We study the conformal bootstrap for a 4-point function of fermions in 3D. We first introduce an embedding formalism for 3D spinors and compute the conformal blocks appearing in fermion 4-point functions. Using these results, we find general bounds on the dimensions of operators appearing in the ψ × ψ OPE, and also on the central charge C T. We observe features in our bounds that coincide with scaling dimensions in the GrossNeveu models at large N. Finally, we also speculate that other features could coincide with a fermionic CFT containing no relevant scalar operators.

Lung cancer signature biomarkers: tissue specific semantic similarity based clustering of digital differential display (DDD) data.

PubMed

Srivastava, Mousami; Khurana, Pankaj; Sugadev, Ragumani

2012-11-02

The tissue-specific Unigene Sets derived from more than one million expressed sequence tags (ESTs) in the NCBI, GenBank database offers a platform for identifying significantly and differentially expressed tissue-specific genes by in-silico methods. Digital differential display (DDD) rapidly creates transcription profiles based on EST comparisons and numerically calculates, as a fraction of the pool of ESTs, the relative sequence abundance of known and novel genes. However, the process of identifying the most likely tissue for a specific disease in which to search for candidate genes from the pool of differentially expressed genes remains difficult. Therefore, we have used 'Gene Ontology semantic similarity score' to measure the GO similarity between gene products of lung tissue-specific candidate genes from control (normal) and disease (cancer) sets. This semantic similarity score matrix based on hierarchical clustering represents in the form of a dendrogram. The dendrogram cluster stability was assessed by multiple bootstrapping. Multiple bootstrapping also computes a p-value for each cluster and corrects the bias of the bootstrap probability. Subsequent hierarchical clustering by the multiple bootstrapping method (α = 0.95) identified seven clusters. The comparative, as well as subtractive, approach revealed a set of 38 biomarkers comprising four distinct lung cancer signature biomarker clusters (panel 1-4). Further gene enrichment analysis of the four panels revealed that each panel represents a set of lung cancer linked metastasis diagnostic biomarkers (panel 1), chemotherapy/drug resistance biomarkers (panel 2), hypoxia regulated biomarkers (panel 3) and lung extra cellular matrix biomarkers (panel 4). Expression analysis reveals that hypoxia induced lung cancer related biomarkers (panel 3), HIF and its modulating proteins (TGM2, CSNK1A1, CTNNA1, NAMPT/Visfatin, TNFRSF1A, ETS1, SRC-1, FN1, APLP2, DMBT1/SAG, AIB1 and AZIN1) are significantly down regulated. All down regulated genes in this panel were highly up regulated in most other types of cancers. These panels of proteins may represent signature biomarkers for lung cancer and will aid in lung cancer diagnosis and disease monitoring as well as in the prediction of responses to therapeutics.

Can the Direct Medical Cost of Chronic Disease Be Transferred across Different Countries? Using Cost-of-Illness Studies on Type 2 Diabetes, Epilepsy and Schizophrenia as Examples

PubMed Central

Gao, Lan; Hu, Hao; Zhao, Fei-Li; Li, Shu-Chuen

2016-01-01

Objectives To systematically review cost of illness studies for schizophrenia (SC), epilepsy (EP) and type 2 diabetes mellitus (T2DM) and explore the transferability of direct medical cost across countries. Methods A comprehensive literature search was performed to yield studies that estimated direct medical costs. A generalized linear model (GLM) with gamma distribution and log link was utilized to explore the variation in costs that accounted by the included factors. Both parametric (Random-effects model) and non-parametric (Boot-strapping) meta-analyses were performed to pool the converted raw cost data (expressed as percentage of GDP/capita of the country where the study was conducted). Results In total, 93 articles were included (40 studies were for T2DM, 34 studies for EP and 19 studies for SC). Significant variances were detected inter- and intra-disease classes for the direct medical costs. Multivariate analysis identified that GDP/capita (p<0.05) was a significant factor contributing to the large variance in the cost results. Bootstrapping meta-analysis generated more conservative estimations with slightly wider 95% confidence intervals (CI) than the parametric meta-analysis, yielding a mean (95%CI) of 16.43% (11.32, 21.54) for T2DM, 36.17% (22.34, 50.00) for SC and 10.49% (7.86, 13.41) for EP. Conclusions Converting the raw cost data into percentage of GDP/capita of individual country was demonstrated to be a feasible approach to transfer the direct medical cost across countries. The approach from our study to obtain an estimated direct cost value along with the size of specific disease population from each jurisdiction could be used for a quick check on the economic burden of particular disease for countries without such data. PMID:26814959

A fast Monte Carlo EM algorithm for estimation in latent class model analysis with an application to assess diagnostic accuracy for cervical neoplasia in women with AGC

PubMed Central

Kang, Le; Carter, Randy; Darcy, Kathleen; Kauderer, James; Liao, Shu-Yuan

2013-01-01

In this article we use a latent class model (LCM) with prevalence modeled as a function of covariates to assess diagnostic test accuracy in situations where the true disease status is not observed, but observations on three or more conditionally independent diagnostic tests are available. A fast Monte Carlo EM (MCEM) algorithm with binary (disease) diagnostic data is implemented to estimate parameters of interest; namely, sensitivity, specificity, and prevalence of the disease as a function of covariates. To obtain standard errors for confidence interval construction of estimated parameters, the missing information principle is applied to adjust information matrix estimates. We compare the adjusted information matrix based standard error estimates with the bootstrap standard error estimates both obtained using the fast MCEM algorithm through an extensive Monte Carlo study. Simulation demonstrates that the adjusted information matrix approach estimates the standard error similarly with the bootstrap methods under certain scenarios. The bootstrap percentile intervals have satisfactory coverage probabilities. We then apply the LCM analysis to a real data set of 122 subjects from a Gynecologic Oncology Group (GOG) study of significant cervical lesion (S-CL) diagnosis in women with atypical glandular cells of undetermined significance (AGC) to compare the diagnostic accuracy of a histology-based evaluation, a CA-IX biomarker-based test and a human papillomavirus (HPV) DNA test. PMID:24163493

A comparison of different statistical methods analyzing hypoglycemia data using bootstrap simulations.

PubMed

Jiang, Honghua; Ni, Xiao; Huster, William; Heilmann, Cory

2015-01-01

Hypoglycemia has long been recognized as a major barrier to achieving normoglycemia with intensive diabetic therapies. It is a common safety concern for the diabetes patients. Therefore, it is important to apply appropriate statistical methods when analyzing hypoglycemia data. Here, we carried out bootstrap simulations to investigate the performance of the four commonly used statistical models (Poisson, negative binomial, analysis of covariance [ANCOVA], and rank ANCOVA) based on the data from a diabetes clinical trial. Zero-inflated Poisson (ZIP) model and zero-inflated negative binomial (ZINB) model were also evaluated. Simulation results showed that Poisson model inflated type I error, while negative binomial model was overly conservative. However, after adjusting for dispersion, both Poisson and negative binomial models yielded slightly inflated type I errors, which were close to the nominal level and reasonable power. Reasonable control of type I error was associated with ANCOVA model. Rank ANCOVA model was associated with the greatest power and with reasonable control of type I error. Inflated type I error was observed with ZIP and ZINB models.

Bootstrapping the (A1, A2) Argyres-Douglas theory

NASA Astrophysics Data System (ADS)

Cornagliotto, Martina; Lemos, Madalena; Liendo, Pedro

2018-03-01

We apply bootstrap techniques in order to constrain the CFT data of the ( A 1 , A 2) Argyres-Douglas theory, which is arguably the simplest of the Argyres-Douglas models. We study the four-point function of its single Coulomb branch chiral ring generator and put numerical bounds on the low-lying spectrum of the theory. Of particular interest is an infinite family of semi-short multiplets labeled by the spin ℓ. Although the conformal dimensions of these multiplets are protected, their three-point functions are not. Using the numerical bootstrap we impose rigorous upper and lower bounds on their values for spins up to ℓ = 20. Through a recently obtained inversion formula, we also estimate them for sufficiently large ℓ, and the comparison of both approaches shows consistent results. We also give a rigorous numerical range for the OPE coefficient of the next operator in the chiral ring, and estimates for the dimension of the first R-symmetry neutral non-protected multiplet for small spin.

Acculturation, Income and Vegetable Consumption Behaviors Among Latino Adults in the U.S.: A Mediation Analysis with the Bootstrapping Technique.

PubMed

López, Erick B; Yamashita, Takashi

2017-02-01

This study examined whether household income mediates the relationship between acculturation and vegetable consumption among Latino adults in the U.S. Data from the 2009 to 2010 National Health and Nutrition Examination Survey were analyzed. Vegetable consumption index was created based on the frequencies of five kinds of vegetables intake. Acculturation was measured with the degree of English language use at home. Path model with bootstrapping technique was employed for mediation analysis. A significant partial mediation relationship was identified. Greater acculturation [95 % bias corrected bootstrap confident interval (BCBCI) = (0.02, 0.33)] was associated with the higher income and in turn, greater vegetable consumption. At the same time, greater acculturation was associated with lower vegetable consumption [95 % BCBCI = (-0.88, -0.07)]. Findings regarding the income as a mediator of the acculturation-dietary behavior relationship inform unique intervention programs and policy changes to address health disparities by race/ethnicity.

Transport barriers in bootstrap-driven tokamaks

NASA Astrophysics Data System (ADS)

Staebler, G. M.; Garofalo, A. M.; Pan, C.; McClenaghan, J.; Van Zeeland, M. A.; Lao, L. L.

2018-05-01

Experiments have demonstrated improved energy confinement due to the spontaneous formation of an internal transport barrier in high bootstrap fraction discharges. Gyrokinetic analysis, and quasilinear predictive modeling, demonstrates that the observed transport barrier is caused by the suppression of turbulence primarily from the large Shafranov shift. It is shown that the Shafranov shift can produce a bifurcation to improved confinement in regions of positive magnetic shear or a continuous reduction in transport for weak or negative magnetic shear. Operation at high safety factor lowers the pressure gradient threshold for the Shafranov shift-driven barrier formation. Two self-organized states of the internal and edge transport barrier are observed. It is shown that these two states are controlled by the interaction of the bootstrap current with magnetic shear, and the kinetic ballooning mode instability boundary. Election scale energy transport is predicted to be dominant in the inner 60% of the profile. Evidence is presented that energetic particle-driven instabilities could be playing a role in the thermal energy transport in this region.

Comparison of Sample Size by Bootstrap and by Formulas Based on Normal Distribution Assumption.

PubMed

Wang, Zuozhen

2018-01-01

Bootstrapping technique is distribution-independent, which provides an indirect way to estimate the sample size for a clinical trial based on a relatively smaller sample. In this paper, sample size estimation to compare two parallel-design arms for continuous data by bootstrap procedure are presented for various test types (inequality, non-inferiority, superiority, and equivalence), respectively. Meanwhile, sample size calculation by mathematical formulas (normal distribution assumption) for the identical data are also carried out. Consequently, power difference between the two calculation methods is acceptably small for all the test types. It shows that the bootstrap procedure is a credible technique for sample size estimation. After that, we compared the powers determined using the two methods based on data that violate the normal distribution assumption. To accommodate the feature of the data, the nonparametric statistical method of Wilcoxon test was applied to compare the two groups in the data during the process of bootstrap power estimation. As a result, the power estimated by normal distribution-based formula is far larger than that by bootstrap for each specific sample size per group. Hence, for this type of data, it is preferable that the bootstrap method be applied for sample size calculation at the beginning, and that the same statistical method as used in the subsequent statistical analysis is employed for each bootstrap sample during the course of bootstrap sample size estimation, provided there is historical true data available that can be well representative of the population to which the proposed trial is planning to extrapolate.

Application of the Bootstrap Methods in Factor Analysis.

ERIC Educational Resources Information Center

Ichikawa, Masanori; Konishi, Sadanori

1995-01-01

A Monte Carlo experiment was conducted to investigate the performance of bootstrap methods in normal theory maximum likelihood factor analysis when the distributional assumption was satisfied or unsatisfied. Problems arising with the use of bootstrap methods are highlighted. (SLD)

Determination of Time Dependent Virus Inactivation Rates

NASA Astrophysics Data System (ADS)

Chrysikopoulos, C. V.; Vogler, E. T.

2003-12-01

A methodology is developed for estimating temporally variable virus inactivation rate coefficients from experimental virus inactivation data. The methodology consists of a technique for slope estimation of normalized virus inactivation data in conjunction with a resampling parameter estimation procedure. The slope estimation technique is based on a relatively flexible geostatistical method known as universal kriging. Drift coefficients are obtained by nonlinear fitting of bootstrap samples and the corresponding confidence intervals are obtained by bootstrap percentiles. The proposed methodology yields more accurate time dependent virus inactivation rate coefficients than those estimated by fitting virus inactivation data to a first-order inactivation model. The methodology is successfully applied to a set of poliovirus batch inactivation data. Furthermore, the importance of accurate inactivation rate coefficient determination on virus transport in water saturated porous media is demonstrated with model simulations.

Bootstrap position analysis for forecasting low flow frequency

USGS Publications Warehouse

Tasker, Gary D.; Dunne, P.

1997-01-01

A method of random resampling of residuals from stochastic models is used to generate a large number of 12-month-long traces of natural monthly runoff to be used in a position analysis model for a water-supply storage and delivery system. Position analysis uses the traces to forecast the likelihood of specified outcomes such as reservoir levels falling below a specified level or streamflows falling below statutory passing flows conditioned on the current reservoir levels and streamflows. The advantages of this resampling scheme, called bootstrap position analysis, are that it does not rely on the unverifiable assumption of normality, fewer parameters need to be estimated directly from the data, and accounting for parameter uncertainty is easily done. For a given set of operating rules and water-use requirements for a system, water managers can use such a model as a decision-making tool to evaluate different operating rules. ?? ASCE,.

Bootstrap confidence intervals and bias correction in the estimation of HIV incidence from surveillance data with testing for recent infection.

PubMed

Carnegie, Nicole Bohme

2011-04-15

The incidence of new infections is a key measure of the status of the HIV epidemic, but accurate measurement of incidence is often constrained by limited data. Karon et al. (Statist. Med. 2008; 27:4617–4633) developed a model to estimate the incidence of HIV infection from surveillance data with biologic testing for recent infection for newly diagnosed cases. This method has been implemented by public health departments across the United States and is behind the new national incidence estimates, which are about 40 per cent higher than previous estimates. We show that the delta method approximation given for the variance of the estimator is incomplete, leading to an inflated variance estimate. This contributes to the generation of overly conservative confidence intervals, potentially obscuring important differences between populations. We demonstrate via simulation that an innovative model-based bootstrap method using the specified model for the infection and surveillance process improves confidence interval coverage and adjusts for the bias in the point estimate. Confidence interval coverage is about 94–97 per cent after correction, compared with 96–99 per cent before. The simulated bias in the estimate of incidence ranges from −6.3 to +14.6 per cent under the original model but is consistently under 1 per cent after correction by the model-based bootstrap. In an application to data from King County, Washington in 2007 we observe correction of 7.2 per cent relative bias in the incidence estimate and a 66 per cent reduction in the width of the 95 per cent confidence interval using this method. We provide open-source software to implement the method that can also be extended for alternate models.

Wavelet-based time series bootstrap model for multidecadal streamflow simulation using climate indicators

NASA Astrophysics Data System (ADS)

Erkyihun, Solomon Tassew; Rajagopalan, Balaji; Zagona, Edith; Lall, Upmanu; Nowak, Kenneth

2016-05-01

A model to generate stochastic streamflow projections conditioned on quasi-oscillatory climate indices such as Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO) is presented. Recognizing that each climate index has underlying band-limited components that contribute most of the energy of the signals, we first pursue a wavelet decomposition of the signals to identify and reconstruct these features from annually resolved historical data and proxy based paleoreconstructions of each climate index covering the period from 1650 to 2012. A K-Nearest Neighbor block bootstrap approach is then developed to simulate the total signal of each of these climate index series while preserving its time-frequency structure and marginal distributions. Finally, given the simulated climate signal time series, a K-Nearest Neighbor bootstrap is used to simulate annual streamflow series conditional on the joint state space defined by the simulated climate index for each year. We demonstrate this method by applying it to simulation of streamflow at Lees Ferry gauge on the Colorado River using indices of two large scale climate forcings: Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO), which are known to modulate the Colorado River Basin (CRB) hydrology at multidecadal time scales. Skill in stochastic simulation of multidecadal projections of flow using this approach is demonstrated.

Modality specificity and integration in working memory: Insights from visuospatial bootstrapping.

PubMed

Allen, Richard J; Havelka, Jelena; Falcon, Thomas; Evans, Sally; Darling, Stephen

2015-05-01

The question of how meaningful associations between verbal and spatial information might be utilized to facilitate working memory performance is potentially highly instructive for models of memory function. The present study explored how separable processing capacities within specialized domains might each contribute to this, by examining the disruptive impacts of simple verbal and spatial concurrent tasks on young adults' recall of visually presented digit sequences encountered either in a single location or within a meaningful spatial "keypad" configuration. The previously observed advantage for recall in the latter condition (the "visuospatial bootstrapping effect") consistently emerged across 3 experiments, indicating use of familiar spatial information in boosting verbal memory. The magnitude of this effect interacted with concurrent activity; articulatory suppression during encoding disrupted recall to a greater extent when digits were presented in single locations (Experiment 1), while spatial tapping during encoding had a larger impact on the keypad condition and abolished the visuospatial bootstrapping advantage (Experiment 2). When spatial tapping was performed during recall (Experiment 3), no task by display interaction was observed. Outcomes are discussed within the context of the multicomponent model of working memory, with a particular emphasis on cross-domain storage in the episodic buffer (Baddeley, 2000). (c) 2015 APA, all rights reserved).

Bootstrap-based procedures for inference in nonparametric receiver-operating characteristic curve regression analysis.

PubMed

Rodríguez-Álvarez, María Xosé; Roca-Pardiñas, Javier; Cadarso-Suárez, Carmen; Tahoces, Pablo G

2018-03-01

Prior to using a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is essential. The receiver-operating characteristic curve is the measure of accuracy most widely used for continuous diagnostic tests. However, the possible impact of extra information about the patient (or even the environment) on diagnostic accuracy also needs to be assessed. In this paper, we focus on an estimator for the covariate-specific receiver-operating characteristic curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of generalised additive models for the receiver-operating characteristic curve. The main aim of the paper is to offer new inferential procedures for testing the effect of covariates on the conditional receiver-operating characteristic curve within the above-mentioned class. Specifically, two different bootstrap-based tests are suggested to check (a) the possible effect of continuous covariates on the receiver-operating characteristic curve and (b) the presence of factor-by-curve interaction terms. The validity of the proposed bootstrap-based procedures is supported by simulations. To facilitate the application of these new procedures in practice, an R-package, known as npROCRegression, is provided and briefly described. Finally, data derived from a computer-aided diagnostic system for the automatic detection of tumour masses in breast cancer is analysed.

Bootstrap confidence levels for phylogenetic trees.

PubMed

Efron, B; Halloran, E; Holmes, S

1996-07-09

Evolutionary trees are often estimated from DNA or RNA sequence data. How much confidence should we have in the estimated trees? In 1985, Felsenstein [Felsenstein, J. (1985) Evolution 39, 783-791] suggested the use of the bootstrap to answer this question. Felsenstein's method, which in concept is a straightforward application of the bootstrap, is widely used, but has been criticized as biased in the genetics literature. This paper concerns the use of the bootstrap in the tree problem. We show that Felsenstein's method is not biased, but that it can be corrected to better agree with standard ideas of confidence levels and hypothesis testing. These corrections can be made by using the more elaborate bootstrap method presented here, at the expense of considerably more computation.

Coefficient Alpha Bootstrap Confidence Interval under Nonnormality

ERIC Educational Resources Information Center

Padilla, Miguel A.; Divers, Jasmin; Newton, Matthew

2012-01-01

Three different bootstrap methods for estimating confidence intervals (CIs) for coefficient alpha were investigated. In addition, the bootstrap methods were compared with the most promising coefficient alpha CI estimation methods reported in the literature. The CI methods were assessed through a Monte Carlo simulation utilizing conditions…

Modeling error in experimental assays using the bootstrap principle: Understanding discrepancies between assays using different dispensing technologies

PubMed Central

Hanson, Sonya M.; Ekins, Sean; Chodera, John D.

2015-01-01

All experimental assay data contains error, but the magnitude, type, and primary origin of this error is often not obvious. Here, we describe a simple set of assay modeling techniques based on the bootstrap principle that allow sources of error and bias to be simulated and propagated into assay results. We demonstrate how deceptively simple operations—such as the creation of a dilution series with a robotic liquid handler—can significantly amplify imprecision and even contribute substantially to bias. To illustrate these techniques, we review an example of how the choice of dispensing technology can impact assay measurements, and show how large contributions to discrepancies between assays can be easily understood and potentially corrected for. These simple modeling techniques—illustrated with an accompanying IPython notebook—can allow modelers to understand the expected error and bias in experimental datasets, and even help experimentalists design assays to more effectively reach accuracy and imprecision goals. PMID:26678597

Maturity associated variance in physical activity and health-related quality of life in adolescent females: a mediated effects model.

PubMed

Smart, Joan E Hunter; Cumming, Sean P; Sherar, Lauren B; Standage, Martyn; Neville, Helen; Malina, Robert M

2012-01-01

This study tested a mediated effects model of psychological and behavioral adaptation to puberty within the context of physical activity (PA). Biological maturity status, physical self-concept, PA, and health-related quality of life (HRQoL) were assessed in 222 female British year 7 to 9 pupils (mean age = 12.7 years, SD = .8). Structural equation modeling using maximum likelihood estimation and bootstrapping procedures supported the hypothesized model. Maturation status was inversely related to perceptions of sport competence, body attractiveness, and physical condition; and indirectly and inversely related to physical self-worth, PA, and HRQoL. Examination of the bootstrap-generated bias-corrected confidence intervals representing the direct and indirect paths between suggested that physical self-concept partially mediated the relations between maturity status and PA, and maturity status and HRQoL. Evidence supports the contention that perceptions of the physical self partially mediate relations maturity, PA, and HRQoL in adolescent females.

BootGraph: probabilistic fiber tractography using bootstrap algorithms and graph theory.

PubMed

Vorburger, Robert S; Reischauer, Carolin; Boesiger, Peter

2013-02-01

Bootstrap methods have recently been introduced to diffusion-weighted magnetic resonance imaging to estimate the measurement uncertainty of ensuing diffusion parameters directly from the acquired data without the necessity to assume a noise model. These methods have been previously combined with deterministic streamline tractography algorithms to allow for the assessment of connection probabilities in the human brain. Thereby, the local noise induced disturbance in the diffusion data is accumulated additively due to the incremental progression of streamline tractography algorithms. Graph based approaches have been proposed to overcome this drawback of streamline techniques. For this reason, the bootstrap method is in the present work incorporated into a graph setup to derive a new probabilistic fiber tractography method, called BootGraph. The acquired data set is thereby converted into a weighted, undirected graph by defining a vertex in each voxel and edges between adjacent vertices. By means of the cone of uncertainty, which is derived using the wild bootstrap, a weight is thereafter assigned to each edge. Two path finding algorithms are subsequently applied to derive connection probabilities. While the first algorithm is based on the shortest path approach, the second algorithm takes all existing paths between two vertices into consideration. Tracking results are compared to an established algorithm based on the bootstrap method in combination with streamline fiber tractography and to another graph based algorithm. The BootGraph shows a very good performance in crossing situations with respect to false negatives and permits incorporating additional constraints, such as a curvature threshold. By inheriting the advantages of the bootstrap method and graph theory, the BootGraph method provides a computationally efficient and flexible probabilistic tractography setup to compute connection probability maps and virtual fiber pathways without the drawbacks of streamline tractography algorithms or the assumption of a noise distribution. Moreover, the BootGraph can be applied to common DTI data sets without further modifications and shows a high repeatability. Thus, it is very well suited for longitudinal studies and meta-studies based on DTI. Copyright © 2012 Elsevier Inc. All rights reserved.

Bootstrap Estimates of Standard Errors in Generalizability Theory

ERIC Educational Resources Information Center

Tong, Ye; Brennan, Robert L.

2007-01-01

Estimating standard errors of estimated variance components has long been a challenging task in generalizability theory. Researchers have speculated about the potential applicability of the bootstrap for obtaining such estimates, but they have identified problems (especially bias) in using the bootstrap. Using Brennan's bias-correcting procedures…

Problems with Multivariate Normality: Can the Multivariate Bootstrap Help?

ERIC Educational Resources Information Center

Thompson, Bruce

Multivariate normality is required for some statistical tests. This paper explores the implications of violating the assumption of multivariate normality and illustrates a graphical procedure for evaluating multivariate normality. The logic for using the multivariate bootstrap is presented. The multivariate bootstrap can be used when distribution…

Non-Gaussian spatiotemporal simulation of multisite daily precipitation: downscaling framework

NASA Astrophysics Data System (ADS)

Ben Alaya, M. A.; Ouarda, T. B. M. J.; Chebana, F.

2018-01-01

Probabilistic regression approaches for downscaling daily precipitation are very useful. They provide the whole conditional distribution at each forecast step to better represent the temporal variability. The question addressed in this paper is: how to simulate spatiotemporal characteristics of multisite daily precipitation from probabilistic regression models? Recent publications point out the complexity of multisite properties of daily precipitation and highlight the need for using a non-Gaussian flexible tool. This work proposes a reasonable compromise between simplicity and flexibility avoiding model misspecification. A suitable nonparametric bootstrapping (NB) technique is adopted. A downscaling model which merges a vector generalized linear model (VGLM as a probabilistic regression tool) and the proposed bootstrapping technique is introduced to simulate realistic multisite precipitation series. The model is applied to data sets from the southern part of the province of Quebec, Canada. It is shown that the model is capable of reproducing both at-site properties and the spatial structure of daily precipitations. Results indicate the superiority of the proposed NB technique, over a multivariate autoregressive Gaussian framework (i.e. Gaussian copula).

Explorations in Statistics: the Bootstrap

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2009-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fourth installment of Explorations in Statistics explores the bootstrap. The bootstrap gives us an empirical approach to estimate the theoretical variability among possible values of a sample statistic such as the…

Bootstrapping Confidence Intervals for Robust Measures of Association.

ERIC Educational Resources Information Center

King, Jason E.

A Monte Carlo simulation study was conducted to determine the bootstrap correction formula yielding the most accurate confidence intervals for robust measures of association. Confidence intervals were generated via the percentile, adjusted, BC, and BC(a) bootstrap procedures and applied to the Winsorized, percentage bend, and Pearson correlation…

Quantifying uncertainty on sediment loads using bootstrap confidence intervals

NASA Astrophysics Data System (ADS)

Slaets, Johanna I. F.; Piepho, Hans-Peter; Schmitter, Petra; Hilger, Thomas; Cadisch, Georg

2017-01-01

Load estimates are more informative than constituent concentrations alone, as they allow quantification of on- and off-site impacts of environmental processes concerning pollutants, nutrients and sediment, such as soil fertility loss, reservoir sedimentation and irrigation channel siltation. While statistical models used to predict constituent concentrations have been developed considerably over the last few years, measures of uncertainty on constituent loads are rarely reported. Loads are the product of two predictions, constituent concentration and discharge, integrated over a time period, which does not make it straightforward to produce a standard error or a confidence interval. In this paper, a linear mixed model is used to estimate sediment concentrations. A bootstrap method is then developed that accounts for the uncertainty in the concentration and discharge predictions, allowing temporal correlation in the constituent data, and can be used when data transformations are required. The method was tested for a small watershed in Northwest Vietnam for the period 2010-2011. The results showed that confidence intervals were asymmetric, with the highest uncertainty in the upper limit, and that a load of 6262 Mg year-1 had a 95 % confidence interval of (4331, 12 267) in 2010 and a load of 5543 Mg an interval of (3593, 8975) in 2011. Additionally, the approach demonstrated that direct estimates from the data were biased downwards compared to bootstrap median estimates. These results imply that constituent loads predicted from regression-type water quality models could frequently be underestimating sediment yields and their environmental impact.

Bias-Corrected Estimation of Noncentrality Parameters of Covariance Structure Models

ERIC Educational Resources Information Center

Raykov, Tenko

2005-01-01

A bias-corrected estimator of noncentrality parameters of covariance structure models is discussed. The approach represents an application of the bootstrap methodology for purposes of bias correction, and utilizes the relation between average of resample conventional noncentrality parameter estimates and their sample counterpart. The…

Modelisation de l'historique d'operation de groupes turbine-alternateur

NASA Astrophysics Data System (ADS)

Szczota, Mickael

Because of their ageing fleet, the utility managers are increasingly in needs of tools that can help them to plan efficiently maintenance operations. Hydro-Quebec started a project that aim to foresee the degradation of their hydroelectric runner, and use that information to classify the generating unit. That classification will help to know which generating unit is more at risk to undergo a major failure. Cracks linked to the fatigue phenomenon are a predominant degradation mode and the loading sequences applied to the runner is a parameter impacting the crack growth. So, the aim of this memoir is to create a generator able to generate synthetic loading sequences that are statistically equivalent to the observed history. Those simulated sequences will be used as input in a life assessment model. At first, we describe how the generating units are operated by Hydro-Quebec and analyse the available data, the analysis shows that the data are non-stationnary. Then, we review modelisation and validation methods. In the following chapter a particular attention is given to a precise description of the validation and comparison procedure. Then, we present the comparison of three kind of model : Discrete Time Markov Chains, Discrete Time Semi-Markov Chains and the Moving Block Bootstrap. For the first two models, we describe how to take account for the non-stationnarity. Finally, we show that the Markov Chain is not adapted for our case, and that the Semi-Markov chains are better when they include the non-stationnarity. The final choice between Semi-Markov Chains and the Moving Block Bootstrap depends of the user. But, with a long term vision we recommend the use of Semi-Markov chains for their flexibility. Keywords: Stochastic models, Models validation, Reliability, Semi-Markov Chains, Markov Chains, Bootstrap

HEXT, a software supporting tree-based screens for hybrid taxa in multilocus data sets, and an evaluation of the homoplasy excess test.

PubMed

Schneider, Kevin; Koblmüller, Stephan; Sefc, Kristina M

2015-11-11

The homoplasy excess test (HET) is a tree-based screen for hybrid taxa in multilocus nuclear phylogenies. Homoplasy between a hybrid taxon and the clades containing the parental taxa reduces bootstrap support in the tree. The HET is based on the expectation that excluding the hybrid taxon from the data set increases the bootstrap support for the parental clades, whereas excluding non-hybrid taxa has little effect on statistical node support. To carry out a HET, bootstrap trees are calculated with taxon-jackknife data sets, that is excluding one taxon (species, population) at a time. Excess increase in bootstrap support for certain nodes upon exclusion of a particular taxon indicates the hybrid (the excluded taxon) and its parents (the clades with increased support).We introduce a new software program, hext, which generates the taxon-jackknife data sets, runs the bootstrap tree calculations, and identifies excess bootstrap increases as outlier values in boxplot graphs. hext is written in r language and accepts binary data (0/1; e.g. AFLP) as well as co-dominant SNP and genotype data.We demonstrate the usefulness of hext in large SNP data sets containing putative hybrids and their parents. For instance, using published data of the genus Vitis (~6,000 SNP loci), hext output supports V. × champinii as a hybrid between V. rupestris and V. mustangensis .With simulated SNP and AFLP data sets, excess increases in bootstrap support were not always connected with the hybrid taxon (false positives), whereas the expected bootstrap signal failed to appear on several occasions (false negatives). Potential causes for both types of spurious results are discussed.With both empirical and simulated data sets, the taxon-jackknife output generated by hext provided additional signatures of hybrid taxa, including changes in tree topology across trees, consistent effects of exclusions of the hybrid and the parent taxa, and moderate (rather than excessive) increases in bootstrap support. hext significantly facilitates the taxon-jackknife approach to hybrid taxon detection, even though the simple test for excess bootstrap increase may not reliably identify hybrid taxa in all applications.

A Bootstrap Generalization of Modified Parallel Analysis for IRT Dimensionality Assessment

ERIC Educational Resources Information Center

Finch, Holmes; Monahan, Patrick

2008-01-01

This article introduces a bootstrap generalization to the Modified Parallel Analysis (MPA) method of test dimensionality assessment using factor analysis. This methodology, based on the use of Marginal Maximum Likelihood nonlinear factor analysis, provides for the calculation of a test statistic based on a parametric bootstrap using the MPA…

Quantifying Variations In Multi-parameter Models With The Photon Clean Method (PCM) And Bootstrap Methods

NASA Astrophysics Data System (ADS)

Carpenter, Matthew H.; Jernigan, J. G.

2007-05-01

We present examples of an analysis progression consisting of a synthesis of the Photon Clean Method (Carpenter, Jernigan, Brown, Beiersdorfer 2007) and bootstrap methods to quantify errors and variations in many-parameter models. The Photon Clean Method (PCM) works well for model spaces with large numbers of parameters proportional to the number of photons, therefore a Monte Carlo paradigm is a natural numerical approach. Consequently, PCM, an "inverse Monte-Carlo" method, requires a new approach for quantifying errors as compared to common analysis methods for fitting models of low dimensionality. This presentation will explore the methodology and presentation of analysis results derived from a variety of public data sets, including observations with XMM-Newton, Chandra, and other NASA missions. Special attention is given to the visualization of both data and models including dynamic interactive presentations. This work was performed under the auspices of the Department of Energy under contract No. W-7405-Eng-48. We thank Peter Beiersdorfer and Greg Brown for their support of this technical portion of a larger program related to science with the LLNL EBIT program.

Augmenting Literacy: The Role of Expertise in Digital Writing

ERIC Educational Resources Information Center

Van Ittersum, Derek

2011-01-01

This essay presents a model of reflective use of writing technologies, one that provides a means of more fully exploiting the possibilities of these tools for transforming writing activity. Derived from the work of computer designer Douglas Engelbart, the "bootstrapping" model of reflective use extends current arguments in the field…

Epistemic uncertainty in the location and magnitude of earthquakes in Italy from Macroseismic data

USGS Publications Warehouse

Bakun, W.H.; Gomez, Capera A.; Stucchi, M.

2011-01-01

Three independent techniques (Bakun and Wentworth, 1997; Boxer from Gasperini et al., 1999; and Macroseismic Estimation of Earthquake Parameters [MEEP; see Data and Resources section, deliverable D3] from R.M.W. Musson and M.J. Jimenez) have been proposed for estimating an earthquake location and magnitude from intensity data alone. The locations and magnitudes obtained for a given set of intensity data are almost always different, and no one technique is consistently best at matching instrumental locations and magnitudes of recent well-recorded earthquakes in Italy. Rather than attempting to select one of the three solutions as best, we use all three techniques to estimate the location and the magnitude and the epistemic uncertainties among them. The estimates are calculated using bootstrap resampled data sets with Monte Carlo sampling of a decision tree. The decision-tree branch weights are based on goodness-of-fit measures of location and magnitude for recent earthquakes. The location estimates are based on the spatial distribution of locations calculated from the bootstrap resampled data. The preferred source location is the locus of the maximum bootstrap location spatial density. The location uncertainty is obtained from contours of the bootstrap spatial density: 68% of the bootstrap locations are within the 68% confidence region, and so on. For large earthquakes, our preferred location is not associated with the epicenter but with a location on the extended rupture surface. For small earthquakes, the epicenters are generally consistent with the location uncertainties inferred from the intensity data if an epicenter inaccuracy of 2-3 km is allowed. The preferred magnitude is the median of the distribution of bootstrap magnitudes. As with location uncertainties, the uncertainties in magnitude are obtained from the distribution of bootstrap magnitudes: the bounds of the 68% uncertainty range enclose 68% of the bootstrap magnitudes, and so on. The instrumental magnitudes for large and small earthquakes are generally consistent with the confidence intervals inferred from the distribution of bootstrap resampled magnitudes.

Control of bootstrap current in the pedestal region of tokamaks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shaing, K. C.; Department of Engineering Physics, University of Wisconsin, Madison, Wisconsin 53796; Lai, A. L.

2013-12-15

The high confinement mode (H-mode) plasmas in the pedestal region of tokamaks are characterized by steep gradient of the radial electric field, and sonic poloidal U{sub p,m} flow that consists of poloidal components of the E×B flow and the plasma flow velocity that is parallel to the magnetic field B. Here, E is the electric field. The bootstrap current that is important for the equilibrium, and stability of the pedestal of H-mode plasmas is shown to have an expression different from that in the conventional theory. In the limit where ‖U{sub p,m}‖≫ 1, the bootstrap current is driven by themore » electron temperature gradient and inductive electric field fundamentally different from that in the conventional theory. The bootstrap current in the pedestal region can be controlled through manipulating U{sub p,m} and the gradient of the radial electric. This, in turn, can control plasma stability such as edge-localized modes. Quantitative evaluations of various coefficients are shown to illustrate that the bootstrap current remains finite when ‖U{sub p,m}‖ approaches infinite and to provide indications how to control the bootstrap current. Approximate analytic expressions for viscous coefficients that join results in the banana and plateau-Pfirsch-Schluter regimes are presented to facilitate bootstrap and neoclassical transport simulations in the pedestal region.« less

SOCIAL COMPETENCE AND PSYCHOLOGICAL VULNERABILITY: THE MEDIATING ROLE OF FLOURISHING.

PubMed

Uysal, Recep

2015-10-01

This study examined whether flourishing mediated the social competence and psychological vulnerability. Participants were 259 university students (147 women, 112 men; M age = 21.3 yr., SD = 1.7) who completed the Turkish versions of the Perceived Social Competence Scale, the Flourishing Scale, and the Psychological Vulnerability Scale. Mediation models were tested using the bootstrapping method to examine indirect effects. Consistent with the hypotheses, the results indicated a positive relationship between social competence and flourishing, and a negative relationship between social competence and psychological vulnerability. Results of the bootstrapping method revealed that flourishing significantly mediated the relationship between social competence and psychological vulnerability. The significance and limitations of the results were discussed.

Bootstrapping the O(N) archipelago

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kos, Filip; Poland, David; Simmons-Duffin, David

2015-11-17

We study 3d CFTs with an O(N) global symmetry using the conformal bootstrap for a system of mixed correlators. Specifically, we consider all nonvanishing scalar four-point functions containing the lowest dimension O(N) vector Φ i and the lowest dimension O(N) singlet s, assumed to be the only relevant operators in their symmetry representations. The constraints of crossing symmetry and unitarity for these four-point functions force the scaling dimensions (Δ Φ , Δ s ) to lie inside small islands. Here, we also make rigorous determinations of current two-point functions in the O(2) and O(3) models, with applications to transport inmore » condensed matter systems.« less

Confidence Intervals for the Mean: To Bootstrap or Not to Bootstrap

ERIC Educational Resources Information Center

Calzada, Maria E.; Gardner, Holly

2011-01-01

The results of a simulation conducted by a research team involving undergraduate and high school students indicate that when data is symmetric the student's "t" confidence interval for a mean is superior to the studied non-parametric bootstrap confidence intervals. When data is skewed and for sample sizes n greater than or equal to 10,…

The Beginner's Guide to the Bootstrap Method of Resampling.

ERIC Educational Resources Information Center

Lane, Ginny G.

The bootstrap method of resampling can be useful in estimating the replicability of study results. The bootstrap procedure creates a mock population from a given sample of data from which multiple samples are then drawn. The method extends the usefulness of the jackknife procedure as it allows for computation of a given statistic across a maximal…

Application of a New Resampling Method to SEM: A Comparison of S-SMART with the Bootstrap

ERIC Educational Resources Information Center

Bai, Haiyan; Sivo, Stephen A.; Pan, Wei; Fan, Xitao

2016-01-01

Among the commonly used resampling methods of dealing with small-sample problems, the bootstrap enjoys the widest applications because it often outperforms its counterparts. However, the bootstrap still has limitations when its operations are contemplated. Therefore, the purpose of this study is to examine an alternative, new resampling method…

A Primer on Bootstrap Factor Analysis as Applied to Health Studies Research

ERIC Educational Resources Information Center

Lu, Wenhua; Miao, Jingang; McKyer, E. Lisako J.

2014-01-01

Objectives: To demonstrate how the bootstrap method could be conducted in exploratory factor analysis (EFA) with a syntax written in SPSS. Methods: The data obtained from the Texas Childhood Obesity Prevention Policy Evaluation project (T-COPPE project) were used for illustration. A 5-step procedure to conduct bootstrap factor analysis (BFA) was…

Evaluating the Invariance of Cognitive Profile Patterns Derived from Profile Analysis via Multidimensional Scaling (PAMS): A Bootstrapping Approach

ERIC Educational Resources Information Center

Kim, Se-Kang

2010-01-01

The aim of the current study is to validate the invariance of major profile patterns derived from multidimensional scaling (MDS) by bootstrapping. Profile Analysis via Multidimensional Scaling (PAMS) was employed to obtain profiles and bootstrapping was used to construct the sampling distributions of the profile coordinates and the empirical…

Using i2b2 to Bootstrap Rural Health Analytics and Learning Networks

PubMed Central

Harris, Daniel R.; Baus, Adam D.; Harper, Tamela J.; Jarrett, Traci D.; Pollard, Cecil R.; Talbert, Jeffery C.

2017-01-01

We demonstrate that the open-source i2b2 (Informatics for Integrating Biology and the Bedside) data model can be used to bootstrap rural health analytics and learning networks. These networks promote communication and research initiatives by providing the infrastructure necessary for sharing data and insights across a group of healthcare and research partners. Data integration remains a crucial challenge in connecting rural healthcare sites with a common data sharing and learning network due to the lack of interoperability and standards within electronic health records. The i2b2 data model acts as a point of convergence for disparate data from multiple healthcare sites. A consistent and natural data model for healthcare data is essential for overcoming integration issues, but challenges such as those caused by weak data standardization must still be addressed. We describe our experience in the context of building the West Virginia/Kentucky Health Analytics and Learning Network, a collaborative, multi-state effort connecting rural healthcare sites. PMID:28261006

Using i2b2 to Bootstrap Rural Health Analytics and Learning Networks.

PubMed

Harris, Daniel R; Baus, Adam D; Harper, Tamela J; Jarrett, Traci D; Pollard, Cecil R; Talbert, Jeffery C

2016-08-01

We demonstrate that the open-source i2b2 (Informatics for Integrating Biology and the Bedside) data model can be used to bootstrap rural health analytics and learning networks. These networks promote communication and research initiatives by providing the infrastructure necessary for sharing data and insights across a group of healthcare and research partners. Data integration remains a crucial challenge in connecting rural healthcare sites with a common data sharing and learning network due to the lack of interoperability and standards within electronic health records. The i2b2 data model acts as a point of convergence for disparate data from multiple healthcare sites. A consistent and natural data model for healthcare data is essential for overcoming integration issues, but challenges such as those caused by weak data standardization must still be addressed. We describe our experience in the context of building the West Virginia/Kentucky Health Analytics and Learning Network, a collaborative, multi-state effort connecting rural healthcare sites.

A neurocomputational theory of how explicit learning bootstraps early procedural learning.

PubMed

Paul, Erick J; Ashby, F Gregory

2013-01-01

It is widely accepted that human learning and memory is mediated by multiple memory systems that are each best suited to different requirements and demands. Within the domain of categorization, at least two systems are thought to facilitate learning: an explicit (declarative) system depending largely on the prefrontal cortex, and a procedural (non-declarative) system depending on the basal ganglia. Substantial evidence suggests that each system is optimally suited to learn particular categorization tasks. However, it remains unknown precisely how these systems interact to produce optimal learning and behavior. In order to investigate this issue, the present research evaluated the progression of learning through simulation of categorization tasks using COVIS, a well-known model of human category learning that includes both explicit and procedural learning systems. Specifically, the model's parameter space was thoroughly explored in procedurally learned categorization tasks across a variety of conditions and architectures to identify plausible interaction architectures. The simulation results support the hypothesis that one-way interaction between the systems occurs such that the explicit system "bootstraps" learning early on in the procedural system. Thus, the procedural system initially learns a suboptimal strategy employed by the explicit system and later refines its strategy. This bootstrapping could be from cortical-striatal projections that originate in premotor or motor regions of cortex, or possibly by the explicit system's control of motor responses through basal ganglia-mediated loops.

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

PubMed

Dwivedi, Alok Kumar; Mallawaarachchi, Indika; Alvarado, Luis A

2017-06-30

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. Copyright © 2017 John Wiley & Sons, Ltd.

Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates.

PubMed

LeDell, Erin; Petersen, Maya; van der Laan, Mark

In binary classification problems, the area under the ROC curve (AUC) is commonly used to evaluate the performance of a prediction model. Often, it is combined with cross-validation in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an estimate for cross-validated AUC, we obtain an estimate of its variance. For massive data sets, the process of generating a single performance estimate can be computationally expensive. Additionally, when using a complex prediction method, the process of cross-validating a predictive model on even a relatively small data set can still require a large amount of computation time. Thus, in many practical settings, the bootstrap is a computationally intractable approach to variance estimation. As an alternative to the bootstrap, we demonstrate a computationally efficient influence curve based approach to obtaining a variance estimate for cross-validated AUC.

Forecasting drought risks for a water supply storage system using bootstrap position analysis

USGS Publications Warehouse

Tasker, Gary; Dunne, Paul

1997-01-01

Forecasting the likelihood of drought conditions is an integral part of managing a water supply storage and delivery system. Position analysis uses a large number of possible flow sequences as inputs to a simulation of a water supply storage and delivery system. For a given set of operating rules and water use requirements, water managers can use such a model to forecast the likelihood of specified outcomes such as reservoir levels falling below a specified level or streamflows falling below statutory passing flows a few months ahead conditioned on the current reservoir levels and streamflows. The large number of possible flow sequences are generated using a stochastic streamflow model with a random resampling of innovations. The advantages of this resampling scheme, called bootstrap position analysis, are that it does not rely on the unverifiable assumption of normality and it allows incorporation of long-range weather forecasts into the analysis.

Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates

PubMed Central

Petersen, Maya; van der Laan, Mark

2015-01-01

In binary classification problems, the area under the ROC curve (AUC) is commonly used to evaluate the performance of a prediction model. Often, it is combined with cross-validation in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an estimate for cross-validated AUC, we obtain an estimate of its variance. For massive data sets, the process of generating a single performance estimate can be computationally expensive. Additionally, when using a complex prediction method, the process of cross-validating a predictive model on even a relatively small data set can still require a large amount of computation time. Thus, in many practical settings, the bootstrap is a computationally intractable approach to variance estimation. As an alternative to the bootstrap, we demonstrate a computationally efficient influence curve based approach to obtaining a variance estimate for cross-validated AUC. PMID:26279737

Bootstrapping Security Policies for Wearable Apps Using Attributed Structural Graphs.

PubMed

González-Tablas, Ana I; Tapiador, Juan E

2016-05-11

We address the problem of bootstrapping security and privacy policies for newly-deployed apps in wireless body area networks (WBAN) composed of smartphones, sensors and other wearable devices. We introduce a framework to model such a WBAN as an undirected graph whose vertices correspond to devices, apps and app resources, while edges model structural relationships among them. This graph is then augmented with attributes capturing the features of each entity together with user-defined tags. We then adapt available graph-based similarity metrics to find the closest app to a new one to be deployed, with the aim of reusing, and possibly adapting, its security policy. We illustrate our approach through a detailed smartphone ecosystem case study. Our results suggest that the scheme can provide users with a reasonably good policy that is consistent with the user's security preferences implicitly captured by policies already in place.

Bootstrapping Security Policies for Wearable Apps Using Attributed Structural Graphs

PubMed Central

González-Tablas, Ana I.; Tapiador, Juan E.

2016-01-01

We address the problem of bootstrapping security and privacy policies for newly-deployed apps in wireless body area networks (WBAN) composed of smartphones, sensors and other wearable devices. We introduce a framework to model such a WBAN as an undirected graph whose vertices correspond to devices, apps and app resources, while edges model structural relationships among them. This graph is then augmented with attributes capturing the features of each entity together with user-defined tags. We then adapt available graph-based similarity metrics to find the closest app to a new one to be deployed, with the aim of reusing, and possibly adapting, its security policy. We illustrate our approach through a detailed smartphone ecosystem case study. Our results suggest that the scheme can provide users with a reasonably good policy that is consistent with the user’s security preferences implicitly captured by policies already in place. PMID:27187385

Measurement Error Correction for Predicted Spatiotemporal Air Pollution Exposures.

PubMed

Keller, Joshua P; Chang, Howard H; Strickland, Matthew J; Szpiro, Adam A

2017-05-01

Air pollution cohort studies are frequently analyzed in two stages, first modeling exposure then using predicted exposures to estimate health effects in a second regression model. The difference between predicted and unobserved true exposures introduces a form of measurement error in the second stage health model. Recent methods for spatial data correct for measurement error with a bootstrap and by requiring the study design ensure spatial compatibility, that is, monitor and subject locations are drawn from the same spatial distribution. These methods have not previously been applied to spatiotemporal exposure data. We analyzed the association between fine particulate matter (PM2.5) and birth weight in the US state of Georgia using records with estimated date of conception during 2002-2005 (n = 403,881). We predicted trimester-specific PM2.5 exposure using a complex spatiotemporal exposure model. To improve spatial compatibility, we restricted to mothers residing in counties with a PM2.5 monitor (n = 180,440). We accounted for additional measurement error via a nonparametric bootstrap. Third trimester PM2.5 exposure was associated with lower birth weight in the uncorrected (-2.4 g per 1 μg/m difference in exposure; 95% confidence interval [CI]: -3.9, -0.8) and bootstrap-corrected (-2.5 g, 95% CI: -4.2, -0.8) analyses. Results for the unrestricted analysis were attenuated (-0.66 g, 95% CI: -1.7, 0.35). This study presents a novel application of measurement error correction for spatiotemporal air pollution exposures. Our results demonstrate the importance of spatial compatibility between monitor and subject locations and provide evidence of the association between air pollution exposure and birth weight.

Comparison of Parametric and Nonparametric Bootstrap Methods for Estimating Random Error in Equipercentile Equating

ERIC Educational Resources Information Center

Cui, Zhongmin; Kolen, Michael J.

2008-01-01

This article considers two methods of estimating standard errors of equipercentile equating: the parametric bootstrap method and the nonparametric bootstrap method. Using a simulation study, these two methods are compared under three sample sizes (300, 1,000, and 3,000), for two test content areas (the Iowa Tests of Basic Skills Maps and Diagrams…

Transport Barriers in Bootstrap Driven Tokamaks

NASA Astrophysics Data System (ADS)

Staebler, Gary

2017-10-01

Maximizing the bootstrap current in a tokamak, so that it drives a high fraction of the total current, reduces the external power required to drive current by other means. Improved energy confinement, relative to empirical scaling laws, enables a reactor to more fully take advantage of the bootstrap driven tokamak. Experiments have demonstrated improved energy confinement due to the spontaneous formation of an internal transport barrier in high bootstrap fraction discharges. Gyrokinetic analysis, and quasilinear predictive modeling, demonstrates that the observed transport barrier is due to the suppression of turbulence primarily due to the large Shafranov shift. ExB velocity shear does not play a significant role in the transport barrier due to the high safety factor. It will be shown, that the Shafranov shift can produce a bifurcation to improved confinement in regions of positive magnetic shear or a continuous reduction in transport for weak or negative magnetic shear. Operation at high safety factor lowers the pressure gradient threshold for the Shafranov shift driven barrier formation. The ion energy transport is reduced to neoclassical and electron energy and particle transport is reduced, but still turbulent, within the barrier. Deeper into the plasma, very large levels of electron transport are observed. The observed electron temperature profile is shown to be close to the threshold for the electron temperature gradient (ETG) mode. A large ETG driven energy transport is qualitatively consistent with recent multi-scale gyrokinetic simulations showing that reducing the ion scale turbulence can lead to large increase in the electron scale transport. A new saturation model for the quasilinear TGLF transport code, that fits these multi-scale gyrokinetic simulations, can match the data if the impact of zonal flow mixing on the ETG modes is reduced at high safety factor. This work was supported by the U.S. Department of Energy under DE-FG02-95ER54309 and DE-FC02-04ER54698.

The Role of Simulation Approaches in Statistics

ERIC Educational Resources Information Center

Wood, Michael

2005-01-01

This article explores the uses of a simulation model (the two bucket story)--implemented by a stand-alone computer program, or an Excel workbook (both on the web)--that can be used for deriving bootstrap confidence intervals, and simulating various probability distributions. The strengths of the model are its generality, the fact that it provides…

Reciprocal Effects of Self-Regulation, Semantic Knowledge, and Reading Comprehension in Early Elementary School

ERIC Educational Resources Information Center

Connor, Carol McDonald; Day, Stephanie L.; Phillips, Beth; Sparapani, Nicole; Ingebrand, Sarah W.; McLean, Leigh; Barrus, Angela; Kaschak, Michael P.

2016-01-01

Many assume that cognitive and linguistic processes, such as semantic knowledge (SK) and self-regulation (SR), subserve learned skills like reading. However, complex models of interacting and bootstrapping effects of SK, SR, instruction, and reading hypothesize reciprocal effects. Testing this "lattice" model with children (n = 852)…

Pragmatic Bootstrapping: A Neural Network Model of Vocabulary Acquisition

ERIC Educational Resources Information Center

Caza, Gregory A.; Knott, Alistair

2012-01-01

The social-pragmatic theory of language acquisition proposes that children only become efficient at learning the meanings of words once they acquire the ability to understand the intentions of other agents, in particular the intention to communicate (Akhtar & Tomasello, 2000). In this paper we present a neural network model of word learning which…

Bootstrap evaluation of a young Douglas-fir height growth model for the Pacific Northwest

Treesearch

Nicholas R. Vaughn; Eric C. Turnblom; Martin W. Ritchie

2010-01-01

We evaluated the stability of a complex regression model developed to predict the annual height growth of young Douglas-fir. This model is highly nonlinear and is fit in an iterative manner for annual growth coefficients from data with multiple periodic remeasurement intervals. The traditional methods for such a sensitivity analysis either involve laborious math or...

Small area estimation for semicontinuous data.

PubMed

Chandra, Hukum; Chambers, Ray

2016-03-01

Survey data often contain measurements for variables that are semicontinuous in nature, i.e. they either take a single fixed value (we assume this is zero) or they have a continuous, often skewed, distribution on the positive real line. Standard methods for small area estimation (SAE) based on the use of linear mixed models can be inefficient for such variables. We discuss SAE techniques for semicontinuous variables under a two part random effects model that allows for the presence of excess zeros as well as the skewed nature of the nonzero values of the response variable. In particular, we first model the excess zeros via a generalized linear mixed model fitted to the probability of a nonzero, i.e. strictly positive, value being observed, and then model the response, given that it is strictly positive, using a linear mixed model fitted on the logarithmic scale. Empirical results suggest that the proposed method leads to efficient small area estimates for semicontinuous data of this type. We also propose a parametric bootstrap method to estimate the MSE of the proposed small area estimator. These bootstrap estimates of the MSE are compared to the true MSE in a simulation study. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Methods for Estimating Uncertainty in Factor Analytic Solutions

EPA Science Inventory

The EPA PMF (Environmental Protection Agency positive matrix factorization) version 5.0 and the underlying multilinear engine-executable ME-2 contain three methods for estimating uncertainty in factor analytic models: classical bootstrap (BS), displacement of factor elements (DI...

Comparison of the CEAS and Williams-type barley yield models for North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Leduc, S. (Principal Investigator)

1982-01-01

The CEAS and Williams type models were compared based on specified selection criteria which includes a ten year bootstrap test (1970-1979). Based on this, the models were quite comparable; however, the CEAS model was slightly better overall. The Williams type model seemed better for the 1974 estimates. Because that year spring wheat yield was particularly low, the Williams type model should not be excluded from further consideration.

Multi-baseline bootstrapping at the Navy precision optical interferometer

NASA Astrophysics Data System (ADS)

Armstrong, J. T.; Schmitt, H. R.; Mozurkewich, D.; Jorgensen, A. M.; Muterspaugh, M. W.; Baines, E. K.; Benson, J. A.; Zavala, Robert T.; Hutter, D. J.

2014-07-01

The Navy Precision Optical Interferometer (NPOI) was designed from the beginning to support baseline boot- strapping with equally-spaced array elements. The motivation was the desire to image the surfaces of resolved stars with the maximum resolution possible with a six-element array. Bootstrapping two baselines together to track fringes on a third baseline has been used at the NPOI for many years, but the capabilities of the fringe tracking software did not permit us to bootstrap three or more baselines together. Recently, both a new backend (VISION; Tennessee State Univ.) and new hardware and firmware (AZ Embedded Systems and New Mexico Tech, respectively) for the current hybrid backend have made multi-baseline bootstrapping possible.

Bootstrap and fast wave current drive for tokamak reactors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ehst, D.A.

1991-09-01

Using the multi-species neoclassical treatment of Hirshman and Sigmar we study steady state bootstrap equilibria with seed currents provided by low frequency (ICRF) fast waves and with additional surface current density driven by lower hybrid waves. This study applies to reactor plasmas of arbitrary aspect ratio. IN one limit the bootstrap component can supply nearly the total equilibrium current with minimal driving power (< 20 MW). However, for larger total currents considerable driving power is required (for ITER: I{sub o} = 18 MA needs P{sub FW} = 15 MW, P{sub LH} = 75 MW). A computational survey of bootstrap fractionmore » and current drive efficiency is presented. 11 refs., 8 figs.« less

Semantic Drift in Espresso-style Bootstrapping: Graph-theoretic Analysis and Evaluation in Word Sense Disambiguation

NASA Astrophysics Data System (ADS)

Komachi, Mamoru; Kudo, Taku; Shimbo, Masashi; Matsumoto, Yuji

Bootstrapping has a tendency, called semantic drift, to select instances unrelated to the seed instances as the iteration proceeds. We demonstrate the semantic drift of Espresso-style bootstrapping has the same root as the topic drift of Kleinberg's HITS, using a simplified graph-based reformulation of bootstrapping. We confirm that two graph-based algorithms, the von Neumann kernels and the regularized Laplacian, can reduce the effect of semantic drift in the task of word sense disambiguation (WSD) on Senseval-3 English Lexical Sample Task. Proposed algorithms achieve superior performance to Espresso and previous graph-based WSD methods, even though the proposed algorithms have less parameters and are easy to calibrate.

Tests of Mediation: Paradoxical Decline in Statistical Power as a Function of Mediator Collinearity

PubMed Central

Beasley, T. Mark

2013-01-01

Increasing the correlation between the independent variable and the mediator (a coefficient) increases the effect size (ab) for mediation analysis; however, increasing a by definition increases collinearity in mediation models. As a result, the standard error of product tests increase. The variance inflation due to increases in a at some point outweighs the increase of the effect size (ab) and results in a loss of statistical power. This phenomenon also occurs with nonparametric bootstrapping approaches because the variance of the bootstrap distribution of ab approximates the variance expected from normal theory. Both variances increase dramatically when a exceeds the b coefficient, thus explaining the power decline with increases in a. Implications for statistical analysis and applied researchers are discussed. PMID:24954952

A scientometric prediction of the discovery of the first potentially habitable planet with a mass similar to Earth.

PubMed

Arbesman, Samuel; Laughlin, Gregory

2010-10-04

The search for a habitable extrasolar planet has long interested scientists, but only recently have the tools become available to search for such planets. In the past decades, the number of known extrasolar planets has ballooned into the hundreds, and with it, the expectation that the discovery of the first Earth-like extrasolar planet is not far off. Here, we develop a novel metric of habitability for discovered planets and use this to arrive at a prediction for when the first habitable planet will be discovered. Using a bootstrap analysis of currently discovered exoplanets, we predict the discovery of the first Earth-like planet to be announced in the first half of 2011, with the likeliest date being early May 2011. Our predictions, using only the properties of previously discovered exoplanets, accord well with external estimates for the discovery of the first potentially habitable extrasolar planet and highlight the the usefulness of predictive scientometric techniques to understand the pace of scientific discovery in many fields.

Confidence limit calculation for antidotal potency ratio derived from lethal dose 50

PubMed Central

Manage, Ananda; Petrikovics, Ilona

2013-01-01

AIM: To describe confidence interval calculation for antidotal potency ratios using bootstrap method. METHODS: We can easily adapt the nonparametric bootstrap method which was invented by Efron to construct confidence intervals in such situations like this. The bootstrap method is a resampling method in which the bootstrap samples are obtained by resampling from the original sample. RESULTS: The described confidence interval calculation using bootstrap method does not require the sampling distribution antidotal potency ratio. This can serve as a substantial help for toxicologists, who are directed to employ the Dixon up-and-down method with the application of lower number of animals to determine lethal dose 50 values for characterizing the investigated toxic molecules and eventually for characterizing the antidotal protections by the test antidotal systems. CONCLUSION: The described method can serve as a useful tool in various other applications. Simplicity of the method makes it easier to do the calculation using most of the programming software packages. PMID:25237618

A resampling strategy based on bootstrap to reduce the effect of large blunders in GPS absolute positioning

NASA Astrophysics Data System (ADS)

Angrisano, Antonio; Maratea, Antonio; Gaglione, Salvatore

2018-01-01

In the absence of obstacles, a GPS device is generally able to provide continuous and accurate estimates of position, while in urban scenarios buildings can generate multipath and echo-only phenomena that severely affect the continuity and the accuracy of the provided estimates. Receiver autonomous integrity monitoring (RAIM) techniques are able to reduce the negative consequences of large blunders in urban scenarios, but require both a good redundancy and a low contamination to be effective. In this paper a resampling strategy based on bootstrap is proposed as an alternative to RAIM, in order to estimate accurately position in case of low redundancy and multiple blunders: starting with the pseudorange measurement model, at each epoch the available measurements are bootstrapped—that is random sampled with replacement—and the generated a posteriori empirical distribution is exploited to derive the final position. Compared to standard bootstrap, in this paper the sampling probabilities are not uniform, but vary according to an indicator of the measurement quality. The proposed method has been compared with two different RAIM techniques on a data set collected in critical conditions, resulting in a clear improvement on all considered figures of merit.

Uncertainty Quantification in High Throughput Screening ...

EPA Pesticide Factsheets

Using uncertainty quantification, we aim to improve the quality of modeling data from high throughput screening assays for use in risk assessment. ToxCast is a large-scale screening program that analyzes thousands of chemicals using over 800 assays representing hundreds of biochemical and cellular processes, including endocrine disruption, cytotoxicity, and zebrafish development. Over 2.6 million concentration response curves are fit to models to extract parameters related to potency and efficacy. Models built on ToxCast results are being used to rank and prioritize the toxicological risk of tested chemicals and to predict the toxicity of tens of thousands of chemicals not yet tested in vivo. However, the data size also presents challenges. When fitting the data, the choice of models, model selection strategy, and hit call criteria must reflect the need for computational efficiency and robustness, requiring hard and somewhat arbitrary cutoffs. When coupled with unavoidable noise in the experimental concentration response data, these hard cutoffs cause uncertainty in model parameters and the hit call itself. The uncertainty will then propagate through all of the models built on the data. Left unquantified, this uncertainty makes it difficult to fully interpret the data for risk assessment. We used bootstrap resampling methods to quantify the uncertainty in fitting models to the concentration response data. Bootstrap resampling determines confidence intervals for

Assessing uncertainties in superficial water provision by different bootstrap-based techniques

NASA Astrophysics Data System (ADS)

Rodrigues, Dulce B. B.; Gupta, Hoshin V.; Mendiondo, Eduardo Mario

2014-05-01

An assessment of water security can incorporate several water-related concepts, characterizing the interactions between societal needs, ecosystem functioning, and hydro-climatic conditions. The superficial freshwater provision level depends on the methods chosen for 'Environmental Flow Requirement' estimations, which integrate the sources of uncertainty in the understanding of how water-related threats to aquatic ecosystem security arise. Here, we develop an uncertainty assessment of superficial freshwater provision based on different bootstrap techniques (non-parametric resampling with replacement). To illustrate this approach, we use an agricultural basin (291 km2) within the Cantareira water supply system in Brazil monitored by one daily streamflow gage (24-year period). The original streamflow time series has been randomly resampled for different times or sample sizes (N = 500; ...; 1000), then applied to the conventional bootstrap approach and variations of this method, such as: 'nearest neighbor bootstrap'; and 'moving blocks bootstrap'. We have analyzed the impact of the sampling uncertainty on five Environmental Flow Requirement methods, based on: flow duration curves or probability of exceedance (Q90%, Q75% and Q50%); 7-day 10-year low-flow statistic (Q7,10); and presumptive standard (80% of the natural monthly mean ?ow). The bootstrap technique has been also used to compare those 'Environmental Flow Requirement' (EFR) methods among themselves, considering the difference between the bootstrap estimates and the "true" EFR characteristic, which has been computed averaging the EFR values of the five methods and using the entire streamflow record at monitoring station. This study evaluates the bootstrapping strategies, the representativeness of streamflow series for EFR estimates and their confidence intervals, in addition to overview of the performance differences between the EFR methods. The uncertainties arisen during EFR methods assessment will be propagated through water security indicators referring to water scarcity and vulnerability, seeking to provide meaningful support to end-users and water managers facing the incorporation of uncertainties in the decision making process.

Direct Estimation of Correlation as a Measure of Association Strength Using Multidimensional Item Response Models

ERIC Educational Resources Information Center

Wang, Wen-Chung

2004-01-01

The Pearson correlation is used to depict effect sizes in the context of item response theory. Amultidimensional Rasch model is used to directly estimate the correlation between latent traits. Monte Carlo simulations were conducted to investigate whether the population correlation could be accurately estimated and whether the bootstrap method…

Method-independent, Computationally Frugal Convergence Testing for Sensitivity Analysis Techniques

NASA Astrophysics Data System (ADS)

Mai, J.; Tolson, B.

2017-12-01

The increasing complexity and runtime of environmental models lead to the current situation that the calibration of all model parameters or the estimation of all of their uncertainty is often computationally infeasible. Hence, techniques to determine the sensitivity of model parameters are used to identify most important parameters. All subsequent model calibrations or uncertainty estimation procedures focus then only on these subsets of parameters and are hence less computational demanding. While the examination of the convergence of calibration and uncertainty methods is state-of-the-art, the convergence of the sensitivity methods is usually not checked. If any, bootstrapping of the sensitivity results is used to determine the reliability of the estimated indexes. Bootstrapping, however, might as well become computationally expensive in case of large model outputs and a high number of bootstraps. We, therefore, present a Model Variable Augmentation (MVA) approach to check the convergence of sensitivity indexes without performing any additional model run. This technique is method- and model-independent. It can be applied either during the sensitivity analysis (SA) or afterwards. The latter case enables the checking of already processed sensitivity indexes. To demonstrate the method's independency of the convergence testing method, we applied it to two widely used, global SA methods: the screening method known as Morris method or Elementary Effects (Morris 1991) and the variance-based Sobol' method (Solbol' 1993). The new convergence testing method is first scrutinized using 12 analytical benchmark functions (Cuntz & Mai et al. 2015) where the true indexes of aforementioned three methods are known. This proof of principle shows that the method reliably determines the uncertainty of the SA results when different budgets are used for the SA. The results show that the new frugal method is able to test the convergence and therefore the reliability of SA results in an efficient way. The appealing feature of this new technique is the necessity of no further model evaluation and therefore enables checking of already processed sensitivity results. This is one step towards reliable and transferable, published sensitivity results.

Evaluation of dissolution profile similarity - Comparison between the f2, the multivariate statistical distance and the f2 bootstrapping methods.

PubMed

Paixão, Paulo; Gouveia, Luís F; Silva, Nuno; Morais, José A G

2017-03-01

A simulation study is presented, evaluating the performance of the f 2 , the model-independent multivariate statistical distance and the f 2 bootstrap methods in the ability to conclude similarity between two dissolution profiles. Different dissolution profiles, based on the Noyes-Whitney equation and ranging from theoretical f 2 values between 100 and 40, were simulated. Variability was introduced in the dissolution model parameters in an increasing order, ranging from a situation complying with the European guidelines requirements for the use of the f 2 metric to several situations where the f 2 metric could not be used anymore. Results have shown that the f 2 is an acceptable metric when used according to the regulatory requirements, but loses its applicability when variability increases. The multivariate statistical distance presented contradictory results in several of the simulation scenarios, which makes it an unreliable metric for dissolution profile comparisons. The bootstrap f 2 , although conservative in its conclusions is an alternative suitable method. Overall, as variability increases, all of the discussed methods reveal problems that can only be solved by increasing the number of dosage form units used in the comparison, which is usually not practical or feasible. Additionally, experimental corrective measures may be undertaken in order to reduce the overall variability, particularly when it is shown that it is mainly due to the dissolution assessment instead of being intrinsic to the dosage form. Copyright © 2016. Published by Elsevier B.V.

Lower hybrid current drive in experiments for transport barriers at high βN of JET (Joint European Torus)

NASA Astrophysics Data System (ADS)

Cesario, R. C.; Castaldo, C.; Fonseca, A.; De Angelis, R.; Parail, V.; Smeulders, P.; Beurskens, M.; Brix, M.; Calabrò, G.; De Vries, P.; Mailloux, J.; Pericoli, V.; Ravera, G.; Zagorski, R.

2007-09-01

LHCD has been used in JET experiments aimed at producing internal transport barriers (ITBs) in highly triangular plasmas (δ≈0.4) at high βN (up to 3) for steady-state application. The LHCD is a potentially valuable tool for (i) modifying the target q-profile, which can help avoid deleterious MHD modes and favour the formation of ITBs, and (ii) contributing to the non-inductive current drive required to prolong such plasma regimes. The q-profile evolution has been simulated during the current ramp-up phase for such a discharge (B0 = 2.3 T, IP = 1.5 MA) where 2 MW of LHCD has been coupled. The JETTO code was used taking measured plasma profiles, and the LHCD profile modeled by the LHstar code. The results are in agreement with MSE measurements and indicate the importance of the elevated electron temperature due to LHCD, as well as the driven current. During main heating with 18 MW of NBI and 3 MW of ICRH the bootstrap current density at the edge also becomes large, consistently with the observed reduction of the local turbulence and of the MHD activity. JETTO modelling suggests that the bootstrap current can reduce the magnetic shear (sh) at large radius, potentially affecting the MHD stability and turbulence behaviour in this region. Keywords: lower hybrid current drive (LHCD), bootstrap current, q (safety factor) and shear (sh) profile evolutions.

Performance of Bootstrap MCEWMA: Study case of Sukuk Musyarakah data

NASA Astrophysics Data System (ADS)

Safiih, L. Muhamad; Hila, Z. Nurul

2014-07-01

Sukuk Musyarakah is one of several instruments of Islamic bond investment in Malaysia, where the form of this sukuk is actually based on restructuring the conventional bond to become a Syariah compliant bond. The Syariah compliant is based on prohibition of any influence of usury, benefit or fixed return. Despite of prohibition, daily returns of sukuk are non-fixed return and in statistic, the data of sukuk returns are said to be a time series data which is dependent and autocorrelation distributed. This kind of data is a crucial problem whether in statistical and financing field. Returns of sukuk can be statistically viewed by its volatility, whether it has high volatility that describing the dramatically change of price and categorized it as risky bond or else. However, this crucial problem doesn't get serious attention among researcher compared to conventional bond. In this study, MCEWMA chart in Statistical Process Control (SPC) is mainly used to monitor autocorrelated data and its application on daily returns of securities investment data has gained widespread attention among statistician. However, this chart has always been influence by inaccurate estimation, whether on base model or its limit, due to produce large error and high of probability of signalling out-of-control process for false alarm study. To overcome this problem, a bootstrap approach used in this study, by hybridise it on MCEWMA base model to construct a new chart, i.e. Bootstrap MCEWMA (BMCEWMA) chart. The hybrid model, BMCEWMA, will be applied to daily returns of sukuk Musyarakah for Rantau Abang Capital Bhd. The performance of BMCEWMA base model showed that its more effective compare to real model, MCEWMA based on smaller error estimation, shorter the confidence interval and smaller false alarm. In other word, hybrid chart reduce the variability which shown by smaller error and false alarm. It concludes that the application of BMCEWMA is better than MCEWMA.

Lightweight CoAP-Based Bootstrapping Service for the Internet of Things.

PubMed

Garcia-Carrillo, Dan; Marin-Lopez, Rafael

2016-03-11

The Internet of Things (IoT) is becoming increasingly important in several fields of industrial applications and personal applications, such as medical e-health, smart cities, etc. The research into protocols and security aspects related to this area is continuously advancing in making these networks more reliable and secure, taking into account these aspects by design. Bootstrapping is a procedure by which a user obtains key material and configuration information, among other parameters, to operate as an authenticated party in a security domain. Until now solutions have focused on re-using security protocols that were not developed for IoT constraints. For this reason, in this work we propose a design and implementation of a lightweight bootstrapping service for IoT networks that leverages one of the application protocols used in IoT : Constrained Application Protocol (CoAP). Additionally, in order to provide flexibility, scalability, support for large scale deployment, accountability and identity federation, our design uses technologies such as the Extensible Authentication Protocol (EAP) and Authentication Authorization and Accounting (AAA). We have named this service CoAP-EAP. First, we review the state of the art in the field of bootstrapping and specifically for IoT. Second, we detail the bootstrapping service: the architecture with entities and interfaces and the flow operation. Third, we obtain performance measurements of CoAP-EAP (bootstrapping time, memory footprint, message processing time, message length and energy consumption) and compare them with PANATIKI. The most significant and constrained representative of the bootstrapping solutions related with CoAP-EAP. As we will show, our solution provides significant improvements, mainly due to an important reduction of the message length.

Lightweight CoAP-Based Bootstrapping Service for the Internet of Things

PubMed Central

Garcia-Carrillo, Dan; Marin-Lopez, Rafael

2016-01-01

The Internet of Things (IoT) is becoming increasingly important in several fields of industrial applications and personal applications, such as medical e-health, smart cities, etc. The research into protocols and security aspects related to this area is continuously advancing in making these networks more reliable and secure, taking into account these aspects by design. Bootstrapping is a procedure by which a user obtains key material and configuration information, among other parameters, to operate as an authenticated party in a security domain. Until now solutions have focused on re-using security protocols that were not developed for IoT constraints. For this reason, in this work we propose a design and implementation of a lightweight bootstrapping service for IoT networks that leverages one of the application protocols used in IoT : Constrained Application Protocol (CoAP). Additionally, in order to provide flexibility, scalability, support for large scale deployment, accountability and identity federation, our design uses technologies such as the Extensible Authentication Protocol (EAP) and Authentication Authorization and Accounting (AAA). We have named this service CoAP-EAP. First, we review the state of the art in the field of bootstrapping and specifically for IoT. Second, we detail the bootstrapping service: the architecture with entities and interfaces and the flow operation. Third, we obtain performance measurements of CoAP-EAP (bootstrapping time, memory footprint, message processing time, message length and energy consumption) and compare them with PANATIKI. The most significant and constrained representative of the bootstrapping solutions related with CoAP-EAP. As we will show, our solution provides significant improvements, mainly due to an important reduction of the message length. PMID:26978362

Bootstrapping Least Squares Estimates in Biochemical Reaction Networks

PubMed Central

Linder, Daniel F.

2015-01-01

The paper proposes new computational methods of computing confidence bounds for the least squares estimates (LSEs) of rate constants in mass-action biochemical reaction network and stochastic epidemic models. Such LSEs are obtained by fitting the set of deterministic ordinary differential equations (ODEs), corresponding to the large volume limit of a reaction network, to network’s partially observed trajectory treated as a continuous-time, pure jump Markov process. In the large volume limit the LSEs are asymptotically Gaussian, but their limiting covariance structure is complicated since it is described by a set of nonlinear ODEs which are often ill-conditioned and numerically unstable. The current paper considers two bootstrap Monte-Carlo procedures, based on the diffusion and linear noise approximations for pure jump processes, which allow one to avoid solving the limiting covariance ODEs. The results are illustrated with both in-silico and real data examples from the LINE 1 gene retrotranscription model and compared with those obtained using other methods. PMID:25898769

Percolation in education and application in the 21st century

NASA Astrophysics Data System (ADS)

Adler, Joan; Elfenbaum, Shaked; Sharir, Liran

2017-03-01

Percolation, "so simple you could teach it to your wife" (Chuck Newman, last century) is an ideal system to introduce young students to phase transitions. Two recent projects in the Computational Physics group at the Technion make this easy. One is a set of analog models to be mounted on our walls and enable visitors to switch between samples to see which mixtures of glass and metal objects have a percolating current. The second is a website enabling the creation of stereo samples of two and three dimensional clusters (suited for viewing with Oculus rift) on desktops, tablets and smartphones. Although there have been many physical applications for regular percolation in the past, for Bootstrap Percolation, where only sites with sufficient occupied neighbours remain active, there have not been a surfeit of condensed matter applications. We have found that the creation of diamond membranes for quantum computers can be modeled with a bootstrap process of graphitization in diamond, enabling prediction of optimal processing procedures.

Rumination and depression in Chinese university students: The mediating role of overgeneral autobiographical memory.

PubMed

Kong, Tianzhu; He, Yini; Auerbach, Randy P; McWhinnie, Chad M; Xiao, Jing

2015-04-01

In this study, we examined the mediator effects of overgeneral autobiographical memory (OGM) on the relationship between rumination and depression in 323 Chinese university students. 323 undergraduates completed the questionnaires measuring OGM (Autobiographical Memory Test), rumination (Ruminative Response Scale) and depression (Center for Epidemiologic Studies Depression Scale). Results using structural equation modeling showed that OGM partially-mediated the relationship between rumination and depression (χ 2 = 88.61, p < .01; RMSEA = .051; SRMR = .040; and CFI = .91). Bootstrap methods were used to assess the magnitude of the indirect effects. The results of the bootstrap estimation procedure and subsequent analyses indicated that the indirect effects of OGM on the relationship between rumination and depressive symptoms were significant. The results indicated that rumination and depression were partially mediated by OGM.

Grain Size and Parameter Recovery with TIMSS and the General Diagnostic Model

ERIC Educational Resources Information Center

Skaggs, Gary; Wilkins, Jesse L. M.; Hein, Serge F.

2016-01-01

The purpose of this study was to explore the degree of grain size of the attributes and the sample sizes that can support accurate parameter recovery with the General Diagnostic Model (GDM) for a large-scale international assessment. In this resampling study, bootstrap samples were obtained from the 2003 Grade 8 TIMSS in Mathematics at varying…

Flexible Approaches to Computing Mediated Effects in Generalized Linear Models: Generalized Estimating Equations and Bootstrapping

ERIC Educational Resources Information Center

Schluchter, Mark D.

2008-01-01

In behavioral research, interest is often in examining the degree to which the effect of an independent variable X on an outcome Y is mediated by an intermediary or mediator variable M. This article illustrates how generalized estimating equations (GEE) modeling can be used to estimate the indirect or mediated effect, defined as the amount by…

Standard Errors and Confidence Intervals from Bootstrapping for Ramsay-Curve Item Response Theory Model Item Parameters

ERIC Educational Resources Information Center

Gu, Fei; Skorupski, William P.; Hoyle, Larry; Kingston, Neal M.

2011-01-01

Ramsay-curve item response theory (RC-IRT) is a nonparametric procedure that estimates the latent trait using splines, and no distributional assumption about the latent trait is required. For item parameters of the two-parameter logistic (2-PL), three-parameter logistic (3-PL), and polytomous IRT models, RC-IRT can provide more accurate estimates…

A Bootstrapping Model of Frequency and Context Effects in Word Learning.

PubMed

Kachergis, George; Yu, Chen; Shiffrin, Richard M

2017-04-01

Prior research has shown that people can learn many nouns (i.e., word-object mappings) from a short series of ambiguous situations containing multiple words and objects. For successful cross-situational learning, people must approximately track which words and referents co-occur most frequently. This study investigates the effects of allowing some word-referent pairs to appear more frequently than others, as is true in real-world learning environments. Surprisingly, high-frequency pairs are not always learned better, but can also boost learning of other pairs. Using a recent associative model (Kachergis, Yu, & Shiffrin, 2012), we explain how mixing pairs of different frequencies can bootstrap late learning of the low-frequency pairs based on early learning of higher frequency pairs. We also manipulate contextual diversity, the number of pairs a given pair appears with across training, since it is naturalistically confounded with frequency. The associative model has competing familiarity and uncertainty biases, and their interaction is able to capture the individual and combined effects of frequency and contextual diversity on human learning. Two other recent word-learning models do not account for the behavioral findings. Copyright © 2016 Cognitive Science Society, Inc.

A brief introduction to computer-intensive methods, with a view towards applications in spatial statistics and stereology.

PubMed

Mattfeldt, Torsten

2011-04-01

Computer-intensive methods may be defined as data analytical procedures involving a huge number of highly repetitive computations. We mention resampling methods with replacement (bootstrap methods), resampling methods without replacement (randomization tests) and simulation methods. The resampling methods are based on simple and robust principles and are largely free from distributional assumptions. Bootstrap methods may be used to compute confidence intervals for a scalar model parameter and for summary statistics from replicated planar point patterns, and for significance tests. For some simple models of planar point processes, point patterns can be simulated by elementary Monte Carlo methods. The simulation of models with more complex interaction properties usually requires more advanced computing methods. In this context, we mention simulation of Gibbs processes with Markov chain Monte Carlo methods using the Metropolis-Hastings algorithm. An alternative to simulations on the basis of a parametric model consists of stochastic reconstruction methods. The basic ideas behind the methods are briefly reviewed and illustrated by simple worked examples in order to encourage novices in the field to use computer-intensive methods. © 2010 The Authors Journal of Microscopy © 2010 Royal Microscopical Society.

Method-independent, Computationally Frugal Convergence Testing for Sensitivity Analysis Techniques

NASA Astrophysics Data System (ADS)

Mai, Juliane; Tolson, Bryan

2017-04-01

The increasing complexity and runtime of environmental models lead to the current situation that the calibration of all model parameters or the estimation of all of their uncertainty is often computationally infeasible. Hence, techniques to determine the sensitivity of model parameters are used to identify most important parameters or model processes. All subsequent model calibrations or uncertainty estimation procedures focus then only on these subsets of parameters and are hence less computational demanding. While the examination of the convergence of calibration and uncertainty methods is state-of-the-art, the convergence of the sensitivity methods is usually not checked. If any, bootstrapping of the sensitivity results is used to determine the reliability of the estimated indexes. Bootstrapping, however, might as well become computationally expensive in case of large model outputs and a high number of bootstraps. We, therefore, present a Model Variable Augmentation (MVA) approach to check the convergence of sensitivity indexes without performing any additional model run. This technique is method- and model-independent. It can be applied either during the sensitivity analysis (SA) or afterwards. The latter case enables the checking of already processed sensitivity indexes. To demonstrate the method independency of the convergence testing method, we applied it to three widely used, global SA methods: the screening method known as Morris method or Elementary Effects (Morris 1991, Campolongo et al., 2000), the variance-based Sobol' method (Solbol' 1993, Saltelli et al. 2010) and a derivative-based method known as Parameter Importance index (Goehler et al. 2013). The new convergence testing method is first scrutinized using 12 analytical benchmark functions (Cuntz & Mai et al. 2015) where the true indexes of aforementioned three methods are known. This proof of principle shows that the method reliably determines the uncertainty of the SA results when different budgets are used for the SA. Subsequently, we focus on the model-independency by testing the frugal method using the hydrologic model mHM (www.ufz.de/mhm) with about 50 model parameters. The results show that the new frugal method is able to test the convergence and therefore the reliability of SA results in an efficient way. The appealing feature of this new technique is the necessity of no further model evaluation and therefore enables checking of already processed (and published) sensitivity results. This is one step towards reliable and transferable, published sensitivity results.

Explanation of Two Anomalous Results in Statistical Mediation Analysis.

PubMed

Fritz, Matthew S; Taylor, Aaron B; Mackinnon, David P

2012-01-01

Previous studies of different methods of testing mediation models have consistently found two anomalous results. The first result is elevated Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap tests not found in nonresampling tests or in resampling tests that did not include a bias correction. This is of special concern as the bias-corrected bootstrap is often recommended and used due to its higher statistical power compared with other tests. The second result is statistical power reaching an asymptote far below 1.0 and in some conditions even declining slightly as the size of the relationship between X and M , a , increased. Two computer simulations were conducted to examine these findings in greater detail. Results from the first simulation found that the increased Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap are a function of an interaction between the size of the individual paths making up the mediated effect and the sample size, such that elevated Type I error rates occur when the sample size is small and the effect size of the nonzero path is medium or larger. Results from the second simulation found that stagnation and decreases in statistical power as a function of the effect size of the a path occurred primarily when the path between M and Y , b , was small. Two empirical mediation examples are provided using data from a steroid prevention and health promotion program aimed at high school football players (Athletes Training and Learning to Avoid Steroids; Goldberg et al., 1996), one to illustrate a possible Type I error for the bias-corrected bootstrap test and a second to illustrate a loss in power related to the size of a . Implications of these findings are discussed.

Sample size determination for mediation analysis of longitudinal data.

PubMed

Pan, Haitao; Liu, Suyu; Miao, Danmin; Yuan, Ying

2018-03-27

Sample size planning for longitudinal data is crucial when designing mediation studies because sufficient statistical power is not only required in grant applications and peer-reviewed publications, but is essential to reliable research results. However, sample size determination is not straightforward for mediation analysis of longitudinal design. To facilitate planning the sample size for longitudinal mediation studies with a multilevel mediation model, this article provides the sample size required to achieve 80% power by simulations under various sizes of the mediation effect, within-subject correlations and numbers of repeated measures. The sample size calculation is based on three commonly used mediation tests: Sobel's method, distribution of product method and the bootstrap method. Among the three methods of testing the mediation effects, Sobel's method required the largest sample size to achieve 80% power. Bootstrapping and the distribution of the product method performed similarly and were more powerful than Sobel's method, as reflected by the relatively smaller sample sizes. For all three methods, the sample size required to achieve 80% power depended on the value of the ICC (i.e., within-subject correlation). A larger value of ICC typically required a larger sample size to achieve 80% power. Simulation results also illustrated the advantage of the longitudinal study design. The sample size tables for most encountered scenarios in practice have also been published for convenient use. Extensive simulations study showed that the distribution of the product method and bootstrapping method have superior performance to the Sobel's method, but the product method was recommended to use in practice in terms of less computation time load compared to the bootstrapping method. A R package has been developed for the product method of sample size determination in mediation longitudinal study design.

Uncertainty estimation of Intensity-Duration-Frequency relationships: A regional analysis

NASA Astrophysics Data System (ADS)

Mélèse, Victor; Blanchet, Juliette; Molinié, Gilles

2018-03-01

We propose in this article a regional study of uncertainties in IDF curves derived from point-rainfall maxima. We develop two generalized extreme value models based on the simple scaling assumption, first in the frequentist framework and second in the Bayesian framework. Within the frequentist framework, uncertainties are obtained i) from the Gaussian density stemming from the asymptotic normality theorem of the maximum likelihood and ii) with a bootstrap procedure. Within the Bayesian framework, uncertainties are obtained from the posterior densities. We confront these two frameworks on the same database covering a large region of 100, 000 km2 in southern France with contrasted rainfall regime, in order to be able to draw conclusion that are not specific to the data. The two frameworks are applied to 405 hourly stations with data back to the 1980's, accumulated in the range 3 h-120 h. We show that i) the Bayesian framework is more robust than the frequentist one to the starting point of the estimation procedure, ii) the posterior and the bootstrap densities are able to better adjust uncertainty estimation to the data than the Gaussian density, and iii) the bootstrap density give unreasonable confidence intervals, in particular for return levels associated to large return period. Therefore our recommendation goes towards the use of the Bayesian framework to compute uncertainty.

A Pilot Investigation of the Relationship between Climate Variability and Milk Compounds under the Bootstrap Technique

PubMed Central

Marami Milani, Mohammad Reza; Hense, Andreas; Rahmani, Elham; Ploeger, Angelika

2015-01-01

This study analyzes the linear relationship between climate variables and milk components in Iran by applying bootstrapping to include and assess the uncertainty. The climate parameters, Temperature Humidity Index (THI) and Equivalent Temperature Index (ETI) are computed from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis (2002–2010). Milk data for fat, protein (measured on fresh matter bases), and milk yield are taken from 936,227 milk records for the same period, using cows fed by natural pasture from April to September. Confidence intervals for the regression model are calculated using the bootstrap technique. This method is applied to the original times series, generating statistically equivalent surrogate samples. As a result, despite the short time data and the related uncertainties, an interesting behavior of the relationships between milk compound and the climate parameters is visible. During spring only, a weak dependency of milk yield and climate variations is obvious, while fat and protein concentrations show reasonable correlations. In summer, milk yield shows a similar level of relationship with ETI, but not with temperature and THI. We suggest this methodology for studies in the field of the impacts of climate change and agriculture, also environment and food with short-term data. PMID:28231215

Impurities in a non-axisymmetric plasma. Transport and effect on bootstrap current

DOE PAGES

Mollén, A.; Landreman, M.; Smith, H. M.; ...

2015-11-20

Impurities cause radiation losses and plasma dilution, and in stellarator plasmas the neoclassical ambipolar radial electric field is often unfavorable for avoiding strong impurity peaking. In this work we use a new continuum drift-kinetic solver, the SFINCS code (the Stellarator Fokker-Planck Iterative Neoclassical Conservative Solver) [M. Landreman et al., Phys. Plasmas 21 (2014) 042503] which employs the full linearized Fokker-Planck-Landau operator, to calculate neoclassical impurity transport coefficients for a Wendelstein 7-X (W7-X) magnetic configuration. We compare SFINCS calculations with theoretical asymptotes in the high collisionality limit. We observe and explain a 1/nu-scaling of the inter-species radial transport coefficient at lowmore » collisionality, arising due to the field term in the inter-species collision operator, and which is not found with simplified collision models even when momentum correction is applied. However, this type of scaling disappears if a radial electric field is present. We use SFINCS to analyze how the impurity content affects the neoclassical impurity dynamics and the bootstrap current. We show that a change in plasma effective charge Z eff of order unity can affect the bootstrap current enough to cause a deviation in the divertor strike point locations.« less

Seasonal comparisons of sea ice concentration estimates derived from SSM/I, OKEAN, and RADARSAT data

USGS Publications Warehouse

Belchansky, Gennady I.; Douglas, David C.

2002-01-01

The Special Sensor Microwave Imager (SSM/I) microwave satellite radiometer and its predecessor SMMR are primary sources of information for global sea ice and climate studies. However, comparisons of SSM/I, Landsat, AVHRR, and ERS-1 synthetic aperture radar (SAR) have shown substantial seasonal and regional differences in their estimates of sea ice concentration. To evaluate these differences, we compared SSM/I estimates of sea ice coverage derived with the NASA Team and Bootstrap algorithms to estimates made using RADARSAT, and OKEAN-01 satellite sensor data. The study area included the Barents Sea, Kara Sea, Laptev Sea, and adjacent parts of the Arctic Ocean, during October 1995 through October 1999. Ice concentration estimates from spatially and temporally near-coincident imagery were calculated using independent algorithms for each sensor type. The OKEAN algorithm implemented the satellite's two-channel active (radar) and passive microwave data in a linear mixture model based on the measured values of brightness temperature and radar backscatter. The RADARSAT algorithm utilized a segmentation approach of the measured radar backscatter, and the SSM/I ice concentrations were derived at National Snow and Ice Data Center (NSIDC) using the NASA Team and Bootstrap algorithms. Seasonal and monthly differences between SSM/I, OKEAN, and RADARSAT ice concentrations were calculated and compared. Overall, total sea ice concentration estimates derived independently from near-coincident RADARSAT, OKEAN-01, and SSM/I satellite imagery demonstrated mean differences of less than 5.5% (S.D.<9.5%) during the winter period. Differences between the SSM/I NASA Team and the SSM/I Bootstrap concentrations were no more than 3.1% (S.D.<5.4%) during this period. RADARSAT and OKEAN-01 data both yielded higher total ice concentrations than the NASA Team and the Bootstrap algorithms. The Bootstrap algorithm yielded higher total ice concentrations than the NASA Team algorithm. Total ice concentrations derived from OKEAN-01 and SSM/I satellite imagery were highly correlated during winter, spring, and fall, with mean differences of less than 8.1% (S.D.<15%) for the NASA Team algorithm, and less than 2.8% (S.D.<13.8%) for the Bootstrap algorithm. Respective differences between SSM/I NASA Team and SSM/I Bootstrap total concentrations were less than 5.3% (S.D.<6.9%). Monthly mean differences between SSM/I and OKEAN differed annually by less than 6%, with smaller differences primarily in winter. The NASA Team and Bootstrap algorithms underestimated the total sea ice concentrations relative to the RADARSAT ScanSAR no more than 3.0% (S.D.<9%) and 1.2% (S.D.<7.5%) during cold months, and no more than 12% and 7% during summer, respectively. ScanSAR tended to estimate higher ice concentrations for ice concentrations greater than 50%, when compared to SSM/I during all months. ScanSAR underestimated total sea ice concentration by 2% compared to the OKEAN-01 algorithm during cold months, and gave an overestimation by 2% during spring and summer months. Total NASA Team and Bootstrap sea ice concentration estimates derived from coincident SSM/I and OKEAN-01 data demonstrated mean differences of no more than 5.3% (S.D.<7%), 3.1% (S.D.<5.5%), 2.0% (S.D.<5.5%), and 7.3% (S.D.<10%) for fall, winter, spring, and summer periods, respectively. Large disagreements were observed between the OKEAN and NASA Team results in spring and summer for estimates of the first-year (FY) and multiyear (MY) age classes. The OKEAN-01 algorithm and data tended to estimate, on average, lower concentrations of young or FY ice and higher concentrations of total and MY ice for all months and seasons. Our results contribute to the growing body of documentation about the levels of disparity obtained when seasonal sea ice concentrations are estimated using various types of satellite data and algorithms.

Bootstrap and Counter-Bootstrap approaches for formation of the cortege of Informative indicators by Results of Measurements

NASA Astrophysics Data System (ADS)

Artemenko, M. V.; Chernetskaia, I. E.; Kalugina, N. M.; Shchekina, E. N.

2018-04-01

This article describes the solution of the actual problem of the productive formation of a cortege of informative measured features of the object of observation and / or control using author's algorithms for the use of bootstraps and counter-bootstraps technologies for processing the results of measurements of various states of the object on the basis of different volumes of the training sample. The work that is presented in this paper considers aggregation by specific indicators of informative capacity by linear, majority, logical and “greedy” methods, applied both individually and integrally. The results of the computational experiment are discussed, and in conclusion is drawn that the application of the proposed methods contributes to an increase in the efficiency of classification of the states of the object from the results of measurements.

How Many Subjects are Needed for a Visual Field Normative Database? A Comparison of Ground Truth and Bootstrapped Statistics.

PubMed

Phu, Jack; Bui, Bang V; Kalloniatis, Michael; Khuu, Sieu K

2018-03-01

The number of subjects needed to establish the normative limits for visual field (VF) testing is not known. Using bootstrap resampling, we determined whether the ground truth mean, distribution limits, and standard deviation (SD) could be approximated using different set size ( x ) levels, in order to provide guidance for the number of healthy subjects required to obtain robust VF normative data. We analyzed the 500 Humphrey Field Analyzer (HFA) SITA-Standard results of 116 healthy subjects and 100 HFA full threshold results of 100 psychophysically experienced healthy subjects. These VFs were resampled (bootstrapped) to determine mean sensitivity, distribution limits (5th and 95th percentiles), and SD for different ' x ' and numbers of resamples. We also used the VF results of 122 glaucoma patients to determine the performance of ground truth and bootstrapped results in identifying and quantifying VF defects. An x of 150 (for SITA-Standard) and 60 (for full threshold) produced bootstrapped descriptive statistics that were no longer different to the original distribution limits and SD. Removing outliers produced similar results. Differences between original and bootstrapped limits in detecting glaucomatous defects were minimized at x = 250. Ground truth statistics of VF sensitivities could be approximated using set sizes that are significantly smaller than the original cohort. Outlier removal facilitates the use of Gaussian statistics and does not significantly affect the distribution limits. We provide guidance for choosing the cohort size for different levels of error when performing normative comparisons with glaucoma patients.

Inference With Difference-in-Differences With a Small Number of Groups: A Review, Simulation Study, and Empirical Application Using SHARE Data.

PubMed

Rokicki, Slawa; Cohen, Jessica; Fink, Günther; Salomon, Joshua A; Landrum, Mary Beth

2018-01-01

Difference-in-differences (DID) estimation has become increasingly popular as an approach to evaluate the effect of a group-level policy on individual-level outcomes. Several statistical methodologies have been proposed to correct for the within-group correlation of model errors resulting from the clustering of data. Little is known about how well these corrections perform with the often small number of groups observed in health research using longitudinal data. First, we review the most commonly used modeling solutions in DID estimation for panel data, including generalized estimating equations (GEE), permutation tests, clustered standard errors (CSE), wild cluster bootstrapping, and aggregation. Second, we compare the empirical coverage rates and power of these methods using a Monte Carlo simulation study in scenarios in which we vary the degree of error correlation, the group size balance, and the proportion of treated groups. Third, we provide an empirical example using the Survey of Health, Ageing, and Retirement in Europe. When the number of groups is small, CSE are systematically biased downwards in scenarios when data are unbalanced or when there is a low proportion of treated groups. This can result in over-rejection of the null even when data are composed of up to 50 groups. Aggregation, permutation tests, bias-adjusted GEE, and wild cluster bootstrap produce coverage rates close to the nominal rate for almost all scenarios, though GEE may suffer from low power. In DID estimation with a small number of groups, analysis using aggregation, permutation tests, wild cluster bootstrap, or bias-adjusted GEE is recommended.

Prediction of resource volumes at untested locations using simple local prediction models

USGS Publications Warehouse

Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

2006-01-01

This paper shows how local spatial nonparametric prediction models can be applied to estimate volumes of recoverable gas resources at individual undrilled sites, at multiple sites on a regional scale, and to compute confidence bounds for regional volumes based on the distribution of those estimates. An approach that combines cross-validation, the jackknife, and bootstrap procedures is used to accomplish this task. Simulation experiments show that cross-validation can be applied beneficially to select an appropriate prediction model. The cross-validation procedure worked well for a wide range of different states of nature and levels of information. Jackknife procedures are used to compute individual prediction estimation errors at undrilled locations. The jackknife replicates also are used with a bootstrap resampling procedure to compute confidence bounds for the total volume. The method was applied to data (partitioned into a training set and target set) from the Devonian Antrim Shale continuous-type gas play in the Michigan Basin in Otsego County, Michigan. The analysis showed that the model estimate of total recoverable volumes at prediction sites is within 4 percent of the total observed volume. The model predictions also provide frequency distributions of the cell volumes at the production unit scale. Such distributions are the basis for subsequent economic analyses. ?? Springer Science+Business Media, LLC 2007.

C-17 Centerlining - Analysis of Paratrooper Trajectory

DTIC Science & Technology

2005-06-01

Linear Regression Models. 4th ed. McGraw Hill, 2004. Kuntavanish, Mark. Program Engineer for C-17 System Program Office. Briefing to US Army...Airdrop Risk Assessment Using Bootstrap Sampling, MS AFIT Thesis AFIT/GOR/ENS/96D-01, Dec 1996. Kutner, Michael, C. Nachtsheim, and J. Neter. Applied

Modality Specificity and Integration in Working Memory: Insights from Visuospatial Bootstrapping

ERIC Educational Resources Information Center

Allen, Richard J.; Havelka, Jelena; Falcon, Thomas; Evans, Sally; Darling, Stephen

2015-01-01

The question of how meaningful associations between verbal and spatial information might be utilized to facilitate working memory performance is potentially highly instructive for models of memory function. The present study explored how separable processing capacities within specialized domains might each contribute to this, by examining the…

Variance Estimation Using Replication Methods in Structural Equation Modeling with Complex Sample Data

ERIC Educational Resources Information Center

Stapleton, Laura M.

2008-01-01

This article discusses replication sampling variance estimation techniques that are often applied in analyses using data from complex sampling designs: jackknife repeated replication, balanced repeated replication, and bootstrapping. These techniques are used with traditional analyses such as regression, but are currently not used with structural…

Struggling To Understand Abstract Science Topics: A Roundhouse Diagram-Based Study.

ERIC Educational Resources Information Center

Ward, Robin E.; Wandersee, James H.

2002-01-01

Explores the effects of Roundhouse diagram construction on a previously low-performing middle school science student's struggles to understand abstract science concepts and principles. Based on a metacognition-based visual learning model, aims to elucidate the process by which Roundhouse diagramming helps learners bootstrap their current…

Using the Bootstrap Method to Evaluate the Critical Range of Misfit for Polytomous Rasch Fit Statistics.

PubMed

Seol, Hyunsoo

2016-06-01

The purpose of this study was to apply the bootstrap procedure to evaluate how the bootstrapped confidence intervals (CIs) for polytomous Rasch fit statistics might differ according to sample sizes and test lengths in comparison with the rule-of-thumb critical value of misfit. A total of 25 simulated data sets were generated to fit the Rasch measurement and then a total of 1,000 replications were conducted to compute the bootstrapped CIs under each of 25 testing conditions. The results showed that rule-of-thumb critical values for assessing the magnitude of misfit were not applicable because the infit and outfit mean square error statistics showed different magnitudes of variability over testing conditions and the standardized fit statistics did not exactly follow the standard normal distribution. Further, they also do not share the same critical range for the item and person misfit. Based on the results of the study, the bootstrapped CIs can be used to identify misfitting items or persons as they offer a reasonable alternative solution, especially when the distributions of the infit and outfit statistics are not well known and depend on sample size. © The Author(s) 2016.

The Novaya Zemlya Event of 31 December 1992 and Seismic Identification Issues: Annual Seismic Research Symposium (15th) Held in Vail, Colorado on 8-10 September 1993

DTIC Science & Technology

1993-09-10

1993). A bootstrap generalizedlikelihood ratio test in discriminant analysis, Proc. 15th Annual Seismic Research Symposium, in press. I Hedlin, M., J... ratio indicate that the event does not belong to the first class. The bootstrap technique is used here as well to set the critical value of the test ...Methodist University. Baek, J., H. L. Gray, W. A. Woodward and M.D. Fisk (1993). A Bootstrap Generalized Likelihood Ratio Test in Discriminant

Rumination and depression in Chinese university students: The mediating role of overgeneral autobiographical memory

PubMed Central

Kong, Tianzhu; He, Yini; Auerbach, Randy P.; McWhinnie, Chad M.; Xiao, Jing

2015-01-01

Objective In this study, we examined the mediator effects of overgeneral autobiographical memory (OGM) on the relationship between rumination and depression in 323 Chinese university students. Method 323 undergraduates completed the questionnaires measuring OGM (Autobiographical Memory Test), rumination (Ruminative Response Scale) and depression (Center for Epidemiologic Studies Depression Scale). Results Results using structural equation modeling showed that OGM partially-mediated the relationship between rumination and depression (χ2 = 88.61, p < .01; RMSEA = .051; SRMR = .040; and CFI = .91). Bootstrap methods were used to assess the magnitude of the indirect effects. The results of the bootstrap estimation procedure and subsequent analyses indicated that the indirect effects of OGM on the relationship between rumination and depressive symptoms were significant. Conclusion The results indicated that rumination and depression were partially mediated by OGM. PMID:25977594

Combining Nordtest method and bootstrap resampling for measurement uncertainty estimation of hematology analytes in a medical laboratory.

PubMed

Cui, Ming; Xu, Lili; Wang, Huimin; Ju, Shaoqing; Xu, Shuizhu; Jing, Rongrong

2017-12-01

Measurement uncertainty (MU) is a metrological concept, which can be used for objectively estimating the quality of test results in medical laboratories. The Nordtest guide recommends an approach that uses both internal quality control (IQC) and external quality assessment (EQA) data to evaluate the MU. Bootstrap resampling is employed to simulate the unknown distribution based on the mathematical statistics method using an existing small sample of data, where the aim is to transform the small sample into a large sample. However, there have been no reports of the utilization of this method in medical laboratories. Thus, this study applied the Nordtest guide approach based on bootstrap resampling for estimating the MU. We estimated the MU for the white blood cell (WBC) count, red blood cell (RBC) count, hemoglobin (Hb), and platelets (Plt). First, we used 6months of IQC data and 12months of EQA data to calculate the MU according to the Nordtest method. Second, we combined the Nordtest method and bootstrap resampling with the quality control data and calculated the MU using MATLAB software. We then compared the MU results obtained using the two approaches. The expanded uncertainty results determined for WBC, RBC, Hb, and Plt using the bootstrap resampling method were 4.39%, 2.43%, 3.04%, and 5.92%, respectively, and 4.38%, 2.42%, 3.02%, and 6.00% with the existing quality control data (U [k=2]). For WBC, RBC, Hb, and Plt, the differences between the results obtained using the two methods were lower than 1.33%. The expanded uncertainty values were all less than the target uncertainties. The bootstrap resampling method allows the statistical analysis of the MU. Combining the Nordtest method and bootstrap resampling is considered a suitable alternative method for estimating the MU. Copyright © 2017 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Exploring the Replicability of a Study's Results: Bootstrap Statistics for the Multivariate Case.

ERIC Educational Resources Information Center

Thompson, Bruce

1995-01-01

Use of the bootstrap method in a canonical correlation analysis to evaluate the replicability of a study's results is illustrated. More confidence may be vested in research results that replicate. (SLD)

The Role of GRAIL Orbit Determination in Preprocessing of Gravity Science Measurements

NASA Technical Reports Server (NTRS)

Kruizinga, Gerhard; Asmar, Sami; Fahnestock, Eugene; Harvey, Nate; Kahan, Daniel; Konopliv, Alex; Oudrhiri, Kamal; Paik, Meegyeong; Park, Ryan; Strekalov, Dmitry;

Towards a bootstrap approach to higher orders of epsilon expansion

NASA Astrophysics Data System (ADS)

Dey, Parijat; Kaviraj, Apratim

2018-02-01

We employ a hybrid approach in determining the anomalous dimension and OPE coefficient of higher spin operators in the Wilson-Fisher theory. First we do a large spin analysis for CFT data where we use results obtained from the usual and the Mellin bootstrap and also from Feynman diagram literature. This gives new predictions at O( ɛ 4) and O( ɛ 5) for anomalous dimensions and OPE coefficients, and also provides a cross-check for the results from Mellin bootstrap. These higher orders get contributions from all higher spin operators in the crossed channel. We also use the bootstrap in Mellin space method for ϕ 3 in d = 6 - ɛ CFT where we calculate general higher spin OPE data. We demonstrate a higher loop order calculation in this approach by summing over contributions from higher spin operators of the crossed channel in the same spirit as before.

A Scientometric Prediction of the Discovery of the First Potentially Habitable Planet with a Mass Similar to Earth

PubMed Central

Arbesman, Samuel; Laughlin, Gregory

2010-01-01

Background The search for a habitable extrasolar planet has long interested scientists, but only recently have the tools become available to search for such planets. In the past decades, the number of known extrasolar planets has ballooned into the hundreds, and with it, the expectation that the discovery of the first Earth-like extrasolar planet is not far off. Methodology/Principal Findings Here, we develop a novel metric of habitability for discovered planets and use this to arrive at a prediction for when the first habitable planet will be discovered. Using a bootstrap analysis of currently discovered exoplanets, we predict the discovery of the first Earth-like planet to be announced in the first half of 2011, with the likeliest date being early May 2011. Conclusions/Significance Our predictions, using only the properties of previously discovered exoplanets, accord well with external estimates for the discovery of the first potentially habitable extrasolar planet and highlight the the usefulness of predictive scientometric techniques to understand the pace of scientific discovery in many fields. PMID:20957226

RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models.

PubMed

Stamatakis, Alexandros

2006-11-01

RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets > or =4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25,057 (1463 bp) and 2182 (51,089 bp) taxa, respectively. icwww.epfl.ch/~stamatak

Estimating crustal thickness using SsPmp in regions covered by low-velocity sediments: Imaging the Moho beneath the Southeastern Suture of the Appalachian Margin Experiment (SESAME) array, SE Atlantic Coastal Plain

NASA Astrophysics Data System (ADS)

Parker, E. Horry, Jr.; Hawman, Robert B.; Fischer, Karen M.; Wagner, Lara S.

2016-09-01

Deconvolved waveforms for two earthquakes (Mw: 6.0 and 5.8) show clear postcritical SsPmp arrivals for broadband stations deployed across the coastal plain of Georgia, allowing mapping of crustal thickness in spite of strong reverberations generated by low-velocity sediments. Precritical SsPmp arrivals are also identified. For a basement in which velocity increases linearly with depth, a bootstrapped grid search suggests an average basement velocity of 6.5 ± 0.1 km/s and basement thickness of 29.8 ± 2.0 km. Corresponding normal-incidence Moho two-way times (including sediments) are 10.6 ± 0.6 s, consistent with times for events interpreted as Moho reflections on coincident active-source reflection profiles. Modeling of an underplated mafic layer (Vp = 7.2-7.4 km/s) using travel time constraints from SsPmp data and vertical-incidence Moho reflection times yields a total basement thickness of 30-35 km and average basement velocity of 6.35-6.65 km/s for an underplate thickness of 0-15 km.

Assessing Uncertainties in Surface Water Security: A Probabilistic Multi-model Resampling approach

NASA Astrophysics Data System (ADS)

Rodrigues, D. B. B.

2015-12-01

Various uncertainties are involved in the representation of processes that characterize interactions between societal needs, ecosystem functioning, and hydrological conditions. Here, we develop an empirical uncertainty assessment of water security indicators that characterize scarcity and vulnerability, based on a multi-model and resampling framework. We consider several uncertainty sources including those related to: i) observed streamflow data; ii) hydrological model structure; iii) residual analysis; iv) the definition of Environmental Flow Requirement method; v) the definition of critical conditions for water provision; and vi) the critical demand imposed by human activities. We estimate the overall uncertainty coming from the hydrological model by means of a residual bootstrap resampling approach, and by uncertainty propagation through different methodological arrangements applied to a 291 km² agricultural basin within the Cantareira water supply system in Brazil. Together, the two-component hydrograph residual analysis and the block bootstrap resampling approach result in a more accurate and precise estimate of the uncertainty (95% confidence intervals) in the simulated time series. We then compare the uncertainty estimates associated with water security indicators using a multi-model framework and provided by each model uncertainty estimation approach. The method is general and can be easily extended forming the basis for meaningful support to end-users facing water resource challenges by enabling them to incorporate a viable uncertainty analysis into a robust decision making process.

[Population pharmacokinetics applied to optimising cisplatin doses in cancer patients].

PubMed

Ramón-López, A; Escudero-Ortiz, V; Carbonell, V; Pérez-Ruixo, J J; Valenzuela, B

2012-01-01

To develop and internally validate a population pharmacokinetics model for cisplatin and assess its prediction capacity for personalising doses in cancer patients. Cisplatin plasma concentrations in forty-six cancer patients were used to determine the pharmacokinetic parameters of a two-compartment pharmacokinetic model implemented in NONMEN VI software. Pharmacokinetic parameter identification capacity was assessed using the parametric bootstrap method and the model was validated using the nonparametric bootstrap method and standardised visual and numerical predictive checks. The final model's prediction capacity was evaluated in terms of accuracy and precision during the first (a priori) and second (a posteriori) chemotherapy cycles. Mean population cisplatin clearance is 1.03 L/h with an interpatient variability of 78.0%. Estimated distribution volume at steady state was 48.3 L, with inter- and intrapatient variabilities of 31,3% and 11,7%, respectively. Internal validation confirmed that the population pharmacokinetics model is appropriate to describe changes over time in cisplatin plasma concentrations, as well as its variability in the study population. The accuracy and precision of a posteriori prediction of cisplatin concentrations improved by 21% and 54% compared to a priori prediction. The population pharmacokinetic model developed adequately described the changes in cisplatin plasma concentrations in cancer patients and can be used to optimise cisplatin dosing regimes accurately and precisely. Copyright © 2011 SEFH. Published by Elsevier Espana. All rights reserved.

Estimation of reliability of predictions and model applicability domain evaluation in the analysis of acute toxicity (LD50).

PubMed

Sazonovas, A; Japertas, P; Didziapetris, R

2010-01-01

This study presents a new type of acute toxicity (LD(50)) prediction that enables automated assessment of the reliability of predictions (which is synonymous with the assessment of the Model Applicability Domain as defined by the Organization for Economic Cooperation and Development). Analysis involved nearly 75,000 compounds from six animal systems (acute rat toxicity after oral and intraperitoneal administration; acute mouse toxicity after oral, intraperitoneal, intravenous, and subcutaneous administration). Fragmental Partial Least Squares (PLS) with 100 bootstraps yielded baseline predictions that were automatically corrected for non-linear effects in local chemical spaces--a combination called Global, Adjusted Locally According to Similarity (GALAS) modelling methodology. Each prediction obtained in this manner is provided with a reliability index value that depends on both compound's similarity to the training set (that accounts for similar trends in LD(50) variations within multiple bootstraps) and consistency of experimental results with regard to the baseline model in the local chemical environment. The actual performance of the Reliability Index (RI) was proven by its good (and uniform) correlations with Root Mean Square Error (RMSE) in all validation sets, thus providing quantitative assessment of the Model Applicability Domain. The obtained models can be used for compound screening in the early stages of drug development and prioritization for experimental in vitro testing or later in vivo animal acute toxicity studies.

Nomogram Prediction of Overall Survival After Curative Irradiation for Uterine Cervical Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seo, YoungSeok; Yoo, Seong Yul; Kim, Mi-Sook

Purpose: The purpose of this study was to develop a nomogram capable of predicting the probability of 5-year survival after radical radiotherapy (RT) without chemotherapy for uterine cervical cancer. Methods and Materials: We retrospectively analyzed 549 patients that underwent radical RT for uterine cervical cancer between March 1994 and April 2002 at our institution. Multivariate analysis using Cox proportional hazards regression was performed and this Cox model was used as the basis for the devised nomogram. The model was internally validated for discrimination and calibration by bootstrap resampling. Results: By multivariate regression analysis, the model showed that age, hemoglobin levelmore » before RT, Federation Internationale de Gynecologie Obstetrique (FIGO) stage, maximal tumor diameter, lymph node status, and RT dose at Point A significantly predicted overall survival. The survival prediction model demonstrated good calibration and discrimination. The bootstrap-corrected concordance index was 0.67. The predictive ability of the nomogram proved to be superior to FIGO stage (p = 0.01). Conclusions: The devised nomogram offers a significantly better level of discrimination than the FIGO staging system. In particular, it improves predictions of survival probability and could be useful for counseling patients, choosing treatment modalities and schedules, and designing clinical trials. However, before this nomogram is used clinically, it should be externally validated.« less

BOOTSTRAPPING THE CORONAL MAGNETIC FIELD WITH STEREO: UNIPOLAR POTENTIAL FIELD MODELING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aschwanden, Markus J.; Sandman, Anne W., E-mail: aschwanden@lmsal.co

We investigate the recently quantified misalignment of {alpha}{sub mis} {approx} 20{sup 0}-40{sup 0} between the three-dimensional geometry of stereoscopically triangulated coronal loops observed with STEREO/EUVI (in four active regions (ARs)) and theoretical (potential or nonlinear force-free) magnetic field models extrapolated from photospheric magnetograms. We develop an efficient method of bootstrapping the coronal magnetic field by forward fitting a parameterized potential field model to the STEREO-observed loops. The potential field model consists of a number of unipolar magnetic charges that are parameterized by decomposing a photospheric magnetogram from the Michelson Doppler Imager. The forward-fitting method yields a best-fit magnetic field modelmore » with a reduced misalignment of {alpha}{sub PF} {approx} 13{sup 0}-20{sup 0}. We also evaluate stereoscopic measurement errors and find a contribution of {alpha}{sub SE} {approx} 7{sup 0}-12{sup 0}, which constrains the residual misalignment to {alpha}{sub NP} {approx} 11{sup 0}-17{sup 0}, which is likely due to the nonpotentiality of the ARs. The residual misalignment angle, {alpha}{sub NP}, of the potential field due to nonpotentiality is found to correlate with the soft X-ray flux of the AR, which implies a relationship between electric currents and plasma heating.« less

Performance of DIMTEST-and NOHARM-Based Statistics for Testing Unidimensionality

ERIC Educational Resources Information Center

Finch, Holmes; Habing, Brian

2007-01-01

This Monte Carlo study compares the ability of the parametric bootstrap version of DIMTEST with three goodness-of-fit tests calculated from a fitted NOHARM model to detect violations of the assumption of unidimensionality in testing data. The effectiveness of the procedures was evaluated for different numbers of items, numbers of examinees,…

Bootstrapping Cox’s Regression Model.

DTIC Science & Technology

1985-11-01

crucial points a multivariate martingale central limit theorem. Involved in this is a p x p covariance matrix Z with elements T j2= f {2(s8 ) - s(l)( s ,8o...1980). The statistical analaysis of failure time data. Wiley, New York. Meyer, P.-A. (1971). Square integrable martingales, a survey. Lecture Notes

Explanation of Two Anomalous Results in Statistical Mediation Analysis

ERIC Educational Resources Information Center

Fritz, Matthew S.; Taylor, Aaron B.; MacKinnon, David P.

2012-01-01

Previous studies of different methods of testing mediation models have consistently found two anomalous results. The first result is elevated Type I error rates for the bias-corrected and accelerated bias-corrected bootstrap tests not found in nonresampling tests or in resampling tests that did not include a bias correction. This is of special…

A Bootstrapping Model of Frequency and Context Effects in Word Learning

ERIC Educational Resources Information Center

Kachergis, George; Yu, Chen; Shiffrin, Richard M.

2017-01-01

Prior research has shown that people can learn many nouns (i.e., word--object mappings) from a short series of ambiguous situations containing multiple words and objects. For successful cross-situational learning, people must approximately track which words and referents co-occur most frequently. This study investigates the effects of allowing…

Bootstrapping in a Language of Thought: A Formal Model of Numerical Concept Learning

ERIC Educational Resources Information Center

Piantadosi, Steven T.; Tenenbaum, Joshua B.; Goodman, Noah D.

2012-01-01

In acquiring number words, children exhibit a qualitative leap in which they transition from understanding a few number words, to possessing a rich system of interrelated numerical concepts. We present a computational framework for understanding this inductive leap as the consequence of statistical inference over a sufficiently powerful…

Understanding a Normal Distribution of Data (Part 2).

PubMed

Maltenfort, Mitchell

2016-02-01

Completing the discussion of data normality, advanced techniques for analysis of non-normal data are discussed including data transformation, Generalized Linear Modeling, and bootstrapping. Relative strengths and weaknesses of each technique are helpful in choosing a strategy, but help from a statistician is usually necessary to analyze non-normal data using these methods.

Simulating realistic predator signatures in quantitative fatty acid signature analysis

USGS Publications Warehouse

Bromaghin, Jeffrey F.

2015-01-01

Diet estimation is an important field within quantitative ecology, providing critical insights into many aspects of ecology and community dynamics. Quantitative fatty acid signature analysis (QFASA) is a prominent method of diet estimation, particularly for marine mammal and bird species. Investigators using QFASA commonly use computer simulation to evaluate statistical characteristics of diet estimators for the populations they study. Similar computer simulations have been used to explore and compare the performance of different variations of the original QFASA diet estimator. In both cases, computer simulations involve bootstrap sampling prey signature data to construct pseudo-predator signatures with known properties. However, bootstrap sample sizes have been selected arbitrarily and pseudo-predator signatures therefore may not have realistic properties. I develop an algorithm to objectively establish bootstrap sample sizes that generates pseudo-predator signatures with realistic properties, thereby enhancing the utility of computer simulation for assessing QFASA estimator performance. The algorithm also appears to be computationally efficient, resulting in bootstrap sample sizes that are smaller than those commonly used. I illustrate the algorithm with an example using data from Chukchi Sea polar bears (Ursus maritimus) and their marine mammal prey. The concepts underlying the approach may have value in other areas of quantitative ecology in which bootstrap samples are post-processed prior to their use.

Bootstrap calculation of ultimate strength temperature maxima for neutron irradiated ferritic/martensitic steels

NASA Astrophysics Data System (ADS)

Obraztsov, S. M.; Konobeev, Yu. V.; Birzhevoy, G. A.; Rachkov, V. I.

2006-12-01

The dependence of mechanical properties of ferritic/martensitic (F/M) steels on irradiation temperature is of interest because these steels are used as structural materials for fast, fusion reactors and accelerator driven systems. Experimental data demonstrating temperature peaks in physical and mechanical properties of neutron irradiated pure iron, nickel, vanadium, and austenitic stainless steels are available in the literature. A lack of such an information for F/M steels forces one to apply a computational mathematical-statistical modeling methods. The bootstrap procedure is one of such methods that allows us to obtain the necessary statistical characteristics using only a sample of limited size. In the present work this procedure is used for modeling the frequency distribution histograms of ultimate strength temperature peaks in pure iron and Russian F/M steels EP-450 and EP-823. Results of fitting the sums of Lorentz or Gauss functions to the calculated distributions are presented. It is concluded that there are two temperature (at 360 and 390 °C) peaks of the ultimate strength in EP-450 steel and single peak at 390 °C in EP-823.

Impacts of Stress, Self-Efficacy, and Optimism on Suicide Ideation among Rehabilitation Patients with Acute Pesticide Poisoning

PubMed Central

Feng, Jun; Li, Shusheng; Chen, Huawen

2015-01-01

Background The high incidence of pesticide ingestion as a means to commit suicide is a critical public health problem. An important predictor of suicidal behavior is suicide ideation, which is related to stress. However, studies on how to defend against stress-induced suicidal thoughts are limited. Objective This study explores the impact of stress on suicidal ideation by investigating the mediating effect of self-efficacy and dispositional optimism. Methods Direct and indirect (via self-efficacy and dispositional optimism) effects of stress on suicidal ideation were investigated among 296 patients with acute pesticide poisoning from four general hospitals. For this purpose, structural equation modeling (SEM) and bootstrap method were used. Results Results obtained using SEM and bootstrap method show that stress has a direct effect on suicide ideation. Furthermore, self-efficacy and dispositional optimism partially weakened the relationship between stress and suicidal ideation. Conclusion The final model shows a significant relationship between stress and suicidal ideation through self-efficacy or dispositional optimism. The findings extended prior studies and provide enlightenment on how self-efficacy and optimism prevents stress-induced suicidal thoughts. PMID:25679994

Impacts of stress, self-efficacy, and optimism on suicide ideation among rehabilitation patients with acute pesticide poisoning.

PubMed

Feng, Jun; Li, Shusheng; Chen, Huawen

2015-01-01

The high incidence of pesticide ingestion as a means to commit suicide is a critical public health problem. An important predictor of suicidal behavior is suicide ideation, which is related to stress. However, studies on how to defend against stress-induced suicidal thoughts are limited. This study explores the impact of stress on suicidal ideation by investigating the mediating effect of self-efficacy and dispositional optimism. Direct and indirect (via self-efficacy and dispositional optimism) effects of stress on suicidal ideation were investigated among 296 patients with acute pesticide poisoning from four general hospitals. For this purpose, structural equation modeling (SEM) and bootstrap method were used. Results obtained using SEM and bootstrap method show that stress has a direct effect on suicide ideation. Furthermore, self-efficacy and dispositional optimism partially weakened the relationship between stress and suicidal ideation. The final model shows a significant relationship between stress and suicidal ideation through self-efficacy or dispositional optimism. The findings extended prior studies and provide enlightenment on how self-efficacy and optimism prevents stress-induced suicidal thoughts.

Exact Mass-Coupling Relation for the Homogeneous Sine-Gordon Model.

PubMed

Bajnok, Zoltán; Balog, János; Ito, Katsushi; Satoh, Yuji; Tóth, Gábor Zsolt

2016-05-06

We derive the exact mass-coupling relation of the simplest multiscale quantum integrable model, i.e., the homogeneous sine-Gordon model with two mass scales. The relation is obtained by comparing the perturbed conformal field theory description of the model valid at short distances to the large distance bootstrap description based on the model's integrability. In particular, we find a differential equation for the relation by constructing conserved tensor currents, which satisfy a generalization of the Θ sum rule Ward identity. The mass-coupling relation is written in terms of hypergeometric functions.

Understanding catastrophizing from a misdirected problem-solving perspective.

PubMed

Flink, Ida K; Boersma, Katja; MacDonald, Shane; Linton, Steven J

2012-05-01

The aim is to explore pain catastrophizing from a problem-solving perspective. The links between catastrophizing, problem framing, and problem-solving behaviour are examined through two possible models of mediation as inferred by two contemporary and complementary theoretical models, the misdirected problem solving model (Eccleston & Crombez, 2007) and the fear-anxiety-avoidance model (Asmundson, Norton, & Vlaeyen, 2004). In this prospective study, a general population sample (n= 173) with perceived problems with spinal pain filled out questionnaires twice; catastrophizing and problem framing were assessed on the first occasion and health care seeking (as a proxy for medically oriented problem solving) was assessed 7 months later. Two different approaches were used to explore whether the data supported any of the proposed models of mediation. First, multiple regressions were used according to traditional recommendations for mediation analyses. Second, a bootstrapping method (n= 1000 bootstrap resamples) was used to explore the significance of the indirect effects in both possible models of mediation. The results verified the concepts included in the misdirected problem solving model. However, the direction of the relations was more in line with the fear-anxiety-avoidance model. More specifically, the mediation analyses provided support for viewing catastrophizing as a mediator of the relation between biomedical problem framing and medically oriented problem-solving behaviour. These findings provide support for viewing catastrophizing from a problem-solving perspective and imply a need to examine and address problem framing and catastrophizing in back pain patients. ©2011 The British Psychological Society.

Assessing Mediational Models: Testing and Interval Estimation for Indirect Effects.

PubMed

Biesanz, Jeremy C; Falk, Carl F; Savalei, Victoria

2010-08-06

Theoretical models specifying indirect or mediated effects are common in the social sciences. An indirect effect exists when an independent variable's influence on the dependent variable is mediated through an intervening variable. Classic approaches to assessing such mediational hypotheses ( Baron & Kenny, 1986 ; Sobel, 1982 ) have in recent years been supplemented by computationally intensive methods such as bootstrapping, the distribution of the product methods, and hierarchical Bayesian Markov chain Monte Carlo (MCMC) methods. These different approaches for assessing mediation are illustrated using data from Dunn, Biesanz, Human, and Finn (2007). However, little is known about how these methods perform relative to each other, particularly in more challenging situations, such as with data that are incomplete and/or nonnormal. This article presents an extensive Monte Carlo simulation evaluating a host of approaches for assessing mediation. We examine Type I error rates, power, and coverage. We study normal and nonnormal data as well as complete and incomplete data. In addition, we adapt a method, recently proposed in statistical literature, that does not rely on confidence intervals (CIs) to test the null hypothesis of no indirect effect. The results suggest that the new inferential method-the partial posterior p value-slightly outperforms existing ones in terms of maintaining Type I error rates while maximizing power, especially with incomplete data. Among confidence interval approaches, the bias-corrected accelerated (BC a ) bootstrapping approach often has inflated Type I error rates and inconsistent coverage and is not recommended; In contrast, the bootstrapped percentile confidence interval and the hierarchical Bayesian MCMC method perform best overall, maintaining Type I error rates, exhibiting reasonable power, and producing stable and accurate coverage rates.

Signal detection theory and vestibular perception: III. Estimating unbiased fit parameters for psychometric functions.

PubMed

Chaudhuri, Shomesh E; Merfeld, Daniel M

2013-03-01

Psychophysics generally relies on estimating a subject's ability to perform a specific task as a function of an observed stimulus. For threshold studies, the fitted functions are called psychometric functions. While fitting psychometric functions to data acquired using adaptive sampling procedures (e.g., "staircase" procedures), investigators have encountered a bias in the spread ("slope" or "threshold") parameter that has been attributed to the serial dependency of the adaptive data. Using simulations, we confirm this bias for cumulative Gaussian parametric maximum likelihood fits on data collected via adaptive sampling procedures, and then present a bias-reduced maximum likelihood fit that substantially reduces the bias without reducing the precision of the spread parameter estimate and without reducing the accuracy or precision of the other fit parameters. As a separate topic, we explain how to implement this bias reduction technique using generalized linear model fits as well as other numeric maximum likelihood techniques such as the Nelder-Mead simplex. We then provide a comparison of the iterative bootstrap and observed information matrix techniques for estimating parameter fit variance from adaptive sampling procedure data sets. The iterative bootstrap technique is shown to be slightly more accurate; however, the observed information technique executes in a small fraction (0.005 %) of the time required by the iterative bootstrap technique, which is an advantage when a real-time estimate of parameter fit variance is required.

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data.

PubMed

Abram, Samantha V; Helwig, Nathaniel E; Moodie, Craig A; DeYoung, Colin G; MacDonald, Angus W; Waller, Niels G

2016-01-01

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks.

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data

PubMed Central

Abram, Samantha V.; Helwig, Nathaniel E.; Moodie, Craig A.; DeYoung, Colin G.; MacDonald, Angus W.; Waller, Niels G.

2016-01-01

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks. PMID:27516732

Bootstrapping of Life through Holonomy and Self-modification

NASA Astrophysics Data System (ADS)

Kazansky, Alexander B.

2010-11-01

Life on the Earth demonstrate not only adaptive, cognitive, particularly, anticipatory properties, but also active, transformative function to its local and global environment. As V. Vernadsky stated, life is a powerful geological force. Charles Darwin realized that too. In his last work [1] he proved, that earthworms through their vital activity in geological time scale are able to form and support contemporary structure of soil on the whole planet. Locally, through so-called process of niche construction [2] organisms virtually modifies abiotic and biotic factors of natural selection and thereby insert feedback loop in evolutionary process. Stigmergy [3] is one more form of indirect interaction of organisms via the environment by signs, left in local environment or just by performing working activity in swarms, leading to self-organization and coordination of actions in the process of refuges construction. In organization of life we can separate active, rigid, organism-like, autopoietic-like systems or less rigid, sympoietic, socio-biological type systems [4]. Nevertheless, all forms of life systems demonstrate so-called bootstrapping, or spontaneous process of self-organizing emergence. This process is feasible thanks to self-modification, and holonomy in their organization, or total reflexivity. Analysis of the role of indirect interactions in bootstrapping, made in this paper, is aimed at revealing relationships between concepts and making step to forming new systemic model of organization and evolution of special dual pair, biota and biosphere.

Multiple-instance ensemble learning for hyperspectral images

NASA Astrophysics Data System (ADS)

Ergul, Ugur; Bilgin, Gokhan

2017-10-01

An ensemble framework for multiple-instance (MI) learning (MIL) is introduced for use in hyperspectral images (HSIs) by inspiring the bagging (bootstrap aggregation) method in ensemble learning. Ensemble-based bagging is performed by a small percentage of training samples, and MI bags are formed by a local windowing process with variable window sizes on selected instances. In addition to bootstrap aggregation, random subspace is another method used to diversify base classifiers. The proposed method is implemented using four MIL classification algorithms. The classifier model learning phase is carried out with MI bags, and the estimation phase is performed over single-test instances. In the experimental part of the study, two different HSIs that have ground-truth information are used, and comparative results are demonstrated with state-of-the-art classification methods. In general, the MI ensemble approach produces more compact results in terms of both diversity and error compared to equipollent non-MIL algorithms.

Power in Bayesian Mediation Analysis for Small Sample Research

PubMed Central

Miočević, Milica; MacKinnon, David P.; Levy, Roy

2018-01-01

It was suggested that Bayesian methods have potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This paper compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence intervals at N≤ 200. Bayesian methods with diffuse priors have power comparable to the distribution of the product and bootstrap methods, and Bayesian methods with informative priors had the most power. Varying degrees of precision of prior distributions were also examined. Increased precision led to greater power only when N≥ 100 and the effects were small, N < 60 and the effects were large, and N < 200 and the effects were medium. An empirical example from psychology illustrated a Bayesian analysis of the single mediator model from prior selection to interpreting results. PMID:29662296

Power in Bayesian Mediation Analysis for Small Sample Research.

PubMed

Miočević, Milica; MacKinnon, David P; Levy, Roy

2017-01-01

It was suggested that Bayesian methods have potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This paper compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product, percentile, and bias-corrected bootstrap confidence intervals at N≤ 200. Bayesian methods with diffuse priors have power comparable to the distribution of the product and bootstrap methods, and Bayesian methods with informative priors had the most power. Varying degrees of precision of prior distributions were also examined. Increased precision led to greater power only when N≥ 100 and the effects were small, N < 60 and the effects were large, and N < 200 and the effects were medium. An empirical example from psychology illustrated a Bayesian analysis of the single mediator model from prior selection to interpreting results.

Bootstrap Methods: A Very Leisurely Look.

ERIC Educational Resources Information Center

Hinkle, Dennis E.; Winstead, Wayland H.

The Bootstrap method, a computer-intensive statistical method of estimation, is illustrated using a simple and efficient Statistical Analysis System (SAS) routine. The utility of the method for generating unknown parameters, including standard errors for simple statistics, regression coefficients, discriminant function coefficients, and factor…

Bootstrapping Student Understanding of What Is Going on in Econometrics.

ERIC Educational Resources Information Center

Kennedy, Peter E.

2001-01-01

Explains that econometrics is an intellectual game played by rules based on the sampling distribution concept. Contains explanations for why many students are uncomfortable with econometrics. Encourages instructors to use explain-how-to-bootstrap exercises to promote student understanding. (RLH)

Using the bootstrap to establish statistical significance for relative validity comparisons among patient-reported outcome measures

PubMed Central

2013-01-01

Background Relative validity (RV), a ratio of ANOVA F-statistics, is often used to compare the validity of patient-reported outcome (PRO) measures. We used the bootstrap to establish the statistical significance of the RV and to identify key factors affecting its significance. Methods Based on responses from 453 chronic kidney disease (CKD) patients to 16 CKD-specific and generic PRO measures, RVs were computed to determine how well each measure discriminated across clinically-defined groups of patients compared to the most discriminating (reference) measure. Statistical significance of RV was quantified by the 95% bootstrap confidence interval. Simulations examined the effects of sample size, denominator F-statistic, correlation between comparator and reference measures, and number of bootstrap replicates. Results The statistical significance of the RV increased as the magnitude of denominator F-statistic increased or as the correlation between comparator and reference measures increased. A denominator F-statistic of 57 conveyed sufficient power (80%) to detect an RV of 0.6 for two measures correlated at r = 0.7. Larger denominator F-statistics or higher correlations provided greater power. Larger sample size with a fixed denominator F-statistic or more bootstrap replicates (beyond 500) had minimal impact. Conclusions The bootstrap is valuable for establishing the statistical significance of RV estimates. A reasonably large denominator F-statistic (F > 57) is required for adequate power when using the RV to compare the validity of measures with small or moderate correlations (r < 0.7). Substantially greater power can be achieved when comparing measures of a very high correlation (r > 0.9). PMID:23721463

Predicting the risk for colorectal cancer with personal characteristics and fecal immunochemical test.

PubMed

Li, Wen; Zhao, Li-Zhong; Ma, Dong-Wang; Wang, De-Zheng; Shi, Lei; Wang, Hong-Lei; Dong, Mo; Zhang, Shu-Yi; Cao, Lei; Zhang, Wei-Hua; Zhang, Xi-Peng; Zhang, Qing-Huai; Yu, Lin; Qin, Hai; Wang, Xi-Mo; Chen, Sam Li-Sheng

2018-05-01

We aimed to predict colorectal cancer (CRC) based on the demographic features and clinical correlates of personal symptoms and signs from Tianjin community-based CRC screening data.A total of 891,199 residents who were aged 60 to 74 and were screened in 2012 were enrolled. The Lasso logistic regression model was used to identify the predictors for CRC. Predictive validity was assessed by the receiver operating characteristic (ROC) curve. Bootstrapping method was also performed to validate this prediction model.CRC was best predicted by a model that included age, sex, education level, occupations, diarrhea, constipation, colon mucosa and bleeding, gallbladder disease, a stressful life event, family history of CRC, and a positive fecal immunochemical test (FIT). The area under curve (AUC) for the questionnaire with a FIT was 84% (95% CI: 82%-86%), followed by 76% (95% CI: 74%-79%) for a FIT alone, and 73% (95% CI: 71%-76%) for the questionnaire alone. With 500 bootstrap replications, the estimated optimism (<0.005) shows good discrimination in validation of prediction model.A risk prediction model for CRC based on a series of symptoms and signs related to enteric diseases in combination with a FIT was developed from first round of screening. The results of the current study are useful for increasing the awareness of high-risk subjects and for individual-risk-guided invitations or strategies to achieve mass screening for CRC.

Nonparametric Regression and the Parametric Bootstrap for Local Dependence Assessment.

ERIC Educational Resources Information Center

Habing, Brian

2001-01-01

Discusses ideas underlying nonparametric regression and the parametric bootstrap with an overview of their application to item response theory and the assessment of local dependence. Illustrates the use of the method in assessing local dependence that varies with examinee trait levels. (SLD)

Application of the Bootstrap Statistical Method in Deriving Vibroacoustic Specifications

NASA Technical Reports Server (NTRS)

Hughes, William O.; Paez, Thomas L.

2006-01-01

This paper discusses the Bootstrap Method for specification of vibroacoustic test specifications. Vibroacoustic test specifications are necessary to properly accept or qualify a spacecraft and its components for the expected acoustic, random vibration and shock environments seen on an expendable launch vehicle. Traditionally, NASA and the U.S. Air Force have employed methods of Normal Tolerance Limits to derive these test levels based upon the amount of data available, and the probability and confidence levels desired. The Normal Tolerance Limit method contains inherent assumptions about the distribution of the data. The Bootstrap is a distribution-free statistical subsampling method which uses the measured data themselves to establish estimates of statistical measures of random sources. This is achieved through the computation of large numbers of Bootstrap replicates of a data measure of interest and the use of these replicates to derive test levels consistent with the probability and confidence desired. The comparison of the results of these two methods is illustrated via an example utilizing actual spacecraft vibroacoustic data.

The Reliability and Stability of an Inferred Phylogenetic Tree from Empirical Data.

PubMed

Katsura, Yukako; Stanley, Craig E; Kumar, Sudhir; Nei, Masatoshi

2017-03-01

The reliability of a phylogenetic tree obtained from empirical data is usually measured by the bootstrap probability (Pb) of interior branches of the tree. If the bootstrap probability is high for most branches, the tree is considered to be reliable. If some interior branches show relatively low bootstrap probabilities, we are not sure that the inferred tree is really reliable. Here, we propose another quantity measuring the reliability of the tree called the stability of a subtree. This quantity refers to the probability of obtaining a subtree (Ps) of an inferred tree obtained. We then show that if the tree is to be reliable, both Pb and Ps must be high. We also show that Ps is given by a bootstrap probability of the subtree with the closest outgroup sequence, and computer program RESTA for computing the Pb and Ps values will be presented. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

A revisit to contingency table and tests of independence: bootstrap is preferred to Chi-square approximations as well as Fisher's exact test.

PubMed

Lin, Jyh-Jiuan; Chang, Ching-Hui; Pal, Nabendu

2015-01-01

To test the mutual independence of two qualitative variables (or attributes), it is a common practice to follow the Chi-square tests (Pearson's as well as likelihood ratio test) based on data in the form of a contingency table. However, it should be noted that these popular Chi-square tests are asymptotic in nature and are useful when the cell frequencies are "not too small." In this article, we explore the accuracy of the Chi-square tests through an extensive simulation study and then propose their bootstrap versions that appear to work better than the asymptotic Chi-square tests. The bootstrap tests are useful even for small-cell frequencies as they maintain the nominal level quite accurately. Also, the proposed bootstrap tests are more convenient than the Fisher's exact test which is often criticized for being too conservative. Finally, all test methods are applied to a few real-life datasets for demonstration purposes.

A primer for biomedical scientists on how to execute model II linear regression analysis.

PubMed

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

Confidence Interval Coverage for Cohen's Effect Size Statistic

ERIC Educational Resources Information Center

Algina, James; Keselman, H. J.; Penfield, Randall D.

2006-01-01

Kelley compared three methods for setting a confidence interval (CI) around Cohen's standardized mean difference statistic: the noncentral-"t"-based, percentile (PERC) bootstrap, and biased-corrected and accelerated (BCA) bootstrap methods under three conditions of nonnormality, eight cases of sample size, and six cases of population…

A Bootstrap Procedure of Propensity Score Estimation

ERIC Educational Resources Information Center

Bai, Haiyan

2013-01-01

Propensity score estimation plays a fundamental role in propensity score matching for reducing group selection bias in observational data. To increase the accuracy of propensity score estimation, the author developed a bootstrap propensity score. The commonly used propensity score matching methods: nearest neighbor matching, caliper matching, and…

Subtyping of Children with Developmental Dyslexia via Bootstrap Aggregated Clustering and the Gap Statistic: Comparison with the Double-Deficit Hypothesis

ERIC Educational Resources Information Center

King, Wayne M.; Giess, Sally A.; Lombardino, Linda J.

2007-01-01

Background: The marked degree of heterogeneity in persons with developmental dyslexia has motivated the investigation of possible subtypes. Attempts have proceeded both from theoretical models of reading and the application of unsupervised learning (clustering) methods. Previous cluster analyses of data obtained from persons with reading…

Control Variates and Optimal Designs in Metamodeling

DTIC Science & Technology

2013-03-01

27 2.4.5 Selection of Control Variates for Inclusion in Model...meet the normality assumption (Nelson 1990, Nelson and Yang 1992, Anonuevo and Nelson 1988). Jacknifing, splitting, and bootstrapping can be used to...freedom to estimate the variance are lost due to being used for the control variate inclusion . This means the variance reduction achieved must now be

On testing an unspecified function through a linear mixed effects model with multiple variance components

PubMed Central

Wang, Yuanjia; Chen, Huaihou

2012-01-01

Summary We examine a generalized F-test of a nonparametric function through penalized splines and a linear mixed effects model representation. With a mixed effects model representation of penalized splines, we imbed the test of an unspecified function into a test of some fixed effects and a variance component in a linear mixed effects model with nuisance variance components under the null. The procedure can be used to test a nonparametric function or varying-coefficient with clustered data, compare two spline functions, test the significance of an unspecified function in an additive model with multiple components, and test a row or a column effect in a two-way analysis of variance model. Through a spectral decomposition of the residual sum of squares, we provide a fast algorithm for computing the null distribution of the test, which significantly improves the computational efficiency over bootstrap. The spectral representation reveals a connection between the likelihood ratio test (LRT) in a multiple variance components model and a single component model. We examine our methods through simulations, where we show that the power of the generalized F-test may be higher than the LRT, depending on the hypothesis of interest and the true model under the alternative. We apply these methods to compute the genome-wide critical value and p-value of a genetic association test in a genome-wide association study (GWAS), where the usual bootstrap is computationally intensive (up to 108 simulations) and asymptotic approximation may be unreliable and conservative. PMID:23020801

On testing an unspecified function through a linear mixed effects model with multiple variance components.

PubMed

Wang, Yuanjia; Chen, Huaihou

2012-12-01

We examine a generalized F-test of a nonparametric function through penalized splines and a linear mixed effects model representation. With a mixed effects model representation of penalized splines, we imbed the test of an unspecified function into a test of some fixed effects and a variance component in a linear mixed effects model with nuisance variance components under the null. The procedure can be used to test a nonparametric function or varying-coefficient with clustered data, compare two spline functions, test the significance of an unspecified function in an additive model with multiple components, and test a row or a column effect in a two-way analysis of variance model. Through a spectral decomposition of the residual sum of squares, we provide a fast algorithm for computing the null distribution of the test, which significantly improves the computational efficiency over bootstrap. The spectral representation reveals a connection between the likelihood ratio test (LRT) in a multiple variance components model and a single component model. We examine our methods through simulations, where we show that the power of the generalized F-test may be higher than the LRT, depending on the hypothesis of interest and the true model under the alternative. We apply these methods to compute the genome-wide critical value and p-value of a genetic association test in a genome-wide association study (GWAS), where the usual bootstrap is computationally intensive (up to 10(8) simulations) and asymptotic approximation may be unreliable and conservative. © 2012, The International Biometric Society.

Bootstrapping Methods Applied for Simulating Laboratory Works

ERIC Educational Resources Information Center

Prodan, Augustin; Campean, Remus

2005-01-01

Purpose: The aim of this work is to implement bootstrapping methods into software tools, based on Java. Design/methodology/approach: This paper presents a category of software e-tools aimed at simulating laboratory works and experiments. Findings: Both students and teaching staff use traditional statistical methods to infer the truth from sample…

Bootstrap Confidence Intervals for Ordinary Least Squares Factor Loadings and Correlations in Exploratory Factor Analysis

ERIC Educational Resources Information Center

Zhang, Guangjian; Preacher, Kristopher J.; Luo, Shanhong

2010-01-01

This article is concerned with using the bootstrap to assign confidence intervals for rotated factor loadings and factor correlations in ordinary least squares exploratory factor analysis. Coverage performances of "SE"-based intervals, percentile intervals, bias-corrected percentile intervals, bias-corrected accelerated percentile…

Bootstrap Estimation and Testing for Variance Equality.

ERIC Educational Resources Information Center

Olejnik, Stephen; Algina, James

The purpose of this study was to develop a single procedure for comparing population variances which could be used for distribution forms. Bootstrap methodology was used to estimate the variability of the sample variance statistic when the population distribution was normal, platykurtic and leptokurtic. The data for the study were generated and…

Evaluating the Use of Random Distribution Theory to Introduce Statistical Inference Concepts to Business Students

ERIC Educational Resources Information Center

Larwin, Karen H.; Larwin, David A.

2011-01-01

Bootstrapping methods and random distribution methods are increasingly recommended as better approaches for teaching students about statistical inference in introductory-level statistics courses. The authors examined the effect of teaching undergraduate business statistics students using random distribution and bootstrapping simulations. It is the…

The effects of time-varying observation errors on semi-empirical sea-level projections

DOE PAGES

Ruckert, Kelsey L.; Guan, Yawen; Bakker, Alexander M. R.; ...

2016-11-30

Sea-level rise is a key driver of projected flooding risks. The design of strategies to manage these risks often hinges on projections that inform decision-makers about the surrounding uncertainties. Producing semi-empirical sea-level projections is difficult, for example, due to the complexity of the error structure of the observations, such as time-varying (heteroskedastic) observation errors and autocorrelation of the data-model residuals. This raises the question of how neglecting the error structure impacts hindcasts and projections. Here, we quantify this effect on sea-level projections and parameter distributions by using a simple semi-empirical sea-level model. Specifically, we compare three model-fitting methods: a frequentistmore » bootstrap as well as a Bayesian inversion with and without considering heteroskedastic residuals. All methods produce comparable hindcasts, but the parametric distributions and projections differ considerably based on methodological choices. In conclusion, our results show that the differences based on the methodological choices are enhanced in the upper tail projections. For example, the Bayesian inversion accounting for heteroskedasticity increases the sea-level anomaly with a 1% probability of being equaled or exceeded in the year 2050 by about 34% and about 40% in the year 2100 compared to a frequentist bootstrap. These results indicate that neglecting known properties of the observation errors and the data-model residuals can lead to low-biased sea-level projections.« less

The effects of time-varying observation errors on semi-empirical sea-level projections

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ruckert, Kelsey L.; Guan, Yawen; Bakker, Alexander M. R.

Sea-level rise is a key driver of projected flooding risks. The design of strategies to manage these risks often hinges on projections that inform decision-makers about the surrounding uncertainties. Producing semi-empirical sea-level projections is difficult, for example, due to the complexity of the error structure of the observations, such as time-varying (heteroskedastic) observation errors and autocorrelation of the data-model residuals. This raises the question of how neglecting the error structure impacts hindcasts and projections. Here, we quantify this effect on sea-level projections and parameter distributions by using a simple semi-empirical sea-level model. Specifically, we compare three model-fitting methods: a frequentistmore » bootstrap as well as a Bayesian inversion with and without considering heteroskedastic residuals. All methods produce comparable hindcasts, but the parametric distributions and projections differ considerably based on methodological choices. In conclusion, our results show that the differences based on the methodological choices are enhanced in the upper tail projections. For example, the Bayesian inversion accounting for heteroskedasticity increases the sea-level anomaly with a 1% probability of being equaled or exceeded in the year 2050 by about 34% and about 40% in the year 2100 compared to a frequentist bootstrap. These results indicate that neglecting known properties of the observation errors and the data-model residuals can lead to low-biased sea-level projections.« less

Parametric modelling of cost data in medical studies.

PubMed

Nixon, R M; Thompson, S G

2004-04-30

The cost of medical resources used is often recorded for each patient in clinical studies in order to inform decision-making. Although cost data are generally skewed to the right, interest is in making inferences about the population mean cost. Common methods for non-normal data, such as data transformation, assuming asymptotic normality of the sample mean or non-parametric bootstrapping, are not ideal. This paper describes possible parametric models for analysing cost data. Four example data sets are considered, which have different sample sizes and degrees of skewness. Normal, gamma, log-normal, and log-logistic distributions are fitted, together with three-parameter versions of the latter three distributions. Maximum likelihood estimates of the population mean are found; confidence intervals are derived by a parametric BC(a) bootstrap and checked by MCMC methods. Differences between model fits and inferences are explored.Skewed parametric distributions fit cost data better than the normal distribution, and should in principle be preferred for estimating the population mean cost. However for some data sets, we find that models that fit badly can give similar inferences to those that fit well. Conversely, particularly when sample sizes are not large, different parametric models that fit the data equally well can lead to substantially different inferences. We conclude that inferences are sensitive to choice of statistical model, which itself can remain uncertain unless there is enough data to model the tail of the distribution accurately. Investigating the sensitivity of conclusions to choice of model should thus be an essential component of analysing cost data in practice. Copyright 2004 John Wiley & Sons, Ltd.

Prediction of two month modified Rankin Scale with an ordinal prediction model in patients with aneurysmal subarachnoid haemorrhage

PubMed Central

2010-01-01

Background Aneurysmal subarachnoid haemorrhage (aSAH) is a devastating event with a frequently disabling outcome. Our aim was to develop a prognostic model to predict an ordinal clinical outcome at two months in patients with aSAH. Methods We studied patients enrolled in the International Subarachnoid Aneurysm Trial (ISAT), a randomized multicentre trial to compare coiling and clipping in aSAH patients. Several models were explored to estimate a patient's outcome according to the modified Rankin Scale (mRS) at two months after aSAH. Our final model was validated internally with bootstrapping techniques. Results The study population comprised of 2,128 patients of whom 159 patients died within 2 months (8%). Multivariable proportional odds analysis identified World Federation of Neurosurgical Societies (WFNS) grade as the most important predictor, followed by age, sex, lumen size of the aneurysm, Fisher grade, vasospasm on angiography, and treatment modality. The model discriminated moderately between those with poor and good mRS scores (c statistic = 0.65), with minor optimism according to bootstrap re-sampling (optimism corrected c statistic = 0.64). Conclusion We presented a calibrated and internally validated ordinal prognostic model to predict two month mRS in aSAH patients who survived the early stage up till a treatment decision. Although generalizability of the model is limited due to the selected population in which it was developed, this model could eventually be used to support clinical decision making after external validation. Trial Registration International Standard Randomised Controlled Trial, Number ISRCTN49866681 PMID:20920243

Carving out the end of the world or (superconformal bootstrap in six dimensions)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, Chi-Ming; Lin, Ying-Hsuan

We bootstrap N=(1,0) superconformal field theories in six dimensions, by analyzing the four-point function of flavor current multiplets. By assuming E 8 flavor group, we present universal bounds on the central charge C T and the flavor central charge C J. Based on the numerical data, we conjecture that the rank-one E-string theory saturates the universal lower bound on C J , and numerically determine the spectrum of long multiplets in the rank-one E-string theory. We comment on the possibility of solving the higher-rank E-string theories by bootstrap and thereby probing M-theory on AdS 7×S 4/Z 2 .

Carving out the end of the world or (superconformal bootstrap in six dimensions)

DOE PAGES

Chang, Chi-Ming; Lin, Ying-Hsuan

2017-08-29

We bootstrap N=(1,0) superconformal field theories in six dimensions, by analyzing the four-point function of flavor current multiplets. By assuming E 8 flavor group, we present universal bounds on the central charge C T and the flavor central charge C J. Based on the numerical data, we conjecture that the rank-one E-string theory saturates the universal lower bound on C J , and numerically determine the spectrum of long multiplets in the rank-one E-string theory. We comment on the possibility of solving the higher-rank E-string theories by bootstrap and thereby probing M-theory on AdS 7×S 4/Z 2 .

Bootstrapping N=2 chiral correlators

NASA Astrophysics Data System (ADS)

Lemos, Madalena; Liendo, Pedro

2016-01-01

We apply the numerical bootstrap program to chiral operators in four-dimensional N=2 SCFTs. In the first part of this work we study four-point functions in which all fields have the same conformal dimension. We give special emphasis to bootstrapping a specific theory: the simplest Argyres-Douglas fixed point with no flavor symmetry. In the second part we generalize our setup and consider correlators of fields with unequal dimension. This is an example of a mixed correlator and allows us to probe new regions in the parameter space of N=2 SCFTs. In particular, our results put constraints on relations in the Coulomb branch chiral ring and on the curvature of the Zamolodchikov metric.

Exploring the Replicability of a Study's Results: Bootstrap Statistics for the Multivariate Case.

ERIC Educational Resources Information Center

Thompson, Bruce

Conventional statistical significance tests do not inform the researcher regarding the likelihood that results will replicate. One strategy for evaluating result replication is to use a "bootstrap" resampling of a study's data so that the stability of results across numerous configurations of the subjects can be explored. This paper…

Introducing Statistical Inference to Biology Students through Bootstrapping and Randomization

ERIC Educational Resources Information Center

Lock, Robin H.; Lock, Patti Frazer

2008-01-01

Bootstrap methods and randomization tests are increasingly being used as alternatives to standard statistical procedures in biology. They also serve as an effective introduction to the key ideas of statistical inference in introductory courses for biology students. We discuss the use of such simulation based procedures in an integrated curriculum…

Forgetski Vygotsky: Or, a Plea for Bootstrapping Accounts of Learning

ERIC Educational Resources Information Center

Luntley, Michael

2017-01-01

This paper argues that sociocultural accounts of learning fail to answer the key question about learning--how is it possible? Accordingly, we should adopt an individualist bootstrapping methodology in providing a theory of learning. Such a methodology takes seriously the idea that learning is staged and distinguishes between a non-comprehending…

Methods for Estimating Uncertainty in PMF Solutions: Examples with Ambient Air and Water Quality Data and Guidance on Reporting PMF Results

EPA Science Inventory

The new version of EPA’s positive matrix factorization (EPA PMF) software, 5.0, includes three error estimation (EE) methods for analyzing factor analytic solutions: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement (BS-DISP)...

Bootsie: estimation of coefficient of variation of AFLP data by bootstrap analysis

USDA-ARS?s Scientific Manuscript database

Bootsie is an English-native replacement for ASG Coelho’s “DBOOT” utility for estimating coefficient of variation of a population of AFLP marker data using bootstrapping. Bootsie improves on DBOOT by supporting batch processing, time-to-completion estimation, built-in graphs, and a suite of export t...

How to Bootstrap a Human Communication System

ERIC Educational Resources Information Center

Fay, Nicolas; Arbib, Michael; Garrod, Simon

2013-01-01

How might a human communication system be bootstrapped in the absence of conventional language? We argue that motivated signs play an important role (i.e., signs that are linked to meaning by structural resemblance or by natural association). An experimental study is then reported in which participants try to communicate a range of pre-specified…

Visuospatial bootstrapping: Binding useful visuospatial information during verbal working memory encoding does not require set-shifting executive resources.

PubMed

Calia, Clara; Darling, Stephen; Havelka, Jelena; Allen, Richard J

2018-05-01

Immediate serial recall of digits is better when the digits are shown by highlighting them in a familiar array, such as a phone keypad, compared with presenting them serially in a single location, a pattern referred to as "visuospatial bootstrapping." This pattern implies the establishment of temporary links between verbal and spatial working memory, alongside access to information in long-term memory. However, the role of working memory control processes like those implied by the "Central Executive" in bootstrapping has not been directly investigated. Here, we report a study addressing this issue, focusing on executive processes of attentional shifting. Tasks in which information has to be sequenced are thought to be heavily dependent on shifting. Memory for digits presented in keypads versus single locations was assessed under two secondary task load conditions, one with and one without a sequencing requirement, and hence differing in the degree to which they invoke shifting. Results provided clear evidence that multimodal binding (visuospatial bootstrapping) can operate independently of this form of executive control process.

A Bootstrap Metropolis-Hastings Algorithm for Bayesian Analysis of Big Data.

PubMed

Liang, Faming; Kim, Jinsu; Song, Qifan

2016-01-01

Markov chain Monte Carlo (MCMC) methods have proven to be a very powerful tool for analyzing data of complex structures. However, their computer-intensive nature, which typically require a large number of iterations and a complete scan of the full dataset for each iteration, precludes their use for big data analysis. In this paper, we propose the so-called bootstrap Metropolis-Hastings (BMH) algorithm, which provides a general framework for how to tame powerful MCMC methods to be used for big data analysis; that is to replace the full data log-likelihood by a Monte Carlo average of the log-likelihoods that are calculated in parallel from multiple bootstrap samples. The BMH algorithm possesses an embarrassingly parallel structure and avoids repeated scans of the full dataset in iterations, and is thus feasible for big data problems. Compared to the popular divide-and-combine method, BMH can be generally more efficient as it can asymptotically integrate the whole data information into a single simulation run. The BMH algorithm is very flexible. Like the Metropolis-Hastings algorithm, it can serve as a basic building block for developing advanced MCMC algorithms that are feasible for big data problems. This is illustrated in the paper by the tempering BMH algorithm, which can be viewed as a combination of parallel tempering and the BMH algorithm. BMH can also be used for model selection and optimization by combining with reversible jump MCMC and simulated annealing, respectively.

A Bootstrap Metropolis–Hastings Algorithm for Bayesian Analysis of Big Data

PubMed Central

Kim, Jinsu; Song, Qifan

2016-01-01

Markov chain Monte Carlo (MCMC) methods have proven to be a very powerful tool for analyzing data of complex structures. However, their computer-intensive nature, which typically require a large number of iterations and a complete scan of the full dataset for each iteration, precludes their use for big data analysis. In this paper, we propose the so-called bootstrap Metropolis-Hastings (BMH) algorithm, which provides a general framework for how to tame powerful MCMC methods to be used for big data analysis; that is to replace the full data log-likelihood by a Monte Carlo average of the log-likelihoods that are calculated in parallel from multiple bootstrap samples. The BMH algorithm possesses an embarrassingly parallel structure and avoids repeated scans of the full dataset in iterations, and is thus feasible for big data problems. Compared to the popular divide-and-combine method, BMH can be generally more efficient as it can asymptotically integrate the whole data information into a single simulation run. The BMH algorithm is very flexible. Like the Metropolis-Hastings algorithm, it can serve as a basic building block for developing advanced MCMC algorithms that are feasible for big data problems. This is illustrated in the paper by the tempering BMH algorithm, which can be viewed as a combination of parallel tempering and the BMH algorithm. BMH can also be used for model selection and optimization by combining with reversible jump MCMC and simulated annealing, respectively. PMID:29033469

Distilling free-form natural laws from experimental data.

PubMed

Schmidt, Michael; Lipson, Hod

2009-04-03

For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. A key challenge to finding analytic relations automatically is defining algorithmically what makes a correlation in observed data important and insightful. We propose a principle for the identification of nontriviality. We demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula. Without any prior knowledge about physics, kinematics, or geometry, the algorithm discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation. The discovery rate accelerated as laws found for simpler systems were used to bootstrap explanations for more complex systems, gradually uncovering the "alphabet" used to describe those systems.

An artifact caused by undersampling optimal trees in supermatrix analyses of locally sampled characters.

PubMed

Simmons, Mark P; Goloboff, Pablo A

2013-10-01

Empirical and simulated examples are used to demonstrate an artifact caused by undersampling optimal trees in data matrices that consist mostly or entirely of locally sampled (as opposed to globally, for most or all terminals) characters. The artifact is that unsupported clades consisting entirely of terminals scored for the same locally sampled partition may be resolved and assigned high resampling support-despite their being properly unsupported (i.e., not resolved in the strict consensus of all optimal trees). This artifact occurs despite application of random-addition sequences for stepwise terminal addition. The artifact is not necessarily obviated with thorough conventional branch swapping methods (even tree-bisection-reconnection) when just a single tree is held, as is sometimes implemented in parsimony bootstrap pseudoreplicates, and in every GARLI, PhyML, and RAxML pseudoreplicate and search for the most likely tree for the matrix as a whole. Hence GARLI, RAxML, and PhyML-based likelihood results require extra scrutiny, particularly when they provide high resolution and support for clades that are entirely unsupported by methods that perform more thorough searches, as in most parsimony analyses. Copyright © 2013 Elsevier Inc. All rights reserved.

Bootstrap Standard Errors for Maximum Likelihood Ability Estimates When Item Parameters Are Unknown

ERIC Educational Resources Information Center

Patton, Jeffrey M.; Cheng, Ying; Yuan, Ke-Hai; Diao, Qi

2014-01-01

When item parameter estimates are used to estimate the ability parameter in item response models, the standard error (SE) of the ability estimate must be corrected to reflect the error carried over from item calibration. For maximum likelihood (ML) ability estimates, a corrected asymptotic SE is available, but it requires a long test and the…

Illuminating Tradespace Decisions Using Efficient Experimental Space-Filling Designs for the Engineered Resilient System Architecture

DTIC Science & Technology

2015-06-30

7. Building Statistical Metamodels using Simulation Experimental Designs ............................................... 34 7.1. Statistical Design...system design drivers across several different domain models, our methodology uses statistical metamodeling to approximate the simulations’ behavior. A...output. We build metamodels using a number of statistical methods that include stepwise regression, boosted trees, neural nets, and bootstrap forest

Illuminating Tradespace Decisions Using Efficient Experimental Space-Filling Designs for the Engineered Resilient System Architecture

DTIC Science & Technology

2015-06-01

7. Building Statistical Metamodels using Simulation Experimental Designs ............................................... 34 7.1. Statistical Design...system design drivers across several different domain models, our methodology uses statistical metamodeling to approximate the simulations’ behavior. A...output. We build metamodels using a number of statistical methods that include stepwise regression, boosted trees, neural nets, and bootstrap forest

Four distinct types of E.C. 1.2.1.30 enzymes can catalyze the reduction of carboxylic acids to aldehydes.

PubMed

Stolterfoht, Holly; Schwendenwein, Daniel; Sensen, Christoph W; Rudroff, Florian; Winkler, Margit

2017-09-10

Increasing demand for chemicals from renewable resources calls for the development of new biotechnological methods for the reduction of oxidized bio-based compounds. Enzymatic carboxylate reduction is highly selective, both in terms of chemo- and product selectivity, but not many carboxylate reductase enzymes (CARs) have been identified on the sequence level to date. Thus far, their phylogeny is unexplored and very little is known about their structure-function-relationship. CARs minimally contain an adenylation domain, a phosphopantetheinylation domain and a reductase domain. We have recently identified new enzymes of fungal origin, using similarity searches against genomic sequences from organisms in which aldehydes were detected upon incubation with carboxylic acids. Analysis of sequences with known CAR functionality and CAR enzymes recently identified in our laboratory suggests that the three-domain architecture mentioned above is modular. The construction of a distance tree with a subsequent 1000-replicate bootstrap analysis showed that the CAR sequences included in our study fall into four distinct subgroups (one of bacterial origin and three of fungal origin, respectively), each with a bootstrap value of 100%. The multiple sequence alignment of all experimentally confirmed CAR protein sequences revealed fingerprint sequences of residues which are likely to be involved in substrate and co-substrate binding and one of the three catalytic substeps, respectively. The fingerprint sequences broaden our understanding of the amino acids that might be essential for the reduction of organic acids to the corresponding aldehydes in CAR proteins. Copyright © 2017 Elsevier B.V. All rights reserved.

Bootstrap current control studies in the Wendelstein 7-X stellarator using the free-plasma-boundary version of the SIESTA MHD equilibrium code

NASA Astrophysics Data System (ADS)

Peraza-Rodriguez, H.; Reynolds-Barredo, J. M.; Sanchez, R.; Tribaldos, V.; Geiger, J.

2018-02-01

The recently developed free-plasma-boundary version of the SIESTA MHD equilibrium code (Hirshman et al 2011 Phys. Plasmas 18 062504; Peraza-Rodriguez et al 2017 Phys. Plasmas 24 082516) is used for the first time to study scenarios with considerable bootstrap currents for the Wendelstein 7-X (W7-X) stellarator. Bootstrap currents in the range of tens of kAs can lead to the formation of unwanted magnetic island chains or stochastic regions within the plasma and alter the boundary rotational transform due to the small shear in W7-X. The latter issue is of relevance since the island divertor operation of W7-X relies on a proper positioning of magnetic island chains at the plasma edge to control the particle and energy exhaust towards the divertor plates. Two scenarios are examined with the new free-plasma-boundary capabilities of SIESTA: a freely evolving bootstrap current one that illustrates the difficulties arising from the dislocation of the boundary islands, and a second one in which off-axis electron cyclotron current drive (ECCD) is applied to compensate the effects of the bootstrap current and keep the island divertor configuration intact. SIESTA finds that off-axis ECCD is indeed able to keep the location and phase of the edge magnetic island chain unchanged, but it may also lead to an undesired stochastization of parts of the confined plasma if the EC deposition radial profile becomes too narrow.

One- and two-stage Arrhenius models for pharmaceutical shelf life prediction.

PubMed

Fan, Zhewen; Zhang, Lanju

2015-01-01

One of the most challenging aspects of the pharmaceutical development is the demonstration and estimation of chemical stability. It is imperative that pharmaceutical products be stable for two or more years. Long-term stability studies are required to support such shelf life claim at registration. However, during drug development to facilitate formulation and dosage form selection, an accelerated stability study with stressed storage condition is preferred to quickly obtain a good prediction of shelf life under ambient storage conditions. Such a prediction typically uses Arrhenius equation that describes relationship between degradation rate and temperature (and humidity). Existing methods usually rely on the assumption of normality of the errors. In addition, shelf life projection is usually based on confidence band of a regression line. However, the coverage probability of a method is often overlooked or under-reported. In this paper, we introduce two nonparametric bootstrap procedures for shelf life estimation based on accelerated stability testing, and compare them with a one-stage nonlinear Arrhenius prediction model. Our simulation results demonstrate that one-stage nonlinear Arrhenius method has significant lower coverage than nominal levels. Our bootstrap method gave better coverage and led to a shelf life prediction closer to that based on long-term stability data.

Generating a Dynamic Synthetic Population – Using an Age-Structured Two-Sex Model for Household Dynamics

PubMed Central

Namazi-Rad, Mohammad-Reza; Mokhtarian, Payam; Perez, Pascal

2014-01-01

Generating a reliable computer-simulated synthetic population is necessary for knowledge processing and decision-making analysis in agent-based systems in order to measure, interpret and describe each target area and the human activity patterns within it. In this paper, both synthetic reconstruction (SR) and combinatorial optimisation (CO) techniques are discussed for generating a reliable synthetic population for a certain geographic region (in Australia) using aggregated- and disaggregated-level information available for such an area. A CO algorithm using the quadratic function of population estimators is presented in this paper in order to generate a synthetic population while considering a two-fold nested structure for the individuals and households within the target areas. The baseline population in this study is generated from the confidentialised unit record files (CURFs) and 2006 Australian census tables. The dynamics of the created population is then projected over five years using a dynamic micro-simulation model for individual- and household-level demographic transitions. This projection is then compared with the 2011 Australian census. A prediction interval is provided for the population estimates obtained by the bootstrapping method, by which the variability structure of a predictor can be replicated in a bootstrap distribution. PMID:24733522

Workplace ostracism And workplace behaviors: A moderated mediation model of perceived stress and psychological empowerment.

PubMed

Chung, Yang Woon

2018-05-01

Workplace ostracism research has examined numerous underlying mechanisms to understand the link between workplace ostracism and behavioral outcomes. Ostracism has been suggested to be an interpersonal stressor; however, research has not investigated workplace ostracism from a stress perspective. Therefore, the study investigated the mediating effect of perceived stress for the relationships between workplace ostracism and helping behavior, voicing behavior, and task performance. The study also investigated the moderating effect of psychological empowerment for the relationships between perceived stress and behavioral outcomes. The study design was a three-wave self-reported questionnaire. The study sampled 225 full-time employees in South Korea and regression analyses with bootstrapping were conducted to test the moderated mediation models. The bootstrapped 95% CI around the indirect effects did not contain zero; therefore, perceived stress mediated the relationship between workplace ostracism and helping behavior (-.06), voicing behavior (-.07), and task performance (-.07). Further, the moderated mediation analyses found perceived stress mediated the relationships between workplace ostracism and behavioral outcomes only when individuals perceived low levels of psychological empowerment. The findings suggest that workplace ostracism is a stressor and psychological empowerment can mitigate the negative effects of ostracism on behavioral outcomes.

Robust functional regression model for marginal mean and subject-specific inferences.

PubMed

Cao, Chunzheng; Shi, Jian Qing; Lee, Youngjo

2017-01-01

We introduce flexible robust functional regression models, using various heavy-tailed processes, including a Student t-process. We propose efficient algorithms in estimating parameters for the marginal mean inferences and in predicting conditional means as well as interpolation and extrapolation for the subject-specific inferences. We develop bootstrap prediction intervals (PIs) for conditional mean curves. Numerical studies show that the proposed model provides a robust approach against data contamination or distribution misspecification, and the proposed PIs maintain the nominal confidence levels. A real data application is presented as an illustrative example.

BIOMedical Search Engine Framework: Lightweight and customized implementation of domain-specific biomedical search engines.

PubMed

Jácome, Alberto G; Fdez-Riverola, Florentino; Lourenço, Anália

2016-07-01

Text mining and semantic analysis approaches can be applied to the construction of biomedical domain-specific search engines and provide an attractive alternative to create personalized and enhanced search experiences. Therefore, this work introduces the new open-source BIOMedical Search Engine Framework for the fast and lightweight development of domain-specific search engines. The rationale behind this framework is to incorporate core features typically available in search engine frameworks with flexible and extensible technologies to retrieve biomedical documents, annotate meaningful domain concepts, and develop highly customized Web search interfaces. The BIOMedical Search Engine Framework integrates taggers for major biomedical concepts, such as diseases, drugs, genes, proteins, compounds and organisms, and enables the use of domain-specific controlled vocabulary. Technologies from the Typesafe Reactive Platform, the AngularJS JavaScript framework and the Bootstrap HTML/CSS framework support the customization of the domain-oriented search application. Moreover, the RESTful API of the BIOMedical Search Engine Framework allows the integration of the search engine into existing systems or a complete web interface personalization. The construction of the Smart Drug Search is described as proof-of-concept of the BIOMedical Search Engine Framework. This public search engine catalogs scientific literature about antimicrobial resistance, microbial virulence and topics alike. The keyword-based queries of the users are transformed into concepts and search results are presented and ranked accordingly. The semantic graph view portraits all the concepts found in the results, and the researcher may look into the relevance of different concepts, the strength of direct relations, and non-trivial, indirect relations. The number of occurrences of the concept shows its importance to the query, and the frequency of concept co-occurrence is indicative of biological relations meaningful to that particular scope of research. Conversely, indirect concept associations, i.e. concepts related by other intermediary concepts, can be useful to integrate information from different studies and look into non-trivial relations. The BIOMedical Search Engine Framework supports the development of domain-specific search engines. The key strengths of the framework are modularity and extensibilityin terms of software design, the use of open-source consolidated Web technologies, and the ability to integrate any number of biomedical text mining tools and information resources. Currently, the Smart Drug Search keeps over 1,186,000 documents, containing more than 11,854,000 annotations for 77,200 different concepts. The Smart Drug Search is publicly accessible at http://sing.ei.uvigo.es/sds/. The BIOMedical Search Engine Framework is freely available for non-commercial use at https://github.com/agjacome/biomsef. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials.

PubMed

Dudaniec, Rachael Y; Worthington Wilmer, Jessica; Hanson, Jeffrey O; Warren, Matthew; Bell, Sarah; Rhodes, Jonathan R

2016-01-01

Landscape genetics lacks explicit methods for dealing with the uncertainty in landscape resistance estimation, which is particularly problematic when sample sizes of individuals are small. Unless uncertainty can be quantified, valuable but small data sets may be rendered unusable for conservation purposes. We offer a method to quantify uncertainty in landscape resistance estimates using multimodel inference as an improvement over single model-based inference. We illustrate the approach empirically using co-occurring, woodland-preferring Australian marsupials within a common study area: two arboreal gliders (Petaurus breviceps, and Petaurus norfolcensis) and one ground-dwelling antechinus (Antechinus flavipes). First, we use maximum-likelihood and a bootstrap procedure to identify the best-supported isolation-by-resistance model out of 56 models defined by linear and non-linear resistance functions. We then quantify uncertainty in resistance estimates by examining parameter selection probabilities from the bootstrapped data. The selection probabilities provide estimates of uncertainty in the parameters that drive the relationships between landscape features and resistance. We then validate our method for quantifying uncertainty using simulated genetic and landscape data showing that for most parameter combinations it provides sensible estimates of uncertainty. We conclude that small data sets can be informative in landscape genetic analyses provided uncertainty can be explicitly quantified. Being explicit about uncertainty in landscape genetic models will make results more interpretable and useful for conservation decision-making, where dealing with uncertainty is critical. © 2015 John Wiley & Sons Ltd.

Assessing participation in community-based physical activity programs in Brazil.

PubMed

Reis, Rodrigo S; Yan, Yan; Parra, Diana C; Brownson, Ross C

2014-01-01

This study aimed to develop and validate a risk prediction model to examine the characteristics that are associated with participation in community-based physical activity programs in Brazil. We used pooled data from three surveys conducted from 2007 to 2009 in state capitals of Brazil with 6166 adults. A risk prediction model was built considering program participation as an outcome. The predictive accuracy of the model was quantified through discrimination (C statistic) and calibration (Brier score) properties. Bootstrapping methods were used to validate the predictive accuracy of the final model. The final model showed sex (women: odds ratio [OR] = 3.18, 95% confidence interval [CI] = 2.14-4.71), having less than high school degree (OR = 1.71, 95% CI = 1.16-2.53), reporting a good health (OR = 1.58, 95% CI = 1.02-2.24) or very good/excellent health (OR = 1.62, 95% CI = 1.05-2.51), having any comorbidity (OR = 1.74, 95% CI = 1.26-2.39), and perceiving the environment as safe to walk at night (OR = 1.59, 95% CI = 1.18-2.15) as predictors of participation in physical activity programs. Accuracy indices were adequate (C index = 0.778, Brier score = 0.031) and similar to those obtained from bootstrapping (C index = 0.792, Brier score = 0.030). Sociodemographic and health characteristics as well as perceptions of the environment are strong predictors of participation in community-based programs in selected cities of Brazil.

Pulling Econometrics Students up by Their Bootstraps

ERIC Educational Resources Information Center

O'Hara, Michael E.

2014-01-01

Although the concept of the sampling distribution is at the core of much of what we do in econometrics, it is a concept that is often difficult for students to grasp. The thought process behind bootstrapping provides a way for students to conceptualize the sampling distribution in a way that is intuitive and visual. However, teaching students to…

Accuracy assessment of percent canopy cover, cover type, and size class

Treesearch

H. T. Schreuder; S. Bain; R. C. Czaplewski

2003-01-01

Truth for vegetation cover percent and type is obtained from very large-scale photography (VLSP), stand structure as measured by size classes, and vegetation types from a combination of VLSP and ground sampling. We recommend using the Kappa statistic with bootstrap confidence intervals for overall accuracy, and similarly bootstrap confidence intervals for percent...

Finding One's Meaning: A Test of the Relation between Quantifiers and Integers in Language Development

ERIC Educational Resources Information Center

Barner, David; Chow, Katherine; Yang, Shu-Ju

2009-01-01

We explored children's early interpretation of numerals and linguistic number marking, in order to test the hypothesis (e.g., Carey (2004). Bootstrapping and the origin of concepts. "Daedalus", 59-68) that children's initial distinction between "one" and other numerals (i.e., "two," "three," etc.) is bootstrapped from a prior distinction between…

A Resampling Analysis of Federal Family Assistance Program Quality Control Data: An Application of the Bootstrap.

ERIC Educational Resources Information Center

Hand, Michael L.

1990-01-01

Use of the bootstrap resampling technique (BRT) is assessed in its application to resampling analysis associated with measurement of payment allocation errors by federally funded Family Assistance Programs. The BRT is applied to a food stamp quality control database in Oregon. This analysis highlights the outlier-sensitivity of the…

Calculating Confidence Intervals for Regional Economic Impacts of Recreastion by Bootstrapping Visitor Expenditures

Treesearch

Donald B.K. English

2000-01-01

In this paper I use bootstrap procedures to develop confidence intervals for estimates of total industrial output generated per thousand tourist visits. Mean expenditures from replicated visitor expenditure data included weights to correct for response bias. Impacts were estimated with IMPLAN. Ninety percent interval endpoints were 6 to 16 percent above or below the...

Comparison of Methods for Estimating Low Flow Characteristics of Streams

USGS Publications Warehouse

Tasker, Gary D.

1987-01-01

Four methods for estimating the 7-day, 10-year and 7-day, 20-year low flows for streams are compared by the bootstrap method. The bootstrap method is a Monte Carlo technique in which random samples are drawn from an unspecified sampling distribution defined from observed data. The nonparametric nature of the bootstrap makes it suitable for comparing methods based on a flow series for which the true distribution is unknown. Results show that the two methods based on hypothetical distribution (Log-Pearson III and Weibull) had lower mean square errors than did the G. E. P. Box-D. R. Cox transformation method or the Log-W. C. Boughton method which is based on a fit of plotting positions.

Use of volatile organic components in scat to identify canid species

USGS Publications Warehouse

Burnham, E.; Bender, L.C.; Eiceman, G.A.; Pierce, K.M.; Prasad, S.

2008-01-01

Identification of wildlife species from indirect evidence can be an important part of wildlife management, and conventional +methods can be expensive or have high error rates. We used chemical characterization of the volatile organic constituents (VOCs) in scat as a method to identify 5 species of North American canids from multiple individuals. We sampled vapors of scats in the headspace over a sample using solid-phase microextraction and determined VOC content using gas chromatography with a flame ionization detector. We used linear discriminant analysis to develop models for differentiating species with bootstrapping to estimate accuracy. Our method correcdy classified 82.4% (bootstrapped 95% CI = 68.8-93.8%) of scat samples. Red fox (Vulpes vulpes) scat was most frequendy misclassified (25.0% of scats misclassified); red fox was also the most common destination for misclassified samples. Our findings are the first reported identification of animal species using VOCs in vapor emissions from scat and suggest that identification of wildlife species may be plausible through chemical characterization of vapor emissions of scat.

Service Mediation and Negotiation Bootstrapping as First Achievements Towards Self-adaptable Cloud Services

NASA Astrophysics Data System (ADS)

Brandic, Ivona; Music, Dejan; Dustdar, Schahram

Nowadays, novel computing paradigms as for example Cloud Computing are gaining more and more on importance. In case of Cloud Computing users pay for the usage of the computing power provided as a service. Beforehand they can negotiate specific functional and non-functional requirements relevant for the application execution. However, providing computing power as a service bears different research challenges. On one hand dynamic, versatile, and adaptable services are required, which can cope with system failures and environmental changes. On the other hand, human interaction with the system should be minimized. In this chapter we present the first results in establishing adaptable, versatile, and dynamic services considering negotiation bootstrapping and service mediation achieved in context of the Foundations of Self-Governing ICT Infrastructures (FoSII) project. We discuss novel meta-negotiation and SLA mapping solutions for Cloud services bridging the gap between current QoS models and Cloud middleware and representing important prerequisites for the establishment of autonomic Cloud services.

A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data.

PubMed

Nasejje, Justine B; Mwambi, Henry; Dheda, Keertan; Lesosky, Maia

2017-07-28

Random survival forest (RSF) models have been identified as alternative methods to the Cox proportional hazards model in analysing time-to-event data. These methods, however, have been criticised for the bias that results from favouring covariates with many split-points and hence conditional inference forests for time-to-event data have been suggested. Conditional inference forests (CIF) are known to correct the bias in RSF models by separating the procedure for the best covariate to split on from that of the best split point search for the selected covariate. In this study, we compare the random survival forest model to the conditional inference model (CIF) using twenty-two simulated time-to-event datasets. We also analysed two real time-to-event datasets. The first dataset is based on the survival of children under-five years of age in Uganda and it consists of categorical covariates with most of them having more than two levels (many split-points). The second dataset is based on the survival of patients with extremely drug resistant tuberculosis (XDR TB) which consists of mainly categorical covariates with two levels (few split-points). The study findings indicate that the conditional inference forest model is superior to random survival forest models in analysing time-to-event data that consists of covariates with many split-points based on the values of the bootstrap cross-validated estimates for integrated Brier scores. However, conditional inference forests perform comparably similar to random survival forests models in analysing time-to-event data consisting of covariates with fewer split-points. Although survival forests are promising methods in analysing time-to-event data, it is important to identify the best forest model for analysis based on the nature of covariates of the dataset in question.

Filtering Raw Terrestrial Laser Scanning Data for Efficient and Accurate Use in Geomorphologic Modeling

NASA Astrophysics Data System (ADS)

Gleason, M. J.; Pitlick, J.; Buttenfield, B. P.

2011-12-01

Terrestrial laser scanning (TLS) represents a new and particularly effective remote sensing technique for investigating geomorphologic processes. Unfortunately, TLS data are commonly characterized by extremely large volume, heterogeneous point distribution, and erroneous measurements, raising challenges for applied researchers. To facilitate efficient and accurate use of TLS in geomorphology, and to improve accessibility for TLS processing in commercial software environments, we are developing a filtering method for raw TLS data to: eliminate data redundancy; produce a more uniformly spaced dataset; remove erroneous measurements; and maintain the ability of the TLS dataset to accurately model terrain. Our method conducts local aggregation of raw TLS data using a 3-D search algorithm based on the geometrical expression of expected random errors in the data. This approach accounts for the estimated accuracy and precision limitations of the instruments and procedures used in data collection, thereby allowing for identification and removal of potential erroneous measurements prior to data aggregation. Initial tests of the proposed technique on a sample TLS point cloud required a modest processing time of approximately 100 minutes to reduce dataset volume over 90 percent (from 12,380,074 to 1,145,705 points). Preliminary analysis of the filtered point cloud revealed substantial improvement in homogeneity of point distribution and minimal degradation of derived terrain models. We will test the method on two independent TLS datasets collected in consecutive years along a non-vegetated reach of the North Fork Toutle River in Washington. We will evaluate the tool using various quantitative, qualitative, and statistical methods. The crux of this evaluation will include a bootstrapping analysis to test the ability of the filtered datasets to model the terrain at roughly the same accuracy as the raw datasets.

Assessment of oximetry-based statistical classifiers as simplified screening tools in the management of childhood obstructive sleep apnea.

PubMed

Crespo, Andrea; Álvarez, Daniel; Kheirandish-Gozal, Leila; Gutiérrez-Tobal, Gonzalo C; Cerezo-Hernández, Ana; Gozal, David; Hornero, Roberto; Del Campo, Félix

2018-02-16

A variety of statistical models based on overnight oximetry has been proposed to simplify the detection of children with suspected obstructive sleep apnea syndrome (OSAS). Despite the usefulness reported, additional thorough comparative analyses are required. This study was aimed at assessing common binary classification models from oximetry for the detection of childhood OSAS. Overnight oximetry recordings from 176 children referred for clinical suspicion of OSAS were acquired during in-lab polysomnography. Several training and test datasets were randomly composed by means of bootstrapping for model optimization and independent validation. For every child, blood oxygen saturation (SpO 2 ) was parameterized by means of 17 features. Fast correlation-based filter (FCBF) was applied to search for the optimum features. The discriminatory power of three statistical pattern recognition algorithms was assessed: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and logistic regression (LR). The performance of each automated model was evaluated for the three common diagnostic polysomnographic cutoffs in pediatric OSAS: 1, 3, and 5 events/h. Best screening performances emerged using the 1 event/h cutoff for mild-to-severe childhood OSAS. LR achieved 84.3% accuracy (95% CI 76.8-91.5%) and 0.89 AUC (95% CI 0.83-0.94), while QDA reached 96.5% PPV (95% CI 90.3-100%) and 0.91 AUC (95% CI 0.85-0.96%). Moreover, LR and QDA reached diagnostic accuracies of 82.7% (95% CI 75.0-89.6%) and 82.1% (95% CI 73.8-89.5%) for a cutoff of 5 events/h, respectively. Automated analysis of overnight oximetry may be used to develop reliable as well as accurate screening tools for childhood OSAS.

Development of the PCAD Model to Assess Biological Significance of Acoustic Disturbance

DTIC Science & Technology

2013-09-30

substantial pre-existing knowledge of foraging patterns , life-history schedules, and demographics. Therefore, it is essential to use well-studied species to...transiting areas of the post-molt migration . Using a bootstrapping approach, we simulated thousands of disturbances to achieve realistic error estimates...resident population). Given seasonal differences in calving, causes of mortality, and movement patterns , we will estimate demographic rates on a

Music and Science: Tribute to Rolf Hagedorn

NASA Astrophysics Data System (ADS)

Jacob, Maurice

I present here Rolf Hagedorn as a man, and present his achievements as a physicist. He has made several very important contributions: to particle and nuclear fields of research: The Hagedorn Temperature and the Statistical Bootstrap Model are concepts that are here to stay, and which have stimulated much further research. But Rolf Hagedorn is also a wonderful person and, saying that, does not require a specialist.

Extraterrestrial materials processing and construction. [space industrialization

NASA Technical Reports Server (NTRS)

Criswell, D. R.; Waldron, R. D.; Mckenzie, J. D.

1980-01-01

Three different chemical processing schemes were identified for separating lunar soils into the major oxides and elements. Feedstock production for space industry; an HF acid leach process; electrorefining processes for lunar free metal and metal derived from chemical processing of lunar soils; production and use of silanes and spectrally selective materials; glass, ceramics, and electrochemistry workshops; and an econometric model of bootstrapping space industry are discussed.

Bootstrap simulation, Markov decision process models, and role of discounting in the valuation of ecological criteria in uneven-aged forest management

Treesearch

Mo Zhou; Joseph Buongiorno; Jingjing Liang

2012-01-01

Besides the market value of timber, forests provide substantial nonmarket benefits, especially with continuous-cover silviculture, which have long been acknowledged by forest managers. They include wildlife habitat (e.g. Bevers and Hof 1999), carbon sequestration (e.g. Dewar and Cannell 1992), biodiversity (e.g. Kangas and Kuusipalo 1993; Austin and Meyers 1999),...

The Relationships among Students' Future-Oriented Goals and Subgoals, Perceived Task Instrumentality, and Task-Oriented Self-Regulation Strategies in an Academic Environment

ERIC Educational Resources Information Center

Tabachnick, Sharon E.; Miller, Raymond B.; Relyea, George E.

2008-01-01

The authors performed path analysis, followed by a bootstrap procedure, to test the predictions of a model explaining the relationships among students' distal future goals (both extrinsic and intrinsic), their adoption of a middle-range subgoal, their perceptions of task instrumentality, and their proximal task-oriented self-regulation strategies.…

Technical and scale efficiency in public and private Irish nursing homes - a bootstrap DEA approach.

PubMed

Ni Luasa, Shiovan; Dineen, Declan; Zieba, Marta

2016-10-27

This article provides methodological and empirical insights into the estimation of technical efficiency in the nursing home sector. Focusing on long-stay care and using primary data, we examine technical and scale efficiency in 39 public and 73 private Irish nursing homes by applying an input-oriented data envelopment analysis (DEA). We employ robust bootstrap methods to validate our nonparametric DEA scores and to integrate the effects of potential determinants in estimating the efficiencies. Both the homogenous and two-stage double bootstrap procedures are used to obtain confidence intervals for the bias-corrected DEA scores. Importantly, the application of the double bootstrap approach affords true DEA technical efficiency scores after adjusting for the effects of ownership, size, case-mix, and other determinants such as location, and quality. Based on our DEA results for variable returns to scale technology, the average technical efficiency score is 62 %, and the mean scale efficiency is 88 %, with nearly all units operating on the increasing returns to scale part of the production frontier. Moreover, based on the double bootstrap results, Irish nursing homes are less technically efficient, and more scale efficient than the conventional DEA estimates suggest. Regarding the efficiency determinants, in terms of ownership, we find that private facilities are less efficient than the public units. Furthermore, the size of the nursing home has a positive effect, and this reinforces our finding that Irish homes produce at increasing returns to scale. Also, notably, we find that a tendency towards quality improvements can lead to poorer technical efficiency performance.

Empirical single sample quantification of bias and variance in Q-ball imaging.

PubMed

Hainline, Allison E; Nath, Vishwesh; Parvathaneni, Prasanna; Blaber, Justin A; Schilling, Kurt G; Anderson, Adam W; Kang, Hakmook; Landman, Bennett A

2018-02-06

The bias and variance of high angular resolution diffusion imaging methods have not been thoroughly explored in the literature and may benefit from the simulation extrapolation (SIMEX) and bootstrap techniques to estimate bias and variance of high angular resolution diffusion imaging metrics. The SIMEX approach is well established in the statistics literature and uses simulation of increasingly noisy data to extrapolate back to a hypothetical case with no noise. The bias of calculated metrics can then be computed by subtracting the SIMEX estimate from the original pointwise measurement. The SIMEX technique has been studied in the context of diffusion imaging to accurately capture the bias in fractional anisotropy measurements in DTI. Herein, we extend the application of SIMEX and bootstrap approaches to characterize bias and variance in metrics obtained from a Q-ball imaging reconstruction of high angular resolution diffusion imaging data. The results demonstrate that SIMEX and bootstrap approaches provide consistent estimates of the bias and variance of generalized fractional anisotropy, respectively. The RMSE for the generalized fractional anisotropy estimates shows a 7% decrease in white matter and an 8% decrease in gray matter when compared with the observed generalized fractional anisotropy estimates. On average, the bootstrap technique results in SD estimates that are approximately 97% of the true variation in white matter, and 86% in gray matter. Both SIMEX and bootstrap methods are flexible, estimate population characteristics based on single scans, and may be extended for bias and variance estimation on a variety of high angular resolution diffusion imaging metrics. © 2018 International Society for Magnetic Resonance in Medicine.

Multinomial Logistic Regression & Bootstrapping for Bayesian Estimation of Vertical Facies Prediction in Heterogeneous Sandstone Reservoirs

NASA Astrophysics Data System (ADS)

Al-Mudhafar, W. J.

2013-12-01

Precisely prediction of rock facies leads to adequate reservoir characterization by improving the porosity-permeability relationships to estimate the properties in non-cored intervals. It also helps to accurately identify the spatial facies distribution to perform an accurate reservoir model for optimal future reservoir performance. In this paper, the facies estimation has been done through Multinomial logistic regression (MLR) with respect to the well logs and core data in a well in upper sandstone formation of South Rumaila oil field. The entire independent variables are gamma rays, formation density, water saturation, shale volume, log porosity, core porosity, and core permeability. Firstly, Robust Sequential Imputation Algorithm has been considered to impute the missing data. This algorithm starts from a complete subset of the dataset and estimates sequentially the missing values in an incomplete observation by minimizing the determinant of the covariance of the augmented data matrix. Then, the observation is added to the complete data matrix and the algorithm continues with the next observation with missing values. The MLR has been chosen to estimate the maximum likelihood and minimize the standard error for the nonlinear relationships between facies & core and log data. The MLR is used to predict the probabilities of the different possible facies given each independent variable by constructing a linear predictor function having a set of weights that are linearly combined with the independent variables by using a dot product. Beta distribution of facies has been considered as prior knowledge and the resulted predicted probability (posterior) has been estimated from MLR based on Baye's theorem that represents the relationship between predicted probability (posterior) with the conditional probability and the prior knowledge. To assess the statistical accuracy of the model, the bootstrap should be carried out to estimate extra-sample prediction error by randomly drawing datasets with replacement from the training data. Each sample has the same size of the original training set and it can be conducted N times to produce N bootstrap datasets to re-fit the model accordingly to decrease the squared difference between the estimated and observed categorical variables (facies) leading to decrease the degree of uncertainty.

Statistic and dosimetric criteria to assess the shift of the prescribed dose for lung radiotherapy plans when integrating point kernel models in medical physics: are we ready?

PubMed

Chaikh, Abdulhamid; Balosso, Jacques

2016-12-01

To apply the statistical bootstrap analysis and dosimetric criteria's to assess the change of prescribed dose (PD) for lung cancer to maintain the same clinical results when using new generations of dose calculation algorithms. Nine lung cancer cases were studied. For each patient, three treatment plans were generated using exactly the same beams arrangements. In plan 1, the dose was calculated using pencil beam convolution (PBC) algorithm turning on heterogeneity correction with modified batho (PBC-MB). In plan 2, the dose was calculated using anisotropic analytical algorithm (AAA) and the same PD, as plan 1. In plan 3, the dose was calculated using AAA with monitor units (MUs) obtained from PBC-MB, as input. The dosimetric criteria's include MUs, delivered dose at isocentre (Diso) and calculated dose to 95% of the target volume (D95). The bootstrap method was used to assess the significance of the dose differences and to accurately estimate the 95% confidence interval (95% CI). Wilcoxon and Spearman's rank tests were used to calculate P values and the correlation coefficient (ρ). Statistically significant for dose difference was found using point kernel model. A good correlation was observed between both algorithms types, with ρ>0.9. Using AAA instead of PBC-MB, an adjustment of the PD in the isocentre is suggested. For a given set of patients, we assessed the need to readjust the PD for lung cancer using dosimetric indices and bootstrap statistical method. Thus, if the goal is to keep on with the same clinical results, the PD for lung tumors has to be adjusted with AAA. According to our simulation we suggest to readjust the PD by 5% and an optimization for beam arrangements to better protect the organs at risks (OARs).

Bootstrapping rapidity anomalous dimensions for transverse-momentum resummation

DOE PAGES

Li, Ye; Zhu, Hua Xing

2017-01-11

Soft function relevant for transverse-momentum resummation for Drell-Yan or Higgs production at hadron colliders are computed through to three loops in the expansion of strong coupling, with the help of bootstrap technique and supersymmetric decomposition. The corresponding rapidity anomalous dimension is extracted. Furthermore, an intriguing relation between anomalous dimensions for transverse-momentum resummation and threshold resummation is found.

Reliability of confidence intervals calculated by bootstrap and classical methods using the FIA 1-ha plot design

Treesearch

H. T. Schreuder; M. S. Williams

2000-01-01

In simulation sampling from forest populations using sample sizes of 20, 40, and 60 plots respectively, confidence intervals based on the bootstrap (accelerated, percentile, and t-distribution based) were calculated and compared with those based on the classical t confidence intervals for mapped populations and subdomains within those populations. A 68.1 ha mapped...

Morphological Cues vs. Number of Nominals in Learning Verb Types in Turkish: The Syntactic Bootstrapping Mechanism Revisited

ERIC Educational Resources Information Center

Ural, A. Engin; Yuret, Deniz; Ketrez, F. Nihan; Kocbas, Dilara; Kuntay, Aylin C.

2009-01-01

The syntactic bootstrapping mechanism of verb learning was evaluated against child-directed speech in Turkish, a language with rich morphology, nominal ellipsis and free word order. Machine-learning algorithms were run on transcribed caregiver speech directed to two Turkish learners (one hour every two weeks between 0;9 to 1;10) of different…

A Comparison of the Bootstrap-F, Improved General Approximation, and Brown-Forsythe Multivariate Approaches in a Mixed Repeated Measures Design

ERIC Educational Resources Information Center

Seco, Guillermo Vallejo; Izquierdo, Marcelino Cuesta; Garcia, M. Paula Fernandez; Diez, F. Javier Herrero

2006-01-01

The authors compare the operating characteristics of the bootstrap-F approach, a direct extension of the work of Berkovits, Hancock, and Nevitt, with Huynh's improved general approximation (IGA) and the Brown-Forsythe (BF) multivariate approach in a mixed repeated measures design when normality and multisample sphericity assumptions do not hold.…

Sample-based estimation of tree species richness in a wet tropical forest compartment

Treesearch

Steen Magnussen; Raphael Pelissier

2007-01-01

Petersen's capture-recapture ratio estimator and the well-known bootstrap estimator are compared across a range of simulated low-intensity simple random sampling with fixed-area plots of 100 m? in a rich wet tropical forest compartment with 93 tree species in the Western Ghats of India. Petersen's ratio estimator was uniformly superior to the bootstrap...

Common Ground between Form and Content: The Pragmatic Solution to the Bootstrapping Problem

ERIC Educational Resources Information Center

Oller, John W.

2005-01-01

The frame of reference for this article is second or foreign language (L2 or FL) acquisition, but the pragmatic bootstrapping hypothesis applies to language processing and acquisition in any context or modality. It is relevant to teaching children to read. It shows how connections between target language surface forms and their content can be made…

The Bacterial Gene IfpA Influences the Potent Induction of Calcitonin Receptor and Osteoclast-Related Genes in Burkholderia Pseudomallei-Induced TRAP-Positive Multinucleated Giant Cells

DTIC Science & Technology

2006-06-13

with arithmetic mean ( UPGMA ) using random tie breaking and uncorrected pairwise distances in MacVector 7.0 (Oxford Molecular). Numbers on branches...denote the UPGMA bootstrap percentage using a highly stringent number (1000) of replications (Felsenstein, 1985). All bootstrap values are 50%, as shown

A Comparison of Single Sample and Bootstrap Methods to Assess Mediation in Cluster Randomized Trials

ERIC Educational Resources Information Center

Pituch, Keenan A.; Stapleton, Laura M.; Kang, Joo Youn

2006-01-01

A Monte Carlo study examined the statistical performance of single sample and bootstrap methods that can be used to test and form confidence interval estimates of indirect effects in two cluster randomized experimental designs. The designs were similar in that they featured random assignment of clusters to one of two treatment conditions and…

Reference interval computation: which method (not) to choose?

PubMed

Pavlov, Igor Y; Wilson, Andrew R; Delgado, Julio C

2012-07-11

When different methods are applied to reference interval (RI) calculation the results can sometimes be substantially different, especially for small reference groups. If there are no reliable RI data available, there is no way to confirm which method generates results closest to the true RI. We randomly drawn samples obtained from a public database for 33 markers. For each sample, RIs were calculated by bootstrapping, parametric, and Box-Cox transformed parametric methods. Results were compared to the values of the population RI. For approximately half of the 33 markers, results of all 3 methods were within 3% of the true reference value. For other markers, parametric results were either unavailable or deviated considerably from the true values. The transformed parametric method was more accurate than bootstrapping for sample size of 60, very close to bootstrapping for sample size 120, but in some cases unavailable. We recommend against using parametric calculations to determine RIs. The transformed parametric method utilizing Box-Cox transformation would be preferable way of RI calculation, if it satisfies normality test. If not, the bootstrapping is always available, and is almost as accurate and precise as the transformed parametric method. Copyright © 2012 Elsevier B.V. All rights reserved.

The sound symbolism bootstrapping hypothesis for language acquisition and language evolution

PubMed Central

Imai, Mutsumi; Kita, Sotaro

2014-01-01

Sound symbolism is a non-arbitrary relationship between speech sounds and meaning. We review evidence that, contrary to the traditional view in linguistics, sound symbolism is an important design feature of language, which affects online processing of language, and most importantly, language acquisition. We propose the sound symbolism bootstrapping hypothesis, claiming that (i) pre-verbal infants are sensitive to sound symbolism, due to a biologically endowed ability to map and integrate multi-modal input, (ii) sound symbolism helps infants gain referential insight for speech sounds, (iii) sound symbolism helps infants and toddlers associate speech sounds with their referents to establish a lexical representation and (iv) sound symbolism helps toddlers learn words by allowing them to focus on referents embedded in a complex scene, alleviating Quine's problem. We further explore the possibility that sound symbolism is deeply related to language evolution, drawing the parallel between historical development of language across generations and ontogenetic development within individuals. Finally, we suggest that sound symbolism bootstrapping is a part of a more general phenomenon of bootstrapping by means of iconic representations, drawing on similarities and close behavioural links between sound symbolism and speech-accompanying iconic gesture. PMID:25092666

Estimating uncertainty in respondent-driven sampling using a tree bootstrap method.

PubMed

Baraff, Aaron J; McCormick, Tyler H; Raftery, Adrian E

2016-12-20

Respondent-driven sampling (RDS) is a network-based form of chain-referral sampling used to estimate attributes of populations that are difficult to access using standard survey tools. Although it has grown quickly in popularity since its introduction, the statistical properties of RDS estimates remain elusive. In particular, the sampling variability of these estimates has been shown to be much higher than previously acknowledged, and even methods designed to account for RDS result in misleadingly narrow confidence intervals. In this paper, we introduce a tree bootstrap method for estimating uncertainty in RDS estimates based on resampling recruitment trees. We use simulations from known social networks to show that the tree bootstrap method not only outperforms existing methods but also captures the high variability of RDS, even in extreme cases with high design effects. We also apply the method to data from injecting drug users in Ukraine. Unlike other methods, the tree bootstrap depends only on the structure of the sampled recruitment trees, not on the attributes being measured on the respondents, so correlations between attributes can be estimated as well as variability. Our results suggest that it is possible to accurately assess the high level of uncertainty inherent in RDS.

A prediction model-based algorithm for computer-assisted database screening of adverse drug reactions in the Netherlands.

PubMed

Scholl, Joep H G; van Hunsel, Florence P A M; Hak, Eelko; van Puijenbroek, Eugène P

2018-02-01

The statistical screening of pharmacovigilance databases containing spontaneously reported adverse drug reactions (ADRs) is mainly based on disproportionality analysis. The aim of this study was to improve the efficiency of full database screening using a prediction model-based approach. A logistic regression-based prediction model containing 5 candidate predictors was developed and internally validated using the Summary of Product Characteristics as the gold standard for the outcome. All drug-ADR associations, with the exception of those related to vaccines, with a minimum of 3 reports formed the training data for the model. Performance was based on the area under the receiver operating characteristic curve (AUC). Results were compared with the current method of database screening based on the number of previously analyzed associations. A total of 25 026 unique drug-ADR associations formed the training data for the model. The final model contained all 5 candidate predictors (number of reports, disproportionality, reports from healthcare professionals, reports from marketing authorization holders, Naranjo score). The AUC for the full model was 0.740 (95% CI; 0.734-0.747). The internal validity was good based on the calibration curve and bootstrapping analysis (AUC after bootstrapping = 0.739). Compared with the old method, the AUC increased from 0.649 to 0.740, and the proportion of potential signals increased by approximately 50% (from 12.3% to 19.4%). A prediction model-based approach can be a useful tool to create priority-based listings for signal detection in databases consisting of spontaneous ADRs. © 2017 The Authors. Pharmacoepidemiology & Drug Safety Published by John Wiley & Sons Ltd.

Population pharmacokinetic modeling and simulation of huperzine A in elderly Chinese subjects

PubMed Central

Sheng, Lei; Qu, Yi; Yan, Jing; Liu, Gang-yi; Wang, Wei-liang; Wang, Yi-jun; Wang, Hong-yi; Zhang, Meng-qi; Lu, Chuan; Liu, Yun; Jia, Jing-yin; Hu, Chao-ying; Li, Xue-ning; Yu, Chen; Xu, Hong-rong

2016-01-01

Aim: Our preliminary results show that huperzine A, an acetylcholinesterase inhibitor used to treat Alzheimer's disease (AD) patients in China, exhibits different pharmacokinetic features in elderly and young healthy subjects. However, its pharmacokinetic data in elderly subjects remains unavailable to date. Thus, we developed a population pharmacokinetic (PPK) model of huperzine A in elderly Chinese people, and identified the covariate affecting its pharmacokinetics for optimal individual administration. Methods: A total of 341 serum huperzine A concentration records was obtained from 2 completed clinical trials (14 elderly healthy subjects in a phase I pharmacokinetic study; 35 elderly AD patients in a phase II study). Population pharmacokinetic analysis was performed using the non-linear mixed-effect modeling software Phoenix NLME1.1.1. The effects of age, gender, body weight, height, creatinine, endogenous creatinine clearance rate as well as drugs administered concomitantly were analyzed. Bootstrap and visual predictive checks were used simultaneously to validate the final population pharmacokinetics models. Results: The plasma concentration-time profile of huperzine A was best described by a one-compartment model with first-order absorption and elimination. Age was identified as the covariate having significant influence on huperzine A clearance. The final PPK model of huperzine A was: CL (L/h)=2.4649*(age/86)(−3.3856), Ka=0.6750 h−1, V (L)=104.216. The final PPK model was demonstrated to be suitable and effective by the bootstrap and visual predictive checks. Conclusion: A PPK model of huperzine A in elderly Chinese subjects is established, which can be used to predict PPK parameters of huperzine A in the treatment of elderly AD patients. PMID:27180987

A new extranodal scoring system based on the prognostically relevant extranodal sites in diffuse large B-cell lymphoma, not otherwise specified treated with chemoimmunotherapy.

PubMed

Hwang, Hee Sang; Yoon, Dok Hyun; Suh, Cheolwon; Huh, Jooryung

2016-08-01

Extranodal involvement is a well-known prognostic factor in patients with diffuse large B-cell lymphomas (DLBCL). Nevertheless, the prognostic impact of the extranodal scoring system included in the conventional international prognostic index (IPI) has been questioned in an era where rituximab treatment has become widespread. We investigated the prognostic impacts of individual sites of extranodal involvement in 761 patients with DLBCL who received rituximab-based chemoimmunotherapy. Subsequently, we established a new extranodal scoring system based on extranodal sites, showing significant prognostic correlation, and compared this system with conventional scoring systems, such as the IPI and the National Comprehensive Cancer Network-IPI (NCCN-IPI). An internal validation procedure, using bootstrapped samples, was also performed for both univariate and multivariate models. Using multivariate analysis with a backward variable selection, we found nine extranodal sites (the liver, lung, spleen, central nervous system, bone marrow, kidney, skin, adrenal glands, and peritoneum) that remained significant for use in the final model. Our newly established extranodal scoring system, based on these sites, was better correlated with patient survival than standard scoring systems, such as the IPI and the NCCN-IPI. Internal validation by bootstrapping demonstrated an improvement in model performance of our modified extranodal scoring system. Our new extranodal scoring system, based on the prognostically relevant sites, may improve the performance of conventional prognostic models of DLBCL in the rituximab era and warrants further external validation using large study populations.

Assessing uncertainties in surface water security: An empirical multimodel approach

NASA Astrophysics Data System (ADS)

Rodrigues, Dulce B. B.; Gupta, Hoshin V.; Mendiondo, Eduardo M.; Oliveira, Paulo Tarso S.

2015-11-01

Various uncertainties are involved in the representation of processes that characterize interactions among societal needs, ecosystem functioning, and hydrological conditions. Here we develop an empirical uncertainty assessment of water security indicators that characterize scarcity and vulnerability, based on a multimodel and resampling framework. We consider several uncertainty sources including those related to (i) observed streamflow data; (ii) hydrological model structure; (iii) residual analysis; (iv) the method for defining Environmental Flow Requirement; (v) the definition of critical conditions for water provision; and (vi) the critical demand imposed by human activities. We estimate the overall hydrological model uncertainty by means of a residual bootstrap resampling approach, and by uncertainty propagation through different methodological arrangements applied to a 291 km2 agricultural basin within the Cantareira water supply system in Brazil. Together, the two-component hydrograph residual analysis and the block bootstrap resampling approach result in a more accurate and precise estimate of the uncertainty (95% confidence intervals) in the simulated time series. We then compare the uncertainty estimates associated with water security indicators using a multimodel framework and the uncertainty estimates provided by each model uncertainty estimation approach. The range of values obtained for the water security indicators suggests that the models/methods are robust and performs well in a range of plausible situations. The method is general and can be easily extended, thereby forming the basis for meaningful support to end-users facing water resource challenges by enabling them to incorporate a viable uncertainty analysis into a robust decision-making process.

How many studies are necessary to compare niche-based models for geographic distributions? Inductive reasoning may fail at the end.

PubMed

Terribile, L C; Diniz-Filho, J A F; De Marco, P

2010-05-01

The use of ecological niche models (ENM) to generate potential geographic distributions of species has rapidly increased in ecology, conservation and evolutionary biology. Many methods are available and the most used are Maximum Entropy Method (MAXENT) and the Genetic Algorithm for Rule Set Production (GARP). Recent studies have shown that MAXENT perform better than GARP. Here we used the statistics methods of ROC - AUC (area under the Receiver Operating Characteristics curve) and bootstrap to evaluate the performance of GARP and MAXENT in generate potential distribution models for 39 species of New World coral snakes. We found that values of AUC for GARP ranged from 0.923 to 0.999, whereas those for MAXENT ranged from 0.877 to 0.999. On the whole, the differences in AUC were very small, but for 10 species GARP outperformed MAXENT. Means and standard deviations for 100 bootstrapped samples with sample sizes ranging from 3 to 30 species did not show any trends towards deviations from a zero difference in AUC values of GARP minus AUC values of MAXENT. Ours results suggest that further studies are still necessary to establish under which circumstances the statistical performance of the methods vary. However, it is also important to consider the possibility that this empirical inductive reasoning may fail in the end, because we almost certainly could not establish all potential scenarios generating variation in the relative performance of models.

A bootstrap lunar base: Preliminary design review 2

NASA Technical Reports Server (NTRS)

1987-01-01

A bootstrap lunar base is the gateway to manned solar system exploration and requires new ideas and new designs on the cutting edge of technology. A preliminary design for a Bootstrap Lunar Base, the second provided by this contractor, is presented. An overview of the work completed is discussed as well as the technical, management, and cost strategies to complete the program requirements. The lunar base design stresses the transforming capabilities of its lander vehicles to aid in base construction. The design also emphasizes modularity and expandability in the base configuration to support the long-term goals of scientific research and profitable lunar resource exploitation. To successfully construct, develop, and inhabit a permanent lunar base, however, several technological advancements must first be realized. Some of these technological advancements are also discussed.

Spheres, charges, instantons, and bootstrap: A five-dimensional odyssey

NASA Astrophysics Data System (ADS)

Chang, Chi-Ming; Fluder, Martin; Lin, Ying-Hsuan; Wang, Yifan

2018-03-01

We combine supersymmetric localization and the conformal bootstrap to study five-dimensional superconformal field theories. To begin, we classify the admissible counter-terms and derive a general relation between the five-sphere partition function and the conformal and flavor central charges. Along the way, we discover a new superconformal anomaly in five dimensions. We then propose a precise triple factorization formula for the five-sphere partition function, that incorporates instantons and is consistent with flavor symmetry enhancement. We numerically evaluate the central charges for the rank-one Seiberg and Morrison-Seiberg theories, and find strong evidence for their saturation of bootstrap bounds, thereby determining the spectra of long multiplets in these theories. Lastly, our results provide new evidence for the F-theorem and possibly a C-theorem in five-dimensional superconformal theories.

Evaluating hypotheses of basal animal phylogeny using complete sequences of large and small subunit rRNA.

PubMed

Medina, M; Collins, A G; Silberman, J D; Sogin, M L

2001-08-14

We studied the evolutionary relationships among basal metazoan lineages by using complete large subunit (LSU) and small subunit (SSU) ribosomal RNA sequences for 23 taxa. After identifying competing hypotheses, we performed maximum likelihood searches for trees conforming to each hypothesis. Kishino-Hasegawa tests were used to determine whether the data (LSU, SSU, and combined) reject any of the competing hypotheses. We also conducted unconstrained tree searches, compared the resulting topologies, and calculated bootstrap indices. Shimodaira-Hasegawa tests were applied to determine whether the data reject any of the topologies resulting from the constrained and unconstrained tree searches. LSU, SSU, and the combined data strongly contradict two assertions pertaining to sponge phylogeny. Hexactinellid sponges are not likely to be the basal lineage of a monophyletic Porifera or the sister group to all other animals. Instead, Hexactinellida and Demospongia form a well-supported clade of siliceous sponges, Silicea. It remains unclear, on the basis of these data alone, whether the calcarean sponges are more closely related to Silicea or to nonsponge animals. The SSU and combined data reject the hypothesis that Bilateria is more closely related to Ctenophora than it is to Cnidaria, whereas LSU data alone do not refute either hypothesis. LSU and SSU data agree in supporting the monophyly of Bilateria, Cnidaria, Ctenophora, and Metazoa. LSU sequence data reveal phylogenetic structure in a data set with limited taxon sampling. Continued accumulation of LSU sequences should increase our understanding of animal phylogeny.

A tree island approach to inferring phylogeny in the ant subfamily Formicinae, with especial reference to the evolution of weaving.

PubMed

Johnson, Rebecca N; Agapow, Paul-Michael; Crozier, Ross H

2003-11-01

The ant subfamily Formicinae is a large assemblage (2458 species (J. Nat. Hist. 29 (1995) 1037), including species that weave leaf nests together with larval silk and in which the metapleural gland-the ancestrally defining ant character-has been secondarily lost. We used sequences from two mitochondrial genes (cytochrome b and cytochrome oxidase 2) from 18 formicine and 4 outgroup taxa to derive a robust phylogeny, employing a search for tree islands using 10000 randomly constructed trees as starting points and deriving a maximum likelihood consensus tree from the ML tree and those not significantly different from it. Non-parametric bootstrapping showed that the ML consensus tree fit the data significantly better than three scenarios based on morphology, with that of Bolton (Identification Guide to the Ant Genera of the World, Harvard University Press, Cambridge, MA) being the best among these alternative trees. Trait mapping showed that weaving had arisen at least four times and possibly been lost once. A maximum likelihood analysis showed that loss of the metapleural gland is significantly associated with the weaver life-pattern. The graph of the frequencies with which trees were discovered versus their likelihood indicates that trees with high likelihoods have much larger basins of attraction than those with lower likelihoods. While this result indicates that single searches are more likely to find high- than low-likelihood tree islands, it also indicates that searching only for the single best tree may lose important information.

Evaluating hypotheses of basal animal phylogeny using complete sequences of large and small subunit rRNA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Medina, Monica; Collins, Allen G.; Silberman, Jeffrey

2001-06-21

We studied the evolutionary relationships among basal metazoan lineages by using complete large subunit (LSU) and small subunit (SSU) ribosomal RNA sequences for 23 taxa. After identifying competing hypotheses, we performed maximum likelihood searches for trees conforming to each hypothesis. Kishino-Hasegawa tests were used to determine whether the data (LSU, SSU, and combined) reject any of the competing hypotheses. We also conducted unconstrained tree searches, compared the resulting topologies, and calculated bootstrap indices. Shimodaira-Hasegawa tests were applied to determine whether the data reject any of the topologies resulting from the constrained and unconstrained tree searches. LSU, SSU, and the combinedmore » data strongly contradict two assertions pertaining to sponge phylogeny. Hexactinellid sponges are not likely to be the basal lineage of amonophyletic Porifera or the sister group to all other animals. Instead, Hexactinellida and Demospongia form a well-supported clade of siliceous sponges, Silicea. It remains unclear, on the basis of these data alone, whether the calcarean sponges are more closely related to Silicea or to nonsponge animals. The SSU and combined data reject the hypothesis that Bilateria is more closely related to Ctenophora than it is to Cnidaria, whereas LSU data alone do not refute either hypothesis. LSU and SSU data agree in supporting the monophyly of Bilateria, Cnidaria, Ctenophora, and Metazoa. LSU sequence data reveal phylogenetic structure in a data set with limited taxon sampling. Continued accumulation of LSU sequences should increase our understanding of animal phylogeny.« less

Non-inductive current generation in fusion plasmas with turbulence

NASA Astrophysics Data System (ADS)

Wang, Weixing; Ethier, S.; Startsev, E.; Chen, J.; Hahm, T. S.; Yoo, M. G.

2017-10-01

It is found that plasma turbulence may strongly influence non-inductive current generation. This may have radical impact on various aspects of tokamak physics. Our simulation study employs a global gyrokinetic model coupling self-consistent neoclassical and turbulent dynamics with focus on electron current. Distinct phases in electron current generation are illustrated in the initial value simulation. In the early phase before turbulence develops, the electron bootstrap current is established in a time scale of a few electron collision times, which closely agrees with the neoclassical prediction. The second phase follows when turbulence begins to saturate, during which turbulent fluctuations are found to strongly affect electron current. The profile structure, amplitude and phase space structure of electron current density are all significantly modified relative to the neoclassical bootstrap current by the presence of turbulence. Both electron parallel acceleration and parallel residual stress drive are shown to play important roles in turbulence-induced current generation. The current density profile is modified in a way that correlates with the fluctuation intensity gradient through its effect on k//-symmetry breaking in fluctuation spectrum. Turbulence is shown to deduct (enhance) plasma self-generated current in low (high) collisionality regime, and the reduction of total electron current relative to the neoclassical bootstrap current increases as collisionality decreases. The implication of this result to the fully non-inductive current operation in steady state burning plasma regime should be investigated. Finally, significant non-inductive current is observed in flat pressure region, which is a nonlocal effect and results from turbulence spreading induced current diffusion. Work supported by U.S. DOE Contract DE-AC02-09-CH11466.

A Step Beyond Simple Keyword Searches: Services Enabled by a Full Content Digital Journal Archive

NASA Technical Reports Server (NTRS)

Boccippio, Dennis J.

2003-01-01

The problems of managing and searching large archives of scientific journal articles can potentially be addressed through data mining and statistical techniques matured primarily for quantitative scientific data analysis. A journal paper could be represented by a multivariate descriptor, e.g., the occurrence counts of a number key technical terms or phrases (keywords), perhaps derived from a controlled vocabulary ( e . g . , the American Meteorological Society's Glossary of Meteorology) or bootstrapped from the journal archive itself. With this technique, conventional statistical classification tools can be leveraged to address challenges faced by both scientists and professional societies in knowledge management. For example, cluster analyses can be used to find bundles of "most-related" papers, and address the issue of journal bifurcation (when is a new journal necessary, and what topics should it encompass). Similarly, neural networks can be trained to predict the optimal journal (within a society's collection) in which a newly submitted paper should be published. Comparable techniques could enable very powerful end-user tools for journal searches, all premised on the view of a paper as a data point in a multidimensional descriptor space, e.g.: "find papers most similar to the one I am reading", "build a personalized subscription service, based on the content of the papers I am interested in, rather than preselected keywords", "find suitable reviewers, based on the content of their own published works", etc. Such services may represent the next "quantum leap" beyond the rudimentary search interfaces currently provided to end-users, as well as a compelling value-added component needed to bridge the print-to-digital-medium gap, and help stabilize professional societies' revenue stream during the print-to-digital transition.

A multi-marker molecular signature approach for treatment-specific subgroup identification with survival outcomes.

PubMed

Li, L; Guennel, T; Marshall, S; Cheung, L W-K

2014-10-01

Delivering on the promise of personalized medicine has become a focus of the pharmaceutical industry as the era of the blockbuster drug is fading. Central to realizing this promise is the need for improved analytical strategies for effectively integrating information across various biological assays (for example, copy number variation and targeted protein expression) toward identification of a treatment-specific subgroup-identifying the right patients. We propose a novel combination of elastic net followed by a maximal χ(2) and semiparametric bootstrap. The combined approaches are presented in a two-stage strategy that estimates patient-specific multi-marker molecular signatures (MMMS) to identify and directly test for a biomarker-driven subgroup with enhanced treatment effect. This flexible strategy provides for incorporation of business-specific needs, such as confining the search space to a subgroup size that is commercially viable, ultimately resulting in actionable information for use in empirically based decision making.

Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3)

NASA Astrophysics Data System (ADS)

Zhang, Yonggen; Schaap, Marcel G.

2017-04-01

Pedotransfer functions (PTFs) have been widely used to predict soil hydraulic parameters in favor of expensive laboratory or field measurements. Rosetta (Schaap et al., 2001, denoted as Rosetta1) is one of many PTFs and is based on artificial neural network (ANN) analysis coupled with the bootstrap re-sampling method which allows the estimation of van Genuchten water retention parameters (van Genuchten, 1980, abbreviated here as VG), saturated hydraulic conductivity (Ks), and their uncertainties. In this study, we present an improved set of hierarchical pedotransfer functions (Rosetta3) that unify the water retention and Ks submodels into one. Parameter uncertainty of the fit of the VG curve to the original retention data is used in the ANN calibration procedure to reduce bias of parameters predicted by the new PTF. One thousand bootstrap replicas were used to calibrate the new models compared to 60 or 100 in Rosetta1, thus allowing the uni-variate and bi-variate probability distributions of predicted parameters to be quantified in greater detail. We determined the optimal weights for VG parameters and Ks, the optimal number of hidden nodes in ANN, and the number of bootstrap replicas required for statistically stable estimates. Results show that matric potential-dependent bias was reduced significantly while root mean square error (RMSE) for water content were reduced modestly; RMSE for Ks was increased by 0.9% (H3w) to 3.3% (H5w) in the new models on log scale of Ks compared with the Rosetta1 model. It was found that estimated distributions of parameters were mildly non-Gaussian and could instead be described rather well with heavy-tailed α-stable distributions. On the other hand, arithmetic means had only a small estimation bias for most textures when compared with the mean-like "shift" parameter of the α-stable distributions. Arithmetic means and (co-)variances are therefore still recommended as summary statistics of the estimated distributions. However, it may be necessary to parameterize the distributions in different ways if the new estimates are used in stochastic analyses of vadose zone flow and transport. Rosetta1 and Posetta3 were implemented in the python programming language, and the source code as well as additional documentation is available at: http://www.cals.arizona.edu/research/rosettav3.html.

CREATION OF A MODEL TO PREDICT SURVIVAL IN PATIENTS WITH REFRACTORY COELIAC DISEASE USING A MULTINATIONAL REGISTRY

PubMed Central

Rubio-Tapia, Alberto; Malamut, Georgia; Verbeek, Wieke H.M.; van Wanrooij, Roy L.J.; Leffler, Daniel A.; Niveloni, Sonia I.; Arguelles-Grande, Carolina; Lahr, Brian D.; Zinsmeister, Alan R.; Murray, Joseph A.; Kelly, Ciaran P.; Bai, Julio C.; Green, Peter H.; Daum, Severin; Mulder, Chris J.J.; Cellier, Christophe

2016-01-01

Background Refractory coeliac disease is a severe complication of coeliac disease with heterogeneous outcome. Aim To create a prognostic model to estimate survival of patients with refractory coeliac disease. Methods We evaluated predictors of 5-year mortality using Cox proportional hazards regression on subjects from a multinational registry. Bootstrap re-sampling was used to internally validate the individual factors and overall model performance. The mean of the estimated regression coefficients from 400 bootstrap models was used to derive a risk score for 5-year mortality. Results The multinational cohort was composed of 232 patients diagnosed with refractory coeliac disease across 7 centers (range of 11–63 cases per center). The median age was 53 years and 150 (64%) were women. A total of 51 subjects died during 5-year follow-up (cumulative 5-year all-cause mortality = 30%). From a multiple variable Cox proportional hazards model, the following variables were significantly associated with 5-year mortality: age at refractory coeliac disease diagnosis (per 20 year increase, hazard ratio = 2.21; 95% confidence interval: 1.38, 3.55), abnormal intraepithelial lymphocytes (hazard ratio = 2.85; 95% confidence interval: 1.22, 6.62), and albumin (per 0.5 unit increase, hazard ratio = 0.72; 95% confidence interval: 0.61, 0.85). A simple weighted 3-factor risk score was created to estimate 5-year survival. Conclusions Using data from a multinational registry and previously-reported risk factors, we create a prognostic model to predict 5-year mortality among patients with refractory coeliac disease. This new model may help clinicians to guide treatment and follow-up. PMID:27485029

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oberije, Cary, E-mail: cary.oberije@maastro.nl; De Ruysscher, Dirk; Universitaire Ziekenhuizen Leuven, KU Leuven

Purpose: Although patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing andmore » validating a model that can provide physicians with a survival probability for an individual NSCLC patient. Methods and Materials: Data from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130). Results: The final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient's survival probability ( (www.predictcancer.org)). The data set can be downloaded at (https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048). Conclusions: The prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system.« less

Creation of a model to predict survival in patients with refractory coeliac disease using a multinational registry.

PubMed

Rubio-Tapia, A; Malamut, G; Verbeek, W H M; van Wanrooij, R L J; Leffler, D A; Niveloni, S I; Arguelles-Grande, C; Lahr, B D; Zinsmeister, A R; Murray, J A; Kelly, C P; Bai, J C; Green, P H; Daum, S; Mulder, C J J; Cellier, C

2016-10-01

Refractory coeliac disease is a severe complication of coeliac disease with heterogeneous outcome. To create a prognostic model to estimate survival of patients with refractory coeliac disease. We evaluated predictors of 5-year mortality using Cox proportional hazards regression on subjects from a multinational registry. Bootstrap resampling was used to internally validate the individual factors and overall model performance. The mean of the estimated regression coefficients from 400 bootstrap models was used to derive a risk score for 5-year mortality. The multinational cohort was composed of 232 patients diagnosed with refractory coeliac disease across seven centres (range of 11-63 cases per centre). The median age was 53 years and 150 (64%) were women. A total of 51 subjects died during a 5-year follow-up (cumulative 5-year all-cause mortality = 30%). From a multiple variable Cox proportional hazards model, the following variables were significantly associated with 5-year mortality: age at refractory coeliac disease diagnosis (per 20 year increase, hazard ratio = 2.21; 95% confidence interval, CI: 1.38-3.55), abnormal intraepithelial lymphocytes (hazard ratio = 2.85; 95% CI: 1.22-6.62), and albumin (per 0.5 unit increase, hazard ratio = 0.72; 95% CI: 0.61-0.85). A simple weighted three-factor risk score was created to estimate 5-year survival. Using data from a multinational registry and previously reported risk factors, we create a prognostic model to predict 5-year mortality among patients with refractory coeliac disease. This new model may help clinicians to guide treatment and follow-up. © 2016 John Wiley & Sons Ltd.

Analytical modeling of gravity changes and crustal deformation at volcanoes: The Long Valley caldera, California, case study

USGS Publications Warehouse

Battaglia, Maurizio; Hill, D.P.

2009-01-01

Joint measurements of ground deformation and micro-gravity changes are an indispensable component for any volcano monitoring strategy. A number of analytical mathematical models are available in the literature that can be used to fit geodetic data and infer source location, depth and density. Bootstrap statistical methods allow estimations of the range of the inferred parameters. Although analytical models often assume that the crust is elastic, homogenous and isotropic, they can take into account different source geometries, the influence of topography, and gravity background noise. The careful use of analytical models, together with high quality data sets, can produce valuable insights into the nature of the deformation/gravity source. Here we present a review of various modeling methods, and use the historical unrest at Long Valley caldera (California) from 1982 to 1999 to illustrate the practical application of analytical modeling and bootstrap to constrain the source of unrest. A key question is whether the unrest at Long Valley since the late 1970s can be explained without calling upon an intrusion of magma. The answer, apparently, is no. Our modeling indicates that the inflation source is a slightly tilted prolate ellipsoid (dip angle between 91?? and 105??) at a depth of 6.5 to 7.9??km beneath the caldera resurgent dome with an aspect ratio between 0.44 and 0.60, a volume change from 0.161 to 0.173??km3 and a density of 1241 to 2093??kg/m3. The larger uncertainty of the density estimate reflects the higher noise of gravity measurements. These results are consistent with the intrusion of silicic magma with a significant amount of volatiles beneath the caldera resurgent dome. ?? 2008 Elsevier B.V.

Assessing Participation in Community-Based Physical Activity Programs in Brazil

PubMed Central

REIS, RODRIGO S.; YAN, YAN; PARRA, DIANA C.; BROWNSON, ROSS C.

2015-01-01

Purpose This study aimed to develop and validate a risk prediction model to examine the characteristics that are associated with participation in community-based physical activity programs in Brazil. Methods We used pooled data from three surveys conducted from 2007 to 2009 in state capitals of Brazil with 6166 adults. A risk prediction model was built considering program participation as an outcome. The predictive accuracy of the model was quantified through discrimination (C statistic) and calibration (Brier score) properties. Bootstrapping methods were used to validate the predictive accuracy of the final model. Results The final model showed sex (women: odds ratio [OR] = 3.18, 95% confidence interval [CI] = 2.14–4.71), having less than high school degree (OR = 1.71, 95% CI = 1.16–2.53), reporting a good health (OR = 1.58, 95% CI = 1.02–2.24) or very good/excellent health (OR = 1.62, 95% CI = 1.05–2.51), having any comorbidity (OR = 1.74, 95% CI = 1.26–2.39), and perceiving the environment as safe to walk at night (OR = 1.59, 95% CI = 1.18–2.15) as predictors of participation in physical activity programs. Accuracy indices were adequate (C index = 0.778, Brier score = 0.031) and similar to those obtained from bootstrapping (C index = 0.792, Brier score = 0.030). Conclusions Sociodemographic and health characteristics as well as perceptions of the environment are strong predictors of participation in community-based programs in selected cities of Brazil. PMID:23846162

Using a Nonparametric Bootstrap to Obtain a Confidence Interval for Pearson's "r" with Cluster Randomized Data: A Case Study

ERIC Educational Resources Information Center

Wagstaff, David A.; Elek, Elvira; Kulis, Stephen; Marsiglia, Flavio

2009-01-01

A nonparametric bootstrap was used to obtain an interval estimate of Pearson's "r," and test the null hypothesis that there was no association between 5th grade students' positive substance use expectancies and their intentions to not use substances. The students were participating in a substance use prevention program in which the unit of…

Bootstrapping a five-loop amplitude using Steinmann relations

DOE PAGES

Caron-Huot, Simon; Dixon, Lance J.; McLeod, Andrew; ...

2016-12-05

Here, the analytic structure of scattering amplitudes is restricted by Steinmann relations, which enforce the vanishing of certain discontinuities of discontinuities. We show that these relations dramatically simplify the function space for the hexagon function bootstrap in planar maximally supersymmetric Yang-Mills theory. Armed with this simplification, along with the constraints of dual conformal symmetry and Regge exponentiation, we obtain the complete five-loop six-particle amplitude.

A Comparison of Kernel Equating and Traditional Equipercentile Equating Methods and the Parametric Bootstrap Methods for Estimating Standard Errors in Equipercentile Equating

ERIC Educational Resources Information Center

Choi, Sae Il

2009-01-01

This study used simulation (a) to compare the kernel equating method to traditional equipercentile equating methods under the equivalent-groups (EG) design and the nonequivalent-groups with anchor test (NEAT) design and (b) to apply the parametric bootstrap method for estimating standard errors of equating. A two-parameter logistic item response…

Comparison of Bootstrapping and Markov Chain Monte Carlo for Copula Analysis of Hydrological Droughts

NASA Astrophysics Data System (ADS)

Yang, P.; Ng, T. L.; Yang, W.

2015-12-01

Effective water resources management depends on the reliable estimation of the uncertainty of drought events. Confidence intervals (CIs) are commonly applied to quantify this uncertainty. A CI seeks to be at the minimal length necessary to cover the true value of the estimated variable with the desired probability. In drought analysis where two or more variables (e.g., duration and severity) are often used to describe a drought, copulas have been found suitable for representing the joint probability behavior of these variables. However, the comprehensive assessment of the parameter uncertainties of copulas of droughts has been largely ignored, and the few studies that have recognized this issue have not explicitly compared the various methods to produce the best CIs. Thus, the objective of this study to compare the CIs generated using two widely applied uncertainty estimation methods, bootstrapping and Markov Chain Monte Carlo (MCMC). To achieve this objective, (1) the marginal distributions lognormal, Gamma, and Generalized Extreme Value, and the copula functions Clayton, Frank, and Plackett are selected to construct joint probability functions of two drought related variables. (2) The resulting joint functions are then fitted to 200 sets of simulated realizations of drought events with known distribution and extreme parameters and (3) from there, using bootstrapping and MCMC, CIs of the parameters are generated and compared. The effect of an informative prior on the CIs generated by MCMC is also evaluated. CIs are produced for different sample sizes (50, 100, and 200) of the simulated drought events for fitting the joint probability functions. Preliminary results assuming lognormal marginal distributions and the Clayton copula function suggest that for cases with small or medium sample sizes (~50-100), MCMC to be superior method if an informative prior exists. Where an informative prior is unavailable, for small sample sizes (~50), both bootstrapping and MCMC yield the same level of performance, and for medium sample sizes (~100), bootstrapping is better. For cases with a large sample size (~200), there is little difference between the CIs generated using bootstrapping and MCMC regardless of whether or not an informative prior exists.

Contribution of Morphological Awareness and Lexical Inferencing Ability to L2 Vocabulary Knowledge and Reading Comprehension among Advanced EFL Learners: Testing Direct and Indirect Effects

ERIC Educational Resources Information Center

Zhang, Dongbo; Koda, Keiko

2012-01-01

Within the Structural Equation Modeling framework, this study tested the direct and indirect effects of morphological awareness and lexical inferencing ability on L2 vocabulary knowledge and reading comprehension among advanced Chinese EFL readers in a university in China. Using both regular z-test and the bootstrapping (data-based resampling)…

WIDELink: A Bootstrapping Approach to Identifying, Modeling and Linking On-Line Data Sources

DTIC Science & Technology

2005-07-01

Mercedes Benz of Laguna Niguel — and collected on the order of 250 records from these sites. We normalized all data by lowercasing it. We then ran the...0.38 > [ mercedes , benz ] [47K] [5 . 0l v8] [ford] [21 , 3Digit] [4 . 3l v8] [mercury] [20 , 3Digit] [3 . 2l 6cyl] [lincoln] [2Digit , 3Digit] [3 . 2l

Landslide susceptibility near highways is increased by one order of magnitude in the Andes of southern Ecuador, Loja province

NASA Astrophysics Data System (ADS)

Brenning, A.; Schwinn, M.; Ruiz-Páez, A. P.; Muenchow, J.

2014-03-01

Mountain roads in developing countries are known to increase landslide occurrence due to often inadequate drainage systems and mechanical destabilization of hillslopes by undercutting and overloading. This study empirically investigates landslide initiation frequency along two paved interurban highways in the tropical Andes of southern Ecuador across different climatic regimes. Generalized additive models (GAM) and generalized linear models (GLM) were used to analyze the relationship between mapped landslide initiation points and distance to highway while accounting for topographic, climatic and geological predictors as possible confounders. A spatial block bootstrap was used to obtain non-parametric confidence intervals for the odds ratio of landslide occurrence near the highways (25 m distance) compared to a 200 m distance. The estimated odds ratio was 18-21 with lower 95% confidence bounds > 13 in all analyses. Spatial bootstrap estimation using the GAM supports the higher odds ratio estimate of 21.2 (95% confidence interval: 15.5-25.3). The highway-related effects were observed to fade at about 150 m distance. Road effects appear to be enhanced in geological units characterized by Holocene gravels and Laramide andesite/basalt. Overall, landslide susceptibility was found to be more than one order of magnitude higher in close proximity to paved interurban highways in the Andes of southern Ecuador.

Landslide susceptibility near highways is increased by 1 order of magnitude in the Andes of southern Ecuador, Loja province

NASA Astrophysics Data System (ADS)

Brenning, A.; Schwinn, M.; Ruiz-Páez, A. P.; Muenchow, J.

2015-01-01

Mountain roads in developing countries are known to increase landslide occurrence due to often inadequate drainage systems and mechanical destabilization of hillslopes by undercutting and overloading. This study empirically investigates landslide initiation frequency along two paved interurban highways in the tropical Andes of southern Ecuador across different climatic regimes. Generalized additive models (GAM) and generalized linear models (GLM) were used to analyze the relationship between mapped landslide initiation points and distance to highway while accounting for topographic, climatic, and geological predictors as possible confounders. A spatial block bootstrap was used to obtain nonparametric confidence intervals for the odds ratio of landslide occurrence near the highways (25 m distance) compared to a 200 m distance. The estimated odds ratio was 18-21, with lower 95% confidence bounds >13 in all analyses. Spatial bootstrap estimation using the GAM supports the higher odds ratio estimate of 21.2 (95% confidence interval: 15.5-25.3). The highway-related effects were observed to fade at about 150 m distance. Road effects appear to be enhanced in geological units characterized by Holocene gravels and Laramide andesite/basalt. Overall, landslide susceptibility was found to be more than 1 order of magnitude higher in close proximity to paved interurban highways in the Andes of southern Ecuador.

Advances in the high bootstrap fraction regime on DIII-D towards the Q = 5 mission of ITER steady state

DOE PAGES

Qian, Jinping P.; Garofalo, Andrea M.; Gong, Xianzu Z.; ...

2017-03-20

Recent EAST/DIII-D joint experiments on the high poloidal betamore » $${{\\beta}_{\\text{P}}}$$ regime in DIII-D have extended operation with internal transport barriers (ITBs) and excellent energy confinement (H 98y2 ~ 1.6) to higher plasma current, for lower q 95 ≤ 7.0, and more balanced neutral beam injection (NBI) (torque injection < 2 Nm), for lower plasma rotation than previous results. Transport analysis and experimental measurements at low toroidal rotation suggest that the E × B shear effect is not key to the ITB formation in these high $${{\\beta}_{\\text{P}}}$$ discharges. Experiments and TGLF modeling show that the Shafranov shift has a key stabilizing effect on turbulence. Extrapolation of the DIII-D results using a 0D model shows that with the improved confinement, the high bootstrap fraction regime could achieve fusion gain Q = 5 in ITER at $${{\\beta}_{\\text{N}}}$$ ~ 2.9 and q 95 ~ 7. With the optimization of q(0), the required improved confinement is achievable when using 1.5D TGLF-SAT1 for transport simulations. Furthermore, results reported in this paper suggest that the DIII-D high $${{\\beta}_{\\text{P}}}$$ scenario could be a candidate for ITER steady state operation.« less

Soft-tissue anatomy of the extant hominoids: a review and phylogenetic analysis.

PubMed

Gibbs, S; Collard, M; Wood, B

2002-01-01

This paper reports the results of a literature search for information about the soft-tissue anatomy of the extant non-human hominoid genera, Pan, Gorilla, Pongo and Hylobates, together with the results of a phylogenetic analysis of these data plus comparable data for Homo. Information on the four extant non-human hominoid genera was located for 240 out of the 1783 soft-tissue structures listed in the Nomina Anatomica. Numerically these data are biased so that information about some systems (e.g. muscles) and some regions (e.g. the forelimb) are over-represented, whereas other systems and regions (e.g. the veins and the lymphatics of the vascular system, the head region) are either under-represented or not represented at all. Screening to ensure that the data were suitable for use in a phylogenetic analysis reduced the number of eligible soft-tissue structures to 171. These data, together with comparable data for modern humans, were converted into discontinuous character states suitable for phylogenetic analysis and then used to construct a taxon-by-character matrix. This matrix was used in two tests of the hypothesis that soft-tissue characters can be relied upon to reconstruct hominoid phylogenetic relationships. In the first, parsimony analysis was used to identify cladograms requiring the smallest number of character state changes. In the second, the phylogenetic bootstrap was used to determine the confidence intervals of the most parsimonious clades. The parsimony analysis yielded a single most parsimonious cladogram that matched the molecular cladogram. Similarly the bootstrap analysis yielded clades that were compatible with the molecular cladogram; a (Homo, Pan) clade was supported by 95% of the replicates, and a (Gorilla, Pan, Homo) clade by 96%. These are the first hominoid morphological data to provide statistically significant support for the clades favoured by the molecular evidence.

Stability and uncertainty of finite-fault slip inversions: Application to the 2004 Parkfield, California, earthquake

USGS Publications Warehouse

Hartzell, S.; Liu, P.; Mendoza, C.; Ji, C.; Larson, K.M.

2007-01-01

The 2004 Parkfield, California, earthquake is used to investigate stability and uncertainty aspects of the finite-fault slip inversion problem with different a priori model assumptions. We utilize records from 54 strong ground motion stations and 13 continuous, 1-Hz sampled, geodetic instruments. Two inversion procedures are compared: a linear least-squares subfault-based methodology and a nonlinear global search algorithm. These two methods encompass a wide range of the different approaches that have been used to solve the finite-fault slip inversion problem. For the Parkfield earthquake and the inversion of velocity or displacement waveforms, near-surface related site response (top 100 m, frequencies above 1 Hz) is shown to not significantly affect the solution. Results are also insensitive to selection of slip rate functions with similar duration and to subfault size if proper stabilizing constraints are used. The linear and nonlinear formulations yield consistent results when the same limitations in model parameters are in place and the same inversion norm is used. However, the solution is sensitive to the choice of inversion norm, the bounds on model parameters, such as rake and rupture velocity, and the size of the model fault plane. The geodetic data set for Parkfield gives a slip distribution different from that of the strong-motion data, which may be due to the spatial limitation of the geodetic stations and the bandlimited nature of the strong-motion data. Cross validation and the bootstrap method are used to set limits on the upper bound for rupture velocity and to derive mean slip models and standard deviations in model parameters. This analysis shows that slip on the northwestern half of the Parkfield rupture plane from the inversion of strong-motion data is model dependent and has a greater uncertainty than slip near the hypocenter.

The ABC (in any D) of logarithmic CFT

NASA Astrophysics Data System (ADS)

Hogervorst, Matthijs; Paulos, Miguel; Vichi, Alessandro

2017-10-01

Logarithmic conformal field theories have a vast range of applications, from critical percolation to systems with quenched disorder. In this paper we thoroughly examine the structure of these theories based on their symmetry properties. Our analysis is model-independent and holds for any spacetime dimension. Our results include a determination of the general form of correlation functions and conformal block decompositions, clearing the path for future bootstrap applications. Several examples are discussed in detail, including logarithmic generalized free fields, holographic models, self-avoiding random walks and critical percolation.

Unitary subsector of generalized minimal models

NASA Astrophysics Data System (ADS)

Behan, Connor

2018-05-01

We revisit the line of nonunitary theories that interpolate between the Virasoro minimal models. Numerical bootstrap applications have brought about interest in the four-point function involving the scalar primary of lowest dimension. Using recent progress in harmonic analysis on the conformal group, we prove the conjecture that global conformal blocks in this correlator appear with positive coefficients. We also compute many such coefficients in the simplest mixed correlator system. Finally, we comment on the status of using global conformal blocks to isolate the truly unitary points on this line.

Statistical significance of the rich-club phenomenon in complex networks

NASA Astrophysics Data System (ADS)

Jiang, Zhi-Qiang; Zhou, Wei-Xing

2008-04-01

We propose that the rich-club phenomenon in complex networks should be defined in the spirit of bootstrapping, in which a null model is adopted to assess the statistical significance of the rich-club detected. Our method can serve as a definition of the rich-club phenomenon and is applied to analyze three real networks and three model networks. The results show significant improvement compared with previously reported results. We report a dilemma with an exceptional example, showing that there does not exist an omnipotent definition for the rich-club phenomenon.

Statistical inference based on the nonparametric maximum likelihood estimator under double-truncation.

PubMed

Emura, Takeshi; Konno, Yoshihiko; Michimae, Hirofumi

2015-07-01

Doubly truncated data consist of samples whose observed values fall between the right- and left- truncation limits. With such samples, the distribution function of interest is estimated using the nonparametric maximum likelihood estimator (NPMLE) that is obtained through a self-consistency algorithm. Owing to the complicated asymptotic distribution of the NPMLE, the bootstrap method has been suggested for statistical inference. This paper proposes a closed-form estimator for the asymptotic covariance function of the NPMLE, which is computationally attractive alternative to bootstrapping. Furthermore, we develop various statistical inference procedures, such as confidence interval, goodness-of-fit tests, and confidence bands to demonstrate the usefulness of the proposed covariance estimator. Simulations are performed to compare the proposed method with both the bootstrap and jackknife methods. The methods are illustrated using the childhood cancer dataset.

Heptagons from the Steinmann cluster bootstrap

DOE PAGES

Dixon, Lance J.; Drummond, James; Harrington, Thomas; ...

2017-02-28

We reformulate the heptagon cluster bootstrap to take advantage of the Steinmann relations, which require certain double discontinuities of any amplitude to vanish. These constraints vastly reduce the number of functions needed to bootstrap seven-point amplitudes in planarmore » $$ \\mathcal{N} $$ = 4 supersymmetric Yang-Mills theory, making higher-loop contributions to these amplitudes more computationally accessible. In particular, dual superconformal symmetry and well-defined collinear limits suffice to determine uniquely the symbols of the three-loop NMHV and four-loop MHV seven-point amplitudes. We also show that at three loops, relaxing the dual superconformal $$\\bar{Q}$$ relations and imposing dihedral symmetry (and for NMHV the absence of spurious poles) leaves only a single ambiguity in the heptagon amplitudes. These results point to a strong tension between the collinear properties of the amplitudes and the Steinmann relations.« less

Imaging with New Classic and Vision at the NPOI

NASA Astrophysics Data System (ADS)

Jorgensen, Anders

2018-04-01

The Navy Precision Optical Interferometer (NPOI) is unique among interferometric observatories for its ability to position telescopes in an equally-spaced array configuration. This configuration is optimal for interferometric imaging because it allows the use of bootstrapping to track fringes on long baselines with signal-to-noise ratio less than one. When combined with coherent integration techniques this can produce visibilities with acceptable SNR on baselines long enough to resolve features on the surfaces of stars. The stellar surface imaging project at NPOI combines the bootstrapping array configuration of the NPOI array, real-time fringe tracking, baseline- and wavelength bootstrapping with Earth rotation to provide dense coverage in the UV plane at a wide range of spatial frequencies. In this presentation, we provide an overview of the project and an update of the latest status and results from the project.

Bootstrapping and Maintaining Trust in the Cloud

DTIC Science & Technology

2016-12-01

proliferation and popularity of infrastructure-as-a- service (IaaS) cloud computing services such as Amazon Web Services and Google Compute Engine means...IaaS trusted computing system: • Secure Bootstrapping – the system should enable the tenant to securely install an initial root secret into each cloud ...elastically instantiated and terminated. Prior cloud trusted computing solutions address a subset of these features, but none achieve all. Excalibur [31] sup

Sample Reuse in Statistical Remodeling.

DTIC Science & Technology

1987-08-01

as the jackknife and bootstrap, is an expansion of the functional, T(Fn), or of its distribution function or both. Frangos and Schucany (1987a) used...accelerated bootstrap. In the same report Frangos and Schucany demonstrated the small sample superiority of that approach over the proposals that take...higher order terms of an Edgeworth expansion into account. In a second report Frangos and Schucany (1987b) examined the small sample performance of

Innovation cascades: artefacts, organization and attributions

PubMed Central

2016-01-01

Innovation cascades inextricably link the introduction of new artefacts, transformations in social organization, and the emergence of new functionalities and new needs. This paper describes a positive feedback dynamic, exaptive bootstrapping, through which these cascades proceed, and the characteristics of the relationships in which the new attributions that drive this dynamic are generated. It concludes by arguing that the exaptive bootstrapping dynamic is the principal driver of our current Innovation Society. PMID:26926284

Bootstrapping Development of a Cloud-Based Spoken Dialog System in the Educational Domain from Scratch Using Crowdsourced Data. Research Report. ETS RR-16-16

ERIC Educational Resources Information Center

Ramanarayanan, Vikram; Suendermann-Oeft, David; Lange, Patrick; Ivanov, Alexei V.; Evanini, Keelan; Yu, Zhou; Tsuprun, Eugene; Qian, Yao

2016-01-01

We propose a crowdsourcing-based framework to iteratively and rapidly bootstrap a dialog system from scratch for a new domain. We leverage the open-source modular HALEF dialog system to deploy dialog applications. We illustrate the usefulness of this framework using four different prototype dialog items with applications in the educational domain…

The sound symbolism bootstrapping hypothesis for language acquisition and language evolution.

PubMed

Imai, Mutsumi; Kita, Sotaro

2014-09-19

Sound symbolism is a non-arbitrary relationship between speech sounds and meaning. We review evidence that, contrary to the traditional view in linguistics, sound symbolism is an important design feature of language, which affects online processing of language, and most importantly, language acquisition. We propose the sound symbolism bootstrapping hypothesis, claiming that (i) pre-verbal infants are sensitive to sound symbolism, due to a biologically endowed ability to map and integrate multi-modal input, (ii) sound symbolism helps infants gain referential insight for speech sounds, (iii) sound symbolism helps infants and toddlers associate speech sounds with their referents to establish a lexical representation and (iv) sound symbolism helps toddlers learn words by allowing them to focus on referents embedded in a complex scene, alleviating Quine's problem. We further explore the possibility that sound symbolism is deeply related to language evolution, drawing the parallel between historical development of language across generations and ontogenetic development within individuals. Finally, we suggest that sound symbolism bootstrapping is a part of a more general phenomenon of bootstrapping by means of iconic representations, drawing on similarities and close behavioural links between sound symbolism and speech-accompanying iconic gesture. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Advancing theory development: exploring the leadership-climate relationship as a mechanism of the implementation of cultural competence.

PubMed

Guerrero, Erick G; Fenwick, Karissa; Kong, Yinfei

2017-11-14

Leadership style and specific organizational climates have emerged as critical mechanisms to implement targeted practices in organizations. Drawing from relevant theories, we propose that climate for implementation of cultural competence reflects how transformational leadership may enhance the organizational implementation of culturally responsive practices in health care organizations. Using multilevel data from 427 employees embedded in 112 addiction treatment programs collected in 2013, confirmatory factor analysis showed adequate fit statistics for our measure of climate for implementation of cultural competence (Cronbach's alpha = .88) and three outcomes: knowledge (Cronbach's alpha = .88), services (Cronbach's alpha = .86), and personnel (Cronbach's alpha = .86) practices. Results from multilevel path analyses indicate a positive relationship between employee perceptions of transformational leadership and climate for implementation of cultural competence (standardized indirect effect = .057, bootstrap p < .001). We also found a positive indirect effect between transformational leadership and each of the culturally competent practices: knowledge (standardized indirect effect = .006, bootstrap p = .004), services (standardized indirect effect = .019, bootstrap p < .001), and personnel (standardized indirect effect = .014, bootstrap p = .005). Findings contribute to implementation science. They build on leadership theory and offer evidence of the mediating role of climate in the implementation of cultural competence in addiction health service organizations.

Non-parametric methods for cost-effectiveness analysis: the central limit theorem and the bootstrap compared.

PubMed

Nixon, Richard M; Wonderling, David; Grieve, Richard D

2010-03-01

Cost-effectiveness analyses (CEA) alongside randomised controlled trials commonly estimate incremental net benefits (INB), with 95% confidence intervals, and compute cost-effectiveness acceptability curves and confidence ellipses. Two alternative non-parametric methods for estimating INB are to apply the central limit theorem (CLT) or to use the non-parametric bootstrap method, although it is unclear which method is preferable. This paper describes the statistical rationale underlying each of these methods and illustrates their application with a trial-based CEA. It compares the sampling uncertainty from using either technique in a Monte Carlo simulation. The experiments are repeated varying the sample size and the skewness of costs in the population. The results showed that, even when data were highly skewed, both methods accurately estimated the true standard errors (SEs) when sample sizes were moderate to large (n>50), and also gave good estimates for small data sets with low skewness. However, when sample sizes were relatively small and the data highly skewed, using the CLT rather than the bootstrap led to slightly more accurate SEs. We conclude that while in general using either method is appropriate, the CLT is easier to implement, and provides SEs that are at least as accurate as the bootstrap. (c) 2009 John Wiley & Sons, Ltd.

Risk-adjusted econometric model to estimate postoperative costs: an additional instrument for monitoring performance after major lung resection.

PubMed

Brunelli, Alessandro; Salati, Michele; Refai, Majed; Xiumé, Francesco; Rocco, Gaetano; Sabbatini, Armando

2007-09-01

The objectives of this study were to develop a risk-adjusted model to estimate individual postoperative costs after major lung resection and to use it for internal economic audit. Variable and fixed hospital costs were collected for 679 consecutive patients who underwent major lung resection from January 2000 through October 2006 at our unit. Several preoperative variables were used to develop a risk-adjusted econometric model from all patients operated on during the period 2000 through 2003 by a stepwise multiple regression analysis (validated by bootstrap). The model was then used to estimate the postoperative costs in the patients operated on during the 3 subsequent periods (years 2004, 2005, and 2006). Observed and predicted costs were then compared within each period by the Wilcoxon signed rank test. Multiple regression and bootstrap analysis yielded the following model predicting postoperative cost: 11,078 + 1340.3X (age > 70 years) + 1927.8X cardiac comorbidity - 95X ppoFEV1%. No differences between predicted and observed costs were noted in the first 2 periods analyzed (year 2004, $6188.40 vs $6241.40, P = .3; year 2005, $6308.60 vs $6483.60, P = .4), whereas in the most recent period (2006) observed costs were significantly lower than the predicted ones ($3457.30 vs $6162.70, P < .0001). Greater precision in predicting outcome and costs after therapy may assist clinicians in the optimization of clinical pathways and allocation of resources. Our economic model may be used as a methodologic template for economic audit in our specialty and complement more traditional outcome measures in the assessment of performance.

Development of a prognostic nomogram for cirrhotic patients with upper gastrointestinal bleeding.

PubMed

Zhou, Yu-Jie; Zheng, Ji-Na; Zhou, Yi-Fan; Han, Yi-Jing; Zou, Tian-Tian; Liu, Wen-Yue; Braddock, Martin; Shi, Ke-Qing; Wang, Xiao-Dong; Zheng, Ming-Hua

2017-10-01

Upper gastrointestinal bleeding (UGIB) is a complication with a high mortality rate in critically ill patients presenting with cirrhosis. Today, there exist few accurate scoring models specifically designed for mortality risk assessment in critically ill cirrhotic patients with upper gastrointestinal bleeding (CICGIB). Our aim was to develop and evaluate a novel nomogram-based model specific for CICGIB. Overall, 540 consecutive CICGIB patients were enrolled. On the basis of Cox regression analyses, the nomogram was constructed to estimate the probability of 30-day, 90-day, 270-day, and 1-year survival. An upper gastrointestinal bleeding-chronic liver failure-sequential organ failure assessment (UGIB-CLIF-SOFA) score was derived from the nomogram. Performance assessment and internal validation of the model were performed using Harrell's concordance index (C-index), calibration plot, and bootstrap sample procedures. UGIB-CLIF-SOFA was also compared with other prognostic models, such as CLIF-SOFA and model for end-stage liver disease, using C-indices. Eight independent factors derived from Cox analysis (including bilirubin, creatinine, international normalized ratio, sodium, albumin, mean artery pressure, vasopressin used, and hematocrit decrease>10%) were assembled into the nomogram and the UGIB-CLIF-SOFA score. The calibration plots showed optimal agreement between nomogram prediction and actual observation. The C-index of the nomogram using bootstrap (0.729; 95% confidence interval: 0.689-0.766) was higher than that of the other models for predicting survival of CICGIB. We have developed and internally validated a novel nomogram and an easy-to-use scoring system that accurately predicts the mortality probability of CICGIB on the basis of eight easy-to-obtain parameters. External validation is now warranted in future clinical studies.

Self-determined motivation in sport predicts anti-doping motivation and intention: a perspective from the trans-contextual model.

PubMed

Chan, D K C; Dimmock, J A; Donovan, R J; Hardcastle, S; Lentillon-Kaestner, V; Hagger, M S

2015-05-01

Motivation in sport has been frequently identified as a key factor of young athletes' intention of doping in sport, but there has not been any attempt in scrutinizing the motivational mechanism involved. The present study applied the trans-contextual model of motivation to explain the relationship between motivation in a sport context and motivation and the social-cognitive factors (attitude, subjective norm, perceived behavioral control, and intention) from the theory of planned behavior (TPB) in an anti-doping context. A cross-sectional survey was conducted. Questionnaire data was collected from 410 elite and sub-elite young athletes in Australia (Mean age [17.7±3.9 yr], 55.4% male, Years in sport [9.1±3.2]). We measured the key model variables of study in relation to sport motivation (Behavioral Regulation in Sport Questionnaire), and the motivation (adapted version of the Treatment Self-Regulation Questionnaire) and social cognitive patterns (the theory of planned behavior questionnaire) of doping avoidance. The data was analyzed by variance-based structural equation modeling with bootstrapping of 999 replications. The goodness-of-fit of the hypothesized model was acceptable. The bootstrapped parameter estimates revealed that autonomous motivation and amotivation in sport were positively associated with the corresponding types of motivation for the avoidance of doping. Autonomous motivation, subjective norm, and perceived behavioral control in doping avoidance fully mediated the relationship between autonomous motivation in sport and intention for doping avoidance. The findings support the tenets of the trans-contextual model, and explain how motivation in sport is related to athletes' motivation and intention with respect to anti-doping behaviors. Copyright © 2014 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

An algebraic approach to the analytic bootstrap

DOE PAGES

Alday, Luis F.; Zhiboedov, Alexander

2017-04-27

We develop an algebraic approach to the analytic bootstrap in CFTs. By acting with the Casimir operator on the crossing equation we map the problem of doing large spin sums to any desired order to the problem of solving a set of recursion relations. We compute corrections to the anomalous dimension of large spin operators due to the exchange of a primary and its descendants in the crossed channel and show that this leads to a Borel-summable expansion. Here, we analyse higher order corrections to the microscopic CFT data in the direct channel and its matching to infinite towers ofmore » operators in the crossed channel. We apply this method to the critical O(N ) model. At large N we reproduce the first few terms in the large spin expansion of the known two-loop anomalous dimensions of higher spin currents in the traceless symmetric representation of O(N ) and make further predictions. At small N we present the results for the truncated large spin expansion series of anomalous dimensions of higher spin currents.« less

Progress toward steady-state tokamak operation exploiting the high bootstrap current fraction regime

DOE PAGES

Ren, Q. L.; Garofalo, A. M.; Gong, X. Z.; ...

2016-06-20

Recent DIII-D experiments have increased the normalized fusion performance of the high bootstrap current fraction tokamak regime toward reactor-relevant steady state operation. The experiments, conducted by a joint team of researchers from the DIII-D and EAST tokamaks, developed a fully noninductive scenario that could be extended on EAST to a demonstration of long pulse steady-state tokamak operation. Improved understanding of scenario stability has led to the achievement of very high values of β p and β N despite strong ITBs. Good confinement has been achieved with reduced toroidal rotation. These high β p plasmas challenge the energy transport understanding, especiallymore » in the electron energy channel. A new turbulent transport model, named 2 TGLF-SAT1, has been developed which improves the transport prediction. Experiments extending results to long pulse on EAST, based on the physics basis developed at DIII-D, have been conducted. Finally, more investigations will be carried out on EAST with more additional auxiliary power to come online in the near term.« less

Estimating source parameters from deformation data, with an application to the March 1997 earthquake swarm off the Izu Peninsula, Japan

NASA Astrophysics Data System (ADS)

Cervelli, P.; Murray, M. H.; Segall, P.; Aoki, Y.; Kato, T.

2001-06-01

We have applied two Monte Carlo optimization techniques, simulated annealing and random cost, to the inversion of deformation data for fault and magma chamber geometry. These techniques involve an element of randomness that permits them to escape local minima and ultimately converge to the global minimum of misfit space. We have tested the Monte Carlo algorithms on two synthetic data sets. We have also compared them to one another in terms of their efficiency and reliability. We have applied the bootstrap method to estimate confidence intervals for the source parameters, including the correlations inherent in the data. Additionally, we present methods that use the information from the bootstrapping procedure to visualize the correlations between the different model parameters. We have applied these techniques to GPS, tilt, and leveling data from the March 1997 earthquake swarm off of the Izu Peninsula, Japan. Using the two Monte Carlo algorithms, we have inferred two sources, a dike and a fault, that fit the deformation data and the patterns of seismicity and that are consistent with the regional stress field.

Proceedings of the Conference on the Design of Experiments in Army Research, Development and Testing (29th)

DTIC Science & Technology

1984-06-01

SEQUENTIAL TESTING (Bldg. A, Room C) 1300-1330 ’ 1330-1415 1415-1445 1445-1515 BREAK 1515-1545 A TRUNCATED SEQUENTIAL PROBABILITY RATIO TEST J...suicide optical data operational testing reliability random numbers bootstrap methods missing data sequential testing fire support complex computer model carcinogenesis studies EUITION Of 1 NOV 68 I% OBSOLETE a ...contributed papers can be ascertained from the titles of the

How to Select a Good Training-data Subset for Transcription: Submodular Active Selection for Sequences

DTIC Science & Technology

2009-01-01

selection and uncertainty sampling signif- icantly. Index Terms: Transcription, labeling, submodularity, submod- ular selection, active learning , sequence...name of batch active learning , where a subset of data that is most informative and represen- tative of the whole is selected for labeling. Often...representative subset. Note that our Fisher ker- nel is over an unsupervised generative model, which enables us to bootstrap our active learning approach

Bootstrapping and Maintaining Trust in the Cloud

DTIC Science & Technology

2016-03-16

of infrastructure-as-a- service (IaaS) cloud computing services such as Ama- zon Web Services, Google Compute Engine, Rackspace, et. al. means that...Implementation We implemented keylime in ∼3.2k lines of Python in four components: registrar, node, CV, and tenant. The registrar offers a REST-based web ...bootstrap key K. It provides an unencrypted REST-based web service for these two functions. As described earlier, the pro- tocols for exchanging data

Reduced Power Laer Designation Systems

DTIC Science & Technology

2008-06-20

200KD, Ri = = 60Kfl, and R 2 = R4 = 2K yields an overall transimpedance gain of 200K x 30 x 30 = 180MV/A. Figure 3. Three stage photodiode amplifier ...transistor circuit for bootstrap buffering of the input stage, comparing the noise performance of the candidate amplifier designs, selecting the two...transistor bootstrap design as the circuit of choice, and comparing the performance of this circuit against that of a basic transconductance amplifier

Causality constraints in conformal field theory

DOE PAGES

Hartman, Thomas; Jain, Sachin; Kundu, Sandipan

2016-05-17

Causality places nontrivial constraints on QFT in Lorentzian signature, for example fixing the signs of certain terms in the low energy Lagrangian. In d dimensional conformal field theory, we show how such constraints are encoded in crossing symmetry of Euclidean correlators, and derive analogous constraints directly from the conformal bootstrap (analytically). The bootstrap setup is a Lorentzian four-point function corresponding to propagation through a shockwave. Crossing symmetry fixes the signs of certain log terms that appear in the conformal block expansion, which constrains the interactions of low-lying operators. As an application, we use the bootstrap to rederive the well knownmore » sign constraint on the (Φ) 4 coupling in effective field theory, from a dual CFT. We also find constraints on theories with higher spin conserved currents. As a result, our analysis is restricted to scalar correlators, but we argue that similar methods should also impose nontrivial constraints on the interactions of spinning operators« less

Bootstrapping the energy flow in the beginning of life.

PubMed

Hengeveld, R; Fedonkin, M A

2007-01-01

This paper suggests that the energy flow on which all living structures depend only started up slowly, the low-energy, initial phase starting up a second, slightly more energetic phase, and so on. In this way, the build up of the energy flow follows a bootstrapping process similar to that found in the development of computers, the first generation making possible the calculations necessary for constructing the second one, etc. In the biogenetic upstart of an energy flow, non-metals in the lower periods of the Periodic Table of Elements would have constituted the most primitive systems, their operation being enhanced and later supplanted by elements in the higher periods that demand more energy. This bootstrapping process would put the development of the metabolisms based on the second period elements carbon, nitrogen and oxygen at the end of the evolutionary process rather than at, or even before, the biogenetic event.

Three-dimensional quantitative structure-activity relationship studies on novel series of benzotriazine based compounds acting as Src inhibitors using CoMFA and CoMSIA.

PubMed

Gueto, Carlos; Ruiz, José L; Torres, Juan E; Méndez, Jefferson; Vivas-Reyes, Ricardo

2008-03-01

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of benzotriazine derivatives, as Src inhibitors. Ligand molecular superimposition on the template structure was performed by database alignment method. The statistically significant model was established of 72 molecules, which were validated by a test set of six compounds. The CoMFA model yielded a q(2)=0.526, non cross-validated R(2) of 0.781, F value of 88.132, bootstrapped R(2) of 0.831, standard error of prediction=0.587, and standard error of estimate=0.351 while the CoMSIA model yielded the best predictive model with a q(2)=0.647, non cross-validated R(2) of 0.895, F value of 115.906, bootstrapped R(2) of 0.953, standard error of prediction=0.519, and standard error of estimate=0.178. The contour maps obtained from 3D-QSAR studies were appraised for activity trends for the molecules analyzed. Results indicate that small steric volumes in the hydrophobic region, electron-withdrawing groups next to the aryl linker region, and atoms close to the solvent accessible region increase the Src inhibitory activity of the compounds. In fact, adding substituents at positions 5, 6, and 8 of the benzotriazine nucleus were generated new compounds having a higher predicted activity. The data generated from the present study will further help to design novel, potent, and selective Src inhibitors as anticancer therapeutic agents.

A Web-based nomogram predicting para-aortic nodal metastasis in incompletely staged patients with endometrial cancer: a Korean Multicenter Study.

PubMed

Kang, Sokbom; Lee, Jong-Min; Lee, Jae-Kwan; Kim, Jae-Weon; Cho, Chi-Heum; Kim, Seok-Mo; Park, Sang-Yoon; Park, Chan-Yong; Kim, Ki-Tae

2014-03-01

The purpose of this study is to develop a Web-based nomogram for predicting the individualized risk of para-aortic nodal metastasis in incompletely staged patients with endometrial cancer. From 8 institutions, the medical records of 397 patients who underwent pelvic and para-aortic lymphadenectomy as a surgical staging procedure were retrospectively reviewed. A multivariate logistic regression model was created and internally validated by rigorous bootstrap resampling methods. Finally, the model was transformed into a user-friendly Web-based nomogram (http://http://www.kgog.org/nomogram/empa001.html). The rate of para-aortic nodal metastasis was 14.4% (57/397 patients). Using a stepwise variable selection, 4 variables including deep myometrial invasion, non-endometrioid subtype, lymphovascular space invasion, and log-transformed CA-125 levels were finally adopted. After 1000 repetitions of bootstrapping, all of these 4 variables retained a significant association with para-aortic nodal metastasis in the multivariate analysis-deep myometrial invasion (P = 0.001), non-endometrioid histologic subtype (P = 0.034), lymphovascular space invasion (P = 0.003), and log-transformed serum CA-125 levels (P = 0.004). The model showed good discrimination (C statistics = 0.87; 95% confidence interval, 0.82-0.92) and accurate calibration (Hosmer-Lemeshow P = 0.74). This nomogram showed good performance in predicting para-aortic metastasis in patients with endometrial cancer. The tool may be useful in determining the extent of lymphadenectomy after incomplete surgery.

Scoring system to guide decision making for the use of gentamicin-impregnated collagen sponge to prevent deep sternal wound infection.

PubMed

Benedetto, Umberto; Raja, Shahzad G

2014-11-01

The effectiveness of the routine retrosternal placement of a gentamicin-impregnated collagen sponge (GICS) implant before sternotomy closure is currently a matter of some controversy. We aimed to develop a scoring system to guide decision making for the use of GICS to prevent deep sternal wound infection. Fast backward elimination on predictors, including GICS, was performed using the Lawless and Singhal method. The scoring system was reported as a partial nomogram that can be used to manually obtain predicted individual risk of deep sternal wound infection from the regression model. Bootstrapping validation of the regression models was performed. The final populations consisted of 8750 adult patients undergoing cardiac surgery through full sternotomy during the study period. A total of 329 patients (3.8%) received GICS implant. The overall incidence of deep sternal wound infection was lower among patients who received GICS implant (0.6%) than patients who did not (2.01%) (P=.02). A nomogram to predict the individual risk for deep sternal wound infection was developed that included the use of GICS. Bootstrapping validation confirmed a good discriminative power of the models. The scoring system provides an impartial assessment of the decision-making process for clinicians to establish if GICS implant is effective in reducing the risk for deep sternal wound infection in individual patients undergoing cardiac surgery through full sternotomy. Copyright © 2014 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Assessment of resampling methods for causality testing: A note on the US inflation behavior

PubMed Central

Kyrtsou, Catherine; Kugiumtzis, Dimitris; Diks, Cees

2017-01-01

Different resampling methods for the null hypothesis of no Granger causality are assessed in the setting of multivariate time series, taking into account that the driving-response coupling is conditioned on the other observed variables. As appropriate test statistic for this setting, the partial transfer entropy (PTE), an information and model-free measure, is used. Two resampling techniques, time-shifted surrogates and the stationary bootstrap, are combined with three independence settings (giving a total of six resampling methods), all approximating the null hypothesis of no Granger causality. In these three settings, the level of dependence is changed, while the conditioning variables remain intact. The empirical null distribution of the PTE, as the surrogate and bootstrapped time series become more independent, is examined along with the size and power of the respective tests. Additionally, we consider a seventh resampling method by contemporaneously resampling the driving and the response time series using the stationary bootstrap. Although this case does not comply with the no causality hypothesis, one can obtain an accurate sampling distribution for the mean of the test statistic since its value is zero under H0. Results indicate that as the resampling setting gets more independent, the test becomes more conservative. Finally, we conclude with a real application. More specifically, we investigate the causal links among the growth rates for the US CPI, money supply and crude oil. Based on the PTE and the seven resampling methods, we consistently find that changes in crude oil cause inflation conditioning on money supply in the post-1986 period. However this relationship cannot be explained on the basis of traditional cost-push mechanisms. PMID:28708870

Assessment of resampling methods for causality testing: A note on the US inflation behavior.

PubMed

Papana, Angeliki; Kyrtsou, Catherine; Kugiumtzis, Dimitris; Diks, Cees

2017-01-01

Different resampling methods for the null hypothesis of no Granger causality are assessed in the setting of multivariate time series, taking into account that the driving-response coupling is conditioned on the other observed variables. As appropriate test statistic for this setting, the partial transfer entropy (PTE), an information and model-free measure, is used. Two resampling techniques, time-shifted surrogates and the stationary bootstrap, are combined with three independence settings (giving a total of six resampling methods), all approximating the null hypothesis of no Granger causality. In these three settings, the level of dependence is changed, while the conditioning variables remain intact. The empirical null distribution of the PTE, as the surrogate and bootstrapped time series become more independent, is examined along with the size and power of the respective tests. Additionally, we consider a seventh resampling method by contemporaneously resampling the driving and the response time series using the stationary bootstrap. Although this case does not comply with the no causality hypothesis, one can obtain an accurate sampling distribution for the mean of the test statistic since its value is zero under H0. Results indicate that as the resampling setting gets more independent, the test becomes more conservative. Finally, we conclude with a real application. More specifically, we investigate the causal links among the growth rates for the US CPI, money supply and crude oil. Based on the PTE and the seven resampling methods, we consistently find that changes in crude oil cause inflation conditioning on money supply in the post-1986 period. However this relationship cannot be explained on the basis of traditional cost-push mechanisms.

Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis.

PubMed

Pataky, Todd C; Vanrenterghem, Jos; Robinson, Mark A

2015-05-01

Biomechanical processes are often manifested as one-dimensional (1D) trajectories. It has been shown that 1D confidence intervals (CIs) are biased when based on 0D statistical procedures, and the non-parametric 1D bootstrap CI has emerged in the Biomechanics literature as a viable solution. The primary purpose of this paper was to clarify that, for 1D biomechanics datasets, the distinction between 0D and 1D methods is much more important than the distinction between parametric and non-parametric procedures. A secondary purpose was to demonstrate that a parametric equivalent to the 1D bootstrap exists in the form of a random field theory (RFT) correction for multiple comparisons. To emphasize these points we analyzed six datasets consisting of force and kinematic trajectories in one-sample, paired, two-sample and regression designs. Results showed, first, that the 1D bootstrap and other 1D non-parametric CIs were qualitatively identical to RFT CIs, and all were very different from 0D CIs. Second, 1D parametric and 1D non-parametric hypothesis testing results were qualitatively identical for all six datasets. Last, we highlight the limitations of 1D CIs by demonstrating that they are complex, design-dependent, and thus non-generalizable. These results suggest that (i) analyses of 1D data based on 0D models of randomness are generally biased unless one explicitly identifies 0D variables before the experiment, and (ii) parametric and non-parametric 1D hypothesis testing provide an unambiguous framework for analysis when one׳s hypothesis explicitly or implicitly pertains to whole 1D trajectories. Copyright © 2015 Elsevier Ltd. All rights reserved.

Core transport properties in JT-60U and JET identity plasmas

NASA Astrophysics Data System (ADS)

Litaudon, X.; Sakamoto, Y.; de Vries, P. C.; Salmi, A.; Tala, T.; Angioni, C.; Benkadda, S.; Beurskens, M. N. A.; Bourdelle, C.; Brix, M.; Crombé, K.; Fujita, T.; Futatani, S.; Garbet, X.; Giroud, C.; Hawkes, N. C.; Hayashi, N.; Hoang, G. T.; Hogeweij, G. M. D.; Matsunaga, G.; Nakano, T.; Oyama, N.; Parail, V.; Shinohara, K.; Suzuki, T.; Takechi, M.; Takenaga, H.; Takizuka, T.; Urano, H.; Voitsekhovitch, I.; Yoshida, M.; ITPA Transport Group; JT-60 Team; EFDA contributors, JET

2011-07-01

The paper compares the transport properties of a set of dimensionless identity experiments performed between JET and JT-60U in the advanced tokamak regime with internal transport barrier, ITB. These International Tokamak Physics Activity, ITPA, joint experiments were carried out with the same plasma shape, toroidal magnetic field ripple and dimensionless profiles as close as possible during the ITB triggering phase in terms of safety factor, normalized Larmor radius, normalized collision frequency, thermal beta, ratio of ion to electron temperatures. Similarities in the ITB triggering mechanisms and sustainment were observed when a good match was achieved of the most relevant normalized profiles except the toroidal Mach number. Similar thermal ion transport levels in the two devices have been measured in either monotonic or non-monotonic q-profiles. In contrast, differences between JET and JT-60U were observed on the electron thermal and particle confinement in reversed magnetic shear configurations. It was found that the larger shear reversal in the very centre (inside normalized radius of 0.2) of JT-60U plasmas allowed the sustainment of stronger electron density ITBs compared with JET. As a consequence of peaked density profile, the core bootstrap current density is more than five times higher in JT-60U compared with JET. Thanks to the bootstrap effect and the slightly broader neutral beam deposition, reversed magnetic shear configurations are self-sustained in JT-60U scenarios. Analyses of similarities and differences between the two devices address key questions on the validity of the usual assumptions made in ITER steady scenario modelling, e.g. a flat density profile in the core with thermal transport barrier? Such assumptions have consequences on the prediction of fusion performance, bootstrap current and on the sustainment of the scenario.

Different methodologies to quantify uncertainties of air emissions.

PubMed

Romano, Daniela; Bernetti, Antonella; De Lauretis, Riccardo

2004-10-01

Characterization of the uncertainty associated with air emission estimates is of critical importance especially in the compilation of air emission inventories. In this paper, two different theories are discussed and applied to evaluate air emissions uncertainty. In addition to numerical analysis, which is also recommended in the framework of the United Nation Convention on Climate Change guidelines with reference to Monte Carlo and Bootstrap simulation models, fuzzy analysis is also proposed. The methodologies are discussed and applied to an Italian example case study. Air concentration values are measured from two electric power plants: a coal plant, consisting of two boilers and a fuel oil plant, of four boilers; the pollutants considered are sulphur dioxide (SO(2)), nitrogen oxides (NO(X)), carbon monoxide (CO) and particulate matter (PM). Monte Carlo, Bootstrap and fuzzy methods have been applied to estimate uncertainty of these data. Regarding Monte Carlo, the most accurate results apply to Gaussian distributions; a good approximation is also observed for other distributions with almost regular features either positive asymmetrical or negative asymmetrical. Bootstrap, on the other hand, gives a good uncertainty estimation for irregular and asymmetrical distributions. The logic of fuzzy analysis, where data are represented as vague and indefinite in opposition to the traditional conception of neatness, certain classification and exactness of the data, follows a different description. In addition to randomness (stochastic variability) only, fuzzy theory deals with imprecision (vagueness) of data. Fuzzy variance of the data set was calculated; the results cannot be directly compared with empirical data but the overall performance of the theory is analysed. Fuzzy theory may appear more suitable for qualitative reasoning than for a quantitative estimation of uncertainty, but it suits well when little information and few measurements are available and when distributions of data are not properly known.

Universality of Critically Pinned Interfaces in Two-Dimensional Isotropic Random Media

NASA Astrophysics Data System (ADS)

Grassberger, Peter

2018-05-01

Based on extensive simulations, we conjecture that critically pinned interfaces in two-dimensional isotropic random media with short-range correlations are always in the universality class of ordinary percolation. Thus, in contrast to interfaces in >2 dimensions, there is no distinction between fractal (i.e., percolative) and rough but nonfractal interfaces. Our claim includes interfaces in zero-temperature random field Ising models (both with and without spontaneous nucleation), in heterogeneous bootstrap percolation, and in susceptible-weakened-infected-removed epidemics. It does not include models with long-range correlations in the randomness and models where overhangs are explicitly forbidden (which would imply nonisotropy of the medium).

Prediction of forest fires occurrences with area-level Poisson mixed models.

PubMed

Boubeta, Miguel; Lombardía, María José; Marey-Pérez, Manuel Francisco; Morales, Domingo

2015-05-01

The number of fires in forest areas of Galicia (north-west of Spain) during the summer period is quite high. Local authorities are interested in analyzing the factors that explain this phenomenon. Poisson regression models are good tools for describing and predicting the number of fires per forest areas. This work employs area-level Poisson mixed models for treating real data about fires in forest areas. A parametric bootstrap method is applied for estimating the mean squared errors of fires predictors. The developed methodology and software are applied to a real data set of fires in forest areas of Galicia. Copyright © 2015 Elsevier Ltd. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tucker, Susan L.; Liu, H. Helen; Wang, Shulian

Purpose: The aim of this study was to investigate the effect of radiation dose distribution in the lung on the risk of postoperative pulmonary complications among esophageal cancer patients. Methods and Materials: We analyzed data from 110 patients with esophageal cancer treated with concurrent chemoradiotherapy followed by surgery at our institution from 1998 to 2003. The endpoint for analysis was postsurgical pneumonia or acute respiratory distress syndrome. Dose-volume histograms (DVHs) and dose-mass histograms (DMHs) for the whole lung were used to fit normal-tissue complication probability (NTCP) models, and the quality of fits were compared using bootstrap analysis. Results: Normal-tissue complicationmore » probability modeling identified that the risk of postoperative pulmonary complications was most significantly associated with small absolute volumes of lung spared from doses {>=}5 Gy (VS5), that is, exposed to doses <5 Gy. However, bootstrap analysis found no significant difference between the quality of this model and fits based on other dosimetric parameters, including mean lung dose, effective dose, and relative volume of lung receiving {>=}5 Gy, probably because of correlations among these factors. The choice of DVH vs. DMH or the use of fractionation correction did not significantly affect the results of the NTCP modeling. The parameter values estimated for the Lyman NTCP model were as follows (with 95% confidence intervals in parentheses): n = 1.85 (0.04, {infinity}), m = 0.55 (0.22, 1.02), and D {sub 5} = 17.5 Gy (9.4 Gy, 102 Gy). Conclusions: In this cohort of esophageal cancer patients, several dosimetric parameters including mean lung dose, effective dose, and absolute volume of lung receiving <5 Gy provided similar descriptions of the risk of postoperative pulmonary complications as a function of Radiation dose distribution in the lung.« less

Accounting for parameter uncertainty in the definition of parametric distributions used to describe individual patient variation in health economic models.

PubMed

Degeling, Koen; IJzerman, Maarten J; Koopman, Miriam; Koffijberg, Hendrik

2017-12-15

Parametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive guidance on reflecting parameter uncertainty in the (correlated) parameters of distributions used to represent stochastic uncertainty in patient-level models. This study aims to provide this guidance by proposing appropriate methods and illustrating the impact of this uncertainty on modeling outcomes. Two approaches, 1) using non-parametric bootstrapping and 2) using multivariate Normal distributions, were applied in a simulation and case study. The approaches were compared based on point-estimates and distributions of time-to-event and health economic outcomes. To assess sample size impact on the uncertainty in these outcomes, sample size was varied in the simulation study and subgroup analyses were performed for the case-study. Accounting for parameter uncertainty in distributions that reflect stochastic uncertainty substantially increased the uncertainty surrounding health economic outcomes, illustrated by larger confidence ellipses surrounding the cost-effectiveness point-estimates and different cost-effectiveness acceptability curves. Although both approaches performed similar for larger sample sizes (i.e. n = 500), the second approach was more sensitive to extreme values for small sample sizes (i.e. n = 25), yielding infeasible modeling outcomes. Modelers should be aware that parameter uncertainty in distributions used to describe stochastic uncertainty needs to be reflected in probabilistic sensitivity analysis, as it could substantially impact the total amount of uncertainty surrounding health economic outcomes. If feasible, the bootstrap approach is recommended to account for this uncertainty.

Bootstrap Current for the Edge Pedestal Plasma in a Diverted Tokamak Geometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Koh, S.; Chang, C. S.; Ku, S.

The edge bootstrap current plays a critical role in the equilibrium and stability of the steep edge pedestal plasma. The pedestal plasma has an unconventional and difficult neoclassical property, as compared with the core plasma. It has a narrow passing particle region in velocity space that can be easily modified or destroyed by Coulomb collisions. At the same time, the edge pedestal plasma has steep pressure and electrostatic potential gradients whose scale-lengths are comparable with the ion banana width, and includes a magnetic separatrix surface, across which the topological properties of the magnetic field and particle orbits change abruptly. Amore » driftkinetic particle code XGC0, equipped with a mass-momentum-energy conserving collision operator, is used to study the edge bootstrap current in a realistic diverted magnetic field geometry with a self-consistent radial electric field. When the edge electrons are in the weakly collisional banana regime, surprisingly, the present kinetic simulation confirms that the existing analytic expressions [represented by O. Sauter et al. , Phys. Plasmas 6 , 2834 (1999)] are still valid in this unconventional region, except in a thin radial layer in contact with the magnetic separatrix. The agreement arises from the dominance of the electron contribution to the bootstrap current compared with ion contribution and from a reasonable separation of the trapped-passing dynamics without a strong collisional mixing. However, when the pedestal electrons are in plateau-collisional regime, there is significant deviation of numerical results from the existing analytic formulas, mainly due to large effective collisionality of the passing and the boundary layer trapped particles in edge region. In a conventional aspect ratio tokamak, the edge bootstrap current from kinetic simulation can be significantly less than that from the Sauter formula if the electron collisionality is high. On the other hand, when the aspect ratio is close to unity, the collisional edge bootstrap current can be significantly greater than that from the Sauter formula. Rapid toroidal rotation of the magnetic field lines at the high field side of a tight aspect-ratio tokamak is believed to be the cause of the different behavior. A new analytic fitting formula, as a simple modification to the Sauter formula, is obtained to bring the analytic expression to a better agreement with the edge kinetic simulation results« less

Bootstrap current for the edge pedestal plasma in a diverted tokamak geometry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Koh, S.; Choe, W.; Chang, C. S.

The edge bootstrap current plays a critical role in the equilibrium and stability of the steep edge pedestal plasma. The pedestal plasma has an unconventional and difficult neoclassical property, as compared with the core plasma. It has a narrow passing particle region in velocity space that can be easily modified or destroyed by Coulomb collisions. At the same time, the edge pedestal plasma has steep pressure and electrostatic potential gradients whose scale-lengths are comparable with the ion banana width, and includes a magnetic separatrix surface, across which the topological properties of the magnetic field and particle orbits change abruptly. Amore » drift-kinetic particle code XGC0, equipped with a mass-momentum-energy conserving collision operator, is used to study the edge bootstrap current in a realistic diverted magnetic field geometry with a self-consistent radial electric field. When the edge electrons are in the weakly collisional banana regime, surprisingly, the present kinetic simulation confirms that the existing analytic expressions [represented by O. Sauter et al., Phys. Plasmas 6, 2834 (1999)] are still valid in this unconventional region, except in a thin radial layer in contact with the magnetic separatrix. The agreement arises from the dominance of the electron contribution to the bootstrap current compared with ion contribution and from a reasonable separation of the trapped-passing dynamics without a strong collisional mixing. However, when the pedestal electrons are in plateau-collisional regime, there is significant deviation of numerical results from the existing analytic formulas, mainly due to large effective collisionality of the passing and the boundary layer trapped particles in edge region. In a conventional aspect ratio tokamak, the edge bootstrap current from kinetic simulation can be significantly less than that from the Sauter formula if the electron collisionality is high. On the other hand, when the aspect ratio is close to unity, the collisional edge bootstrap current can be significantly greater than that from the Sauter formula. Rapid toroidal rotation of the magnetic field lines at the high field side of a tight aspect-ratio tokamak is believed to be the cause of the different behavior. A new analytic fitting formula, as a simple modification to the Sauter formula, is obtained to bring the analytic expression to a better agreement with the edge kinetic simulation results.« less

A New Survey for Low-Redshift Damped Lyman-Alpha Lines in QSO MgII Systems

NASA Astrophysics Data System (ADS)

Rao, Sandhya

2000-07-01

Studies have shown that most of the observable neutral gas mass in the Universe resides in QSO damped LyAlpha {DLA} systems. However, at low redshift {z<1.65}, DLA can only be found by searching in the UV with HST. Such searches are crucial since z<1.65 corresponds to 3/4 of the age of the Universe. The identification of significant numbers of low- redshift DLA systems is imperative if we ever hope to effectively study this cosmologically massive component of neutral gas. To this end, we recently reported on the results of our initial HST survey to study low-redshift DLA absorbers in QSO MgII systems. We discovered 14 DLA systems and had a success rate of 14%. Now, based on these results and our improved understanding of the selection criteria for successful DLA searches, we propose a new survey for low-redshift DLA lines in QSO MgII systems. With our new revised selection criteria, we can empirically show that our success rate would be 35%. Specifically, we propose to observe the LyAlpha line of 55 MgII systems. We estimate that we will discover 19 new DLA systems with redshift z<1.65. Finding these systems will facilitate the type of research that can be done with DLA systems. By boot-strapping from the MgII statistics, we will be able to further improve the determination of the low- redshift statistical properties of DLA {their incidence and cosmological mass density} and open up new opportunities for studies at low redshift.

A search for evidence of solar rotation in Super-Kamiokande solar neutrino dataset

NASA Astrophysics Data System (ADS)

Desai, Shantanu; Liu, Dawei W.

2016-09-01

We apply the generalized Lomb-Scargle (LS) periodogram, proposed by Zechmeister and Kurster, to the solar neutrino data from Super-Kamiokande (Super-K) using data from its first five years. For each peak in the LS periodogram, we evaluate the statistical significance in two different ways. The first method involves calculating the False Alarm Probability (FAP) using non-parametric bootstrap resampling, and the second method is by calculating the difference in Bayesian Information Criterion (BIC) between the null hypothesis, viz. the data contains only noise, compared to the hypothesis that the data contains a peak at a given frequency. Using these methods, we scan the frequency range between 7-14 cycles per year to look for any peaks caused by solar rotation, since this is the proposed explanation for the statistically significant peaks found by Sturrock and collaborators in the Super-K dataset. From our analysis, we do confirm that similar to Sturrock et al, the maximum peak occurs at a frequency of 9.42/year, corresponding to a period of 38.75 days. The FAP for this peak is about 1.5% and the difference in BIC (between pure white noise and this peak) is about 4.8. We note that the significance depends on the frequency band used to search for peaks and hence it is important to use a search band appropriate for solar rotation. However, The significance of this peak based on the value of BIC is marginal and more data is needed to confirm if the peak persists and is real.

Incorporating Single-nucleotide Polymorphisms Into the Lyman Model to Improve Prediction of Radiation Pneumonitis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tucker, Susan L., E-mail: sltucker@mdanderson.org; Li Minghuan; Xu Ting

2013-01-01

Purpose: To determine whether single-nucleotide polymorphisms (SNPs) in genes associated with DNA repair, cell cycle, transforming growth factor-{beta}, tumor necrosis factor and receptor, folic acid metabolism, and angiogenesis can significantly improve the fit of the Lyman-Kutcher-Burman (LKB) normal-tissue complication probability (NTCP) model of radiation pneumonitis (RP) risk among patients with non-small cell lung cancer (NSCLC). Methods and Materials: Sixteen SNPs from 10 different genes (XRCC1, XRCC3, APEX1, MDM2, TGF{beta}, TNF{alpha}, TNFR, MTHFR, MTRR, and VEGF) were genotyped in 141 NSCLC patients treated with definitive radiation therapy, with or without chemotherapy. The LKB model was used to estimate the risk ofmore » severe (grade {>=}3) RP as a function of mean lung dose (MLD), with SNPs and patient smoking status incorporated into the model as dose-modifying factors. Multivariate analyses were performed by adding significant factors to the MLD model in a forward stepwise procedure, with significance assessed using the likelihood-ratio test. Bootstrap analyses were used to assess the reproducibility of results under variations in the data. Results: Five SNPs were selected for inclusion in the multivariate NTCP model based on MLD alone. SNPs associated with an increased risk of severe RP were in genes for TGF{beta}, VEGF, TNF{alpha}, XRCC1 and APEX1. With smoking status included in the multivariate model, the SNPs significantly associated with increased risk of RP were in genes for TGF{beta}, VEGF, and XRCC3. Bootstrap analyses selected a median of 4 SNPs per model fit, with the 6 genes listed above selected most often. Conclusions: This study provides evidence that SNPs can significantly improve the predictive ability of the Lyman MLD model. With a small number of SNPs, it was possible to distinguish cohorts with >50% risk vs <10% risk of RP when they were exposed to high MLDs.« less

A cluster bootstrap for two-loop MHV amplitudes

DOE PAGES

Golden, John; Spradlin, Marcus

2015-02-02

We apply a bootstrap procedure to two-loop MHV amplitudes in planar N=4 super-Yang-Mills theory. We argue that the mathematically most complicated part (the Λ 2 B 2 coproduct component) of the n-particle amplitude is uniquely determined by a simple cluster algebra property together with a few physical constraints (dihedral symmetry, analytic structure, supersymmetry, and well-defined collinear limits). Finally, we present a concise, closed-form expression which manifests these properties for all n.

CME Velocity and Acceleration Error Estimates Using the Bootstrap Method

NASA Technical Reports Server (NTRS)

Michalek, Grzegorz; Gopalswamy, Nat; Yashiro, Seiji

2017-01-01

The bootstrap method is used to determine errors of basic attributes of coronal mass ejections (CMEs) visually identified in images obtained by the Solar and Heliospheric Observatory (SOHO) mission's Large Angle and Spectrometric Coronagraph (LASCO) instruments. The basic parameters of CMEs are stored, among others, in a database known as the SOHO/LASCO CME catalog and are widely employed for many research studies. The basic attributes of CMEs (e.g. velocity and acceleration) are obtained from manually generated height-time plots. The subjective nature of manual measurements introduces random errors that are difficult to quantify. In many studies the impact of such measurement errors is overlooked. In this study we present a new possibility to estimate measurements errors in the basic attributes of CMEs. This approach is a computer-intensive method because it requires repeating the original data analysis procedure several times using replicate datasets. This is also commonly called the bootstrap method in the literature. We show that the bootstrap approach can be used to estimate the errors of the basic attributes of CMEs having moderately large numbers of height-time measurements. The velocity errors are in the vast majority small and depend mostly on the number of height-time points measured for a particular event. In the case of acceleration, the errors are significant, and for more than half of all CMEs, they are larger than the acceleration itself.

Using the Bootstrap Method for a Statistical Significance Test of Differences between Summary Histograms

NASA Technical Reports Server (NTRS)

Xu, Kuan-Man

2006-01-01

A new method is proposed to compare statistical differences between summary histograms, which are the histograms summed over a large ensemble of individual histograms. It consists of choosing a distance statistic for measuring the difference between summary histograms and using a bootstrap procedure to calculate the statistical significance level. Bootstrapping is an approach to statistical inference that makes few assumptions about the underlying probability distribution that describes the data. Three distance statistics are compared in this study. They are the Euclidean distance, the Jeffries-Matusita distance and the Kuiper distance. The data used in testing the bootstrap method are satellite measurements of cloud systems called cloud objects. Each cloud object is defined as a contiguous region/patch composed of individual footprints or fields of view. A histogram of measured values over footprints is generated for each parameter of each cloud object and then summary histograms are accumulated over all individual histograms in a given cloud-object size category. The results of statistical hypothesis tests using all three distances as test statistics are generally similar, indicating the validity of the proposed method. The Euclidean distance is determined to be most suitable after comparing the statistical tests of several parameters with distinct probability distributions among three cloud-object size categories. Impacts on the statistical significance levels resulting from differences in the total lengths of satellite footprint data between two size categories are also discussed.

Image analysis of representative food structures: application of the bootstrap method.

PubMed

Ramírez, Cristian; Germain, Juan C; Aguilera, José M

2009-08-01

Images (for example, photomicrographs) are routinely used as qualitative evidence of the microstructure of foods. In quantitative image analysis it is important to estimate the area (or volume) to be sampled, the field of view, and the resolution. The bootstrap method is proposed to estimate the size of the sampling area as a function of the coefficient of variation (CV(Bn)) and standard error (SE(Bn)) of the bootstrap taking sub-areas of different sizes. The bootstrap method was applied to simulated and real structures (apple tissue). For simulated structures, 10 computer-generated images were constructed containing 225 black circles (elements) and different coefficient of variation (CV(image)). For apple tissue, 8 images of apple tissue containing cellular cavities with different CV(image) were analyzed. Results confirmed that for simulated and real structures, increasing the size of the sampling area decreased the CV(Bn) and SE(Bn). Furthermore, there was a linear relationship between the CV(image) and CV(Bn) (.) For example, to obtain a CV(Bn) = 0.10 in an image with CV(image) = 0.60, a sampling area of 400 x 400 pixels (11% of whole image) was required, whereas if CV(image) = 1.46, a sampling area of 1000 x 100 pixels (69% of whole image) became necessary. This suggests that a large-size dispersion of element sizes in an image requires increasingly larger sampling areas or a larger number of images.

Procrastination and suicide proneness: A moderated-mediation model for cognitive schemas and gender.

PubMed

Klibert, Jeffrey; LeLeux-LaBarge, Kayla; Tarantino, Nicholas; Yancey, Thresa; Lamis, Dorian A

2016-07-01

This study examined the direct and indirect paths between procrastination and suicide proneness while considering gender differences. Participants included 547 undergraduates from a southeastern university. Procrastination was positively related to suicide proneness for both genders, although this relation was stronger for women. Moderated-mediation analyses with bootstrapping highlighted insufficient self-control schemas as a mediator in the relation between procrastination and suicide proneness. However, indirect pathways did not vary by gender. Results represent an extension of the Procrastination-Health Model by highlighting the contribution of cognitive factors in explaining the relation between procrastination and suicide proneness.

The prevalence of terraced treescapes in analyses of phylogenetic data sets.

PubMed

Dobrin, Barbara H; Zwickl, Derrick J; Sanderson, Michael J

2018-04-04

The pattern of data availability in a phylogenetic data set may lead to the formation of terraces, collections of equally optimal trees. Terraces can arise in tree space if trees are scored with parsimony or with partitioned, edge-unlinked maximum likelihood. Theory predicts that terraces can be large, but their prevalence in contemporary data sets has never been surveyed. We selected 26 data sets and phylogenetic trees reported in recent literature and investigated the terraces to which the trees would belong, under a common set of inference assumptions. We examined terrace size as a function of the sampling properties of the data sets, including taxon coverage density (the proportion of taxon-by-gene positions with any data present) and a measure of gene sampling "sufficiency". We evaluated each data set in relation to the theoretical minimum gene sampling depth needed to reduce terrace size to a single tree, and explored the impact of the terraces found in replicate trees in bootstrap methods. Terraces were identified in nearly all data sets with taxon coverage densities < 0.90. They were not found, however, in high-coverage-density (i.e., ≥ 0.94) transcriptomic and genomic data sets. The terraces could be very large, and size varied inversely with taxon coverage density and with gene sampling sufficiency. Few data sets achieved a theoretical minimum gene sampling depth needed to reduce terrace size to a single tree. Terraces found during bootstrap resampling reduced overall support. If certain inference assumptions apply, trees estimated from empirical data sets often belong to large terraces of equally optimal trees. Terrace size correlates to data set sampling properties. Data sets seldom include enough genes to reduce terrace size to one tree. When bootstrap replicate trees lie on a terrace, statistical support for phylogenetic hypotheses may be reduced. Although some of the published analyses surveyed were conducted with edge-linked inference models (which do not induce terraces), unlinked models have been used and advocated. The present study describes the potential impact of that inference assumption on phylogenetic inference in the context of the kinds of multigene data sets now widely assembled for large-scale tree construction.

A Pragmatic Cognitive System Engineering Approach to Model Dynamic Human Decision-Making Activities in Intelligent and Automated Systems

DTIC Science & Technology

2003-10-01

Among the procedures developed to identify cognitive processes, there are the Cognitive Task Analysis (CTA) and the Cognitive Work Analysis (CWA...of Cognitive Task Design. [11] Potter, S.S., Roth, E.M., Woods, D.D., and Elm, W.C. (2000). Cognitive Task Analysis as Bootstrapping Multiple...Converging Techniques, In Schraagen, Chipman, and Shalin (Eds.). Cognitive Task Analysis . Mahwah, NJ: Lawrence Erlbaum Associates. [12] Roth, E.M

Improved dynamical scaling analysis using the kernel method for nonequilibrium relaxation.

PubMed

Echinaka, Yuki; Ozeki, Yukiyasu

2016-10-01

The dynamical scaling analysis for the Kosterlitz-Thouless transition in the nonequilibrium relaxation method is improved by the use of Bayesian statistics and the kernel method. This allows data to be fitted to a scaling function without using any parametric model function, which makes the results more reliable and reproducible and enables automatic and faster parameter estimation. Applying this method, the bootstrap method is introduced and a numerical discrimination for the transition type is proposed.

Covariance Analysis of Vision Aided Navigation by Bootstrapping

DTIC Science & Technology

2012-03-22

vision aided navigation. The aircraft uses its INS estimate to geolocate ground features, track those features to aid the INS, and using that aided...development of the 2-D case, including the dynamics and measurement model development, the state space representation and the use of the Kalman filter ...reference frame. This reference frame has its origin located somewhere on an A/C. Normally the origin is set at the A/C center of gravity to allow the use

Authenticated IGMP for Controlling Access to Multicast Distribution Tree

NASA Astrophysics Data System (ADS)

Park, Chang-Seop; Kang, Hyun-Sun

A receiver access control scheme is proposed to protect the multicast distribution tree from DoS attack induced by unauthorized use of IGMP, by extending the security-related functionality of IGMP. Based on a specific network and business model adopted for commercial deployment of IP multicast applications, a key management scheme is also presented for bootstrapping the proposed access control as well as accounting and billing for CP (Content Provider), NSP (Network Service Provider), and group members.

Performance of statistical models to predict mental health and substance abuse cost.

PubMed

Montez-Rath, Maria; Christiansen, Cindy L; Ettner, Susan L; Loveland, Susan; Rosen, Amy K

2006-10-26

Providers use risk-adjustment systems to help manage healthcare costs. Typically, ordinary least squares (OLS) models on either untransformed or log-transformed cost are used. We examine the predictive ability of several statistical models, demonstrate how model choice depends on the goal for the predictive model, and examine whether building models on samples of the data affects model choice. Our sample consisted of 525,620 Veterans Health Administration patients with mental health (MH) or substance abuse (SA) diagnoses who incurred costs during fiscal year 1999. We tested two models on a transformation of cost: a Log Normal model and a Square-root Normal model, and three generalized linear models on untransformed cost, defined by distributional assumption and link function: Normal with identity link (OLS); Gamma with log link; and Gamma with square-root link. Risk-adjusters included age, sex, and 12 MH/SA categories. To determine the best model among the entire dataset, predictive ability was evaluated using root mean square error (RMSE), mean absolute prediction error (MAPE), and predictive ratios of predicted to observed cost (PR) among deciles of predicted cost, by comparing point estimates and 95% bias-corrected bootstrap confidence intervals. To study the effect of analyzing a random sample of the population on model choice, we re-computed these statistics using random samples beginning with 5,000 patients and ending with the entire sample. The Square-root Normal model had the lowest estimates of the RMSE and MAPE, with bootstrap confidence intervals that were always lower than those for the other models. The Gamma with square-root link was best as measured by the PRs. The choice of best model could vary if smaller samples were used and the Gamma with square-root link model had convergence problems with small samples. Models with square-root transformation or link fit the data best. This function (whether used as transformation or as a link) seems to help deal with the high comorbidity of this population by introducing a form of interaction. The Gamma distribution helps with the long tail of the distribution. However, the Normal distribution is suitable if the correct transformation of the outcome is used.

The impact of foreign direct investment on CO2 emissions in Turkey: new evidence from cointegration and bootstrap causality analysis.

PubMed

Koçak, Emrah; Şarkgüneşi, Aykut

2018-01-01

Pollution haven hypothesis (PHH), which is defined as foreign direct investment inducing a raising impact on the pollution level in the hosting country, is lately a subject of discussion in the field of economics. This study, within the scope of related discussion, aims to look into the potential impact of foreign direct investments on CO 2 emission in Turkey in 1974-2013 period using environmental Kuznets curve (EKC) model. For this purpose, Maki (Econ Model 29(5):2011-2015, 2012) structural break cointegration test, Stock and Watson (Econometrica 61:783-820, 1993) dynamic ordinary least square estimator (DOLS), and Hacker and Hatemi-J (J Econ Stud 39(2):144-160, 2012) bootstrap test for causality method are used. Research results indicate the existence of a long-term balance relationship between FDI, economic growth, energy usage, and CO 2 emission. As per this relationship, in Turkey, (1) the potential impact of FDI on CO 2 emission is positive. This result shows that PHH is valid in Turkey. (2) Moreover, this is not a one-way relationship; the changes in CO 2 emission also affect FDI entries. (3) The results also provide evidence for the existence of the EKC hypothesis in Turkey. Within the frame of related findings, the study concludes several polities and presents various suggestions.

Nomograms Predicting Progression-Free Survival, Overall Survival, and Pelvic Recurrence in Locally Advanced Cervical Cancer Developed From an Analysis of Identifiable Prognostic Factors in Patients From NRG Oncology/Gynecologic Oncology Group Randomized Trials of Chemoradiotherapy

PubMed Central

Rose, Peter G.; Java, James; Whitney, Charles W.; Stehman, Frederick B.; Lanciano, Rachelle; Thomas, Gillian M.; DiSilvestro, Paul A.

2015-01-01

Purpose To evaluate the prognostic factors in locally advanced cervical cancer limited to the pelvis and develop nomograms for 2-year progression-free survival (PFS), 5-year overall survival (OS), and pelvic recurrence. Patients and Methods We retrospectively reviewed 2,042 patients with locally advanced cervical carcinoma enrolled onto Gynecologic Oncology Group clinical trials of concurrent cisplatin-based chemotherapy and radiotherapy. Nomograms for 2-year PFS, five-year OS, and pelvic recurrence were created as visualizations of Cox proportional hazards regression models. The models were validated by bootstrap-corrected, relatively unbiased estimates of discrimination and calibration. Results Multivariable analysis identified prognostic factors including histology, race/ethnicity, performance status, tumor size, International Federation of Gynecology and Obstetrics stage, tumor grade, pelvic node status, and treatment with concurrent cisplatin-based chemotherapy. PFS, OS, and pelvic recurrence nomograms had bootstrap-corrected concordance indices of 0.62, 0.64, and 0.73, respectively, and were well calibrated. Conclusion Prognostic factors were used to develop nomograms for 2-year PFS, 5-year OS, and pelvic recurrence for locally advanced cervical cancer clinically limited to the pelvis treated with concurrent cisplatin-based chemotherapy and radiotherapy. These nomograms can be used to better estimate individual and collective outcomes. PMID:25732170

Relationship Between Total and Bioaccessible Lead on Children's Blood Lead Levels in Urban Residential Philadelphia Soils.

PubMed

Bradham, Karen D; Nelson, Clay M; Kelly, Jack; Pomales, Ana; Scruton, Karen; Dignam, Tim; Misenheimer, John C; Li, Kevin; Obenour, Daniel R; Thomas, David J

2017-09-05

Relationships between total soil or bioaccessible lead (Pb), measured using an in vitro bioaccessibility assay, and children's blood lead levels (BLL) were investigated in an urban neighborhood in Philadelphia, PA, with a history of soil Pb contamination. Soil samples from 38 homes were analyzed to determine whether accounting for the bioaccessible Pb fraction improves statistical relationships with children's BLLs. Total soil Pb concentration ranged from 58 to 2821 mg/kg; the bioaccessible Pb concentration ranged from 47 to 2567 mg/kg. Children's BLLs ranged from 0.3 to 9.8 μg/dL. Hierarchical models were used to compare relationships between total or bioaccessible Pb in soil and children's BLLs. Total soil Pb concentration as the predictor accounted for 23% of the variability in child BLL; bioaccessible soil Pb concentration as the predictor accounted for 26% of BLL variability. A bootstrapping analysis confirmed a significant increase in R 2 for the model using bioaccessible soil Pb concentration as the predictor with 99.0% of bootstraps showing a positive increase. Estimated increases of 1.3 μg/dL and 1.5 μg/dL in BLL per 1000 mg/kg Pb in soil were observed for this study area using total and bioaccessible Pb concentrations, respectively. Children's age did not contribute significantly to the prediction of BLLs.

Effects of Changes in Potassium With Valsartan Use on Diabetes Risk: Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) Trial

PubMed Central

Thomas, Laine; Svetkey, Laura; Brancati, Frederick L.; Califf, Robert M.; Edelman, David

2013-01-01

BACKGROUND Low and low-normal serum potassium is associated with an increased risk of diabetes. We hypothesized that the protective effect of valsartan on diabetes risk could be mediated by its effect of raising serum potassium. METHODS We analyzed data from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) trial, which randomized participants at risk for diabetes to either valsartan (up to 160mg daily) or no valsartan. Using Cox models, we evaluated the effect of valsartan on diabetes risk over a median of 4 years of follow-up and calculated the mediation effect of serum potassium as the difference in treatment hazard ratios from models excluding and including 1-year change in serum potassium. The 95% confidence interval (CI) for the difference in log hazard ratios was computed by bootstrapping. RESULTS The hazard ratio for developing diabetes among those on valsartan vs. no valsartan was 0.866 (95% CI = 0.795–0.943) vs. 0.868 (95% CI = 0.797–0.945), after controlling for 1-year change in potassium. The bootstrap 95% CI for a difference in these log hazard ratios was not statistically significant (−0.003 to 0.009). CONCLUSIONS Serum potassium does not appear to significantly mediate the protective effect of valsartan on diabetes risk. PMID:23417031

Examination of the reliability of the crash modification factors using empirical Bayes method with resampling technique.

PubMed

Wang, Jung-Han; Abdel-Aty, Mohamed; Wang, Ling

2017-07-01

There have been plenty of studies intended to use different methods, for example, empirical Bayes before-after methods, to get accurate estimation of CMFs. All of them have different assumptions toward crash count if there was no treatment. Additionally, another major assumption is that multiple sites share the same true CMF. Under this assumption, the CMF at an individual intersection is randomly drawn from a normally distributed population of CMFs at all intersections. Since CMFs are non-zero values, the population of all CMFs might not follow normal distributions, and even if it does, the true mean of CMFs at some intersections may be different from that at others. Therefore, a bootstrap method based on before-after empirical Bayes theory was proposed to estimate CMFs, but it did not make distributional assumptions. This bootstrap procedure has the added benefit of producing a measure of CMF stability. Furthermore, based on the bootstrapped CMF, a new CMF precision rating method was proposed to evaluate the reliability of CMFs. This study chose 29 urban four-legged intersections as treated sites, and their controls were changed from stop-controlled to signal-controlled. Meanwhile, 124 urban four-legged stop-controlled intersections were selected as reference sites. At first, different safety performance functions (SPFs) were applied to five crash categories, and it was found that each crash category had different optimal SPF form. Then, the CMFs of these five crash categories were estimated using the bootstrap empirical Bayes method. The results of the bootstrapped method showed that signalization significantly decreased Angle+Left-Turn crashes, and its CMF had the highest precision. While, the CMF for Rear-End crashes was unreliable. For KABCO, KABC, and KAB crashes, their CMFs were proved to be reliable for the majority of intersections, but the estimated effect of signalization may be not accurate at some sites. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients.

PubMed

Oberije, Cary; De Ruysscher, Dirk; Houben, Ruud; van de Heuvel, Michel; Uyterlinde, Wilma; Deasy, Joseph O; Belderbos, Jose; Dingemans, Anne-Marie C; Rimner, Andreas; Din, Shaun; Lambin, Philippe

2015-07-15

Although patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing and validating a model that can provide physicians with a survival probability for an individual NSCLC patient. Data from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130). The final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient's survival probability (www.predictcancer.org). The data set can be downloaded at https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048. The prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Utility of a novel risk score for prediction of ventricular tachycardia and cardiac death in chronic Chagas disease - the SEARCH-RIO study

PubMed Central

Benchimol-Barbosa, P.R.; Tura, B.R.; Barbosa, E.C.; Kantharia, B.K.

2013-01-01

The SEARCH-RIO study prospectively investigated electrocardiogram (ECG)-derived variables in chronic Chagas disease (CCD) as predictors of cardiac death and new onset ventricular tachycardia (VT). Cardiac arrhythmia is a major cause of death in CCD, and electrical markers may play a significant role in risk stratification. One hundred clinically stable outpatients with CCD were enrolled in this study. They initially underwent a 12-lead resting ECG, signal-averaged ECG, and 24-h ambulatory ECG. Abnormal Q-waves, filtered QRS duration, intraventricular electrical transients (IVET), 24-h standard deviation of normal RR intervals (SDNN), and VT were assessed. Echocardiograms assessed left ventricular ejection fraction. Predictors of cardiac death and new onset VT were identified in a Cox proportional hazard model. During a mean follow-up of 95.3 months, 36 patients had adverse events: 22 new onset VT (mean±SD, 18.4±4‰/year) and 20 deaths (26.4±1.8‰/year). In multivariate analysis, only Q-wave (hazard ratio, HR=6.7; P<0.001), VT (HR=5.3; P<0.001), SDNN<100 ms (HR=4.0; P=0.006), and IVET+ (HR=3.0; P=0.04) were independent predictors of the composite endpoint of cardiac death and new onset VT. A prognostic score was developed by weighting points proportional to beta coefficients and summing-up: Q-wave=2; VT=2; SDNN<100 ms=1; IVET+=1. Receiver operating characteristic curve analysis optimized the cutoff value at >1. In 10,000 bootstraps, the C-statistic of this novel score was non-inferior to a previously validated (Rassi) score (0.89±0.03 and 0.80±0.05, respectively; test for non-inferiority: P<0.001). In CCD, surface ECG-derived variables are predictors of cardiac death and new onset VT. PMID:24270912

Inference for finite-sample trajectories in dynamic multi-state site-occupancy models using hidden Markov model smoothing

USGS Publications Warehouse

Fiske, Ian J.; Royle, J. Andrew; Gross, Kevin

2014-01-01

Ecologists and wildlife biologists increasingly use latent variable models to study patterns of species occurrence when detection is imperfect. These models have recently been generalized to accommodate both a more expansive description of state than simple presence or absence, and Markovian dynamics in the latent state over successive sampling seasons. In this paper, we write these multi-season, multi-state models as hidden Markov models to find both maximum likelihood estimates of model parameters and finite-sample estimators of the trajectory of the latent state over time. These estimators are especially useful for characterizing population trends in species of conservation concern. We also develop parametric bootstrap procedures that allow formal inference about latent trend. We examine model behavior through simulation, and we apply the model to data from the North American Amphibian Monitoring Program.

How to bootstrap a human communication system.

PubMed

Fay, Nicolas; Arbib, Michael; Garrod, Simon

2013-01-01

How might a human communication system be bootstrapped in the absence of conventional language? We argue that motivated signs play an important role (i.e., signs that are linked to meaning by structural resemblance or by natural association). An experimental study is then reported in which participants try to communicate a range of pre-specified items to a partner using repeated non-linguistic vocalization, repeated gesture, or repeated non-linguistic vocalization plus gesture (but without using their existing language system). Gesture proved more effective (measured by communication success) and more efficient (measured by the time taken to communicate) than non-linguistic vocalization across a range of item categories (emotion, object, and action). Combining gesture and vocalization did not improve performance beyond gesture alone. We experimentally demonstrate that gesture is a more effective means of bootstrapping a human communication system. We argue that gesture outperforms non-linguistic vocalization because it lends itself more naturally to the production of motivated signs. © 2013 Cognitive Science Society, Inc.

Impact of bootstrap current and Landau-fluid closure on ELM crashes and transport

NASA Astrophysics Data System (ADS)

Chen, J. G.; Xu, X. Q.; Ma, C. H.; Lei, Y. A.

2018-05-01

Results presented here are from 6-field Landau-Fluid simulations using shifted circular cross-section tokamak equilibria on BOUT++ framework. Linear benchmark results imply that the collisional and collisionless Landau resonance closures make a little difference on linear growth rate spectra which are quite close to the results with the flux limited Spitzer-Härm parallel flux. Both linear and nonlinear simulations show that the plasma current profile plays dual roles on the peeling-ballooning modes that it can drive the low-n peeling modes and stabilize the high-n ballooning modes. For fixed total pressure and current, as the pedestal current decreases due to the bootstrap current which becomes smaller when the density (collisionality) increases, the operational point is shifted downwards vertically in the Jped - α diagram, resulting in threshold changes of different modes. The bootstrap current can slightly increase radial turbulence spreading range and enhance the energy and particle transports by increasing the perturbed amplitude and broadening cross-phase frequency distribution.

Spontaneous Cerebellar Hematoma: Decision Making in Conscious Adults.

PubMed

Alkosha, Hazem M; Ali, Nabil Mansour

2017-06-01

To detect predictors of the clinical course and outcome of cerebellar hematoma in conscious patients that may help in decision making. This study entails retrospective and prospective review and collection of the demographic, clinical, and radiologic data of 92 patients with cerebellar hematoma presented conscious and initially treated conservatively. Primary outcome was deterioration lower than a Glasgow Coma Scale score of 14 and secondary outcome was Glasgow Outcome Scale score at discharge and 3 months later. Relevant data to primary outcome were used to create a prediction model and derive a risk score. The model was validated using a bootstrap technique and performance measures of the score were presented. Surgical interventions and secondary outcomes were correlated to the score to explore its use in future decision making. Demographic and clinical data showed no relevance to outcome. The relevant initial computed tomography criteria were used to build up the prediction model. A score was derived after the model proved to be valid using internal validation with bootstrapping technique. The score (0-6) had a cutoff value of ≥2, with sensitivity of 93.3% and specificity of 88.0%. It was found to have a significant negative association with the onset of neurologic deterioration, end point Glasgow Coma Scale scores and the Glasgow Outcome Scale scores at discharge. The score was positively correlated to the aggressiveness of surgical interventions and the length of hospital stay. Early definitive management is critical in conscious patients with cerebellar hematomas and can improve outcome. Our proposed score is a simple tool with high discrimination power that may help in timely decision making in those patients. Copyright © 2017 Elsevier Inc. All rights reserved.

Eigentumors for prediction of treatment failure in patients with early-stage breast cancer using dynamic contrast-enhanced MRI: a feasibility study

NASA Astrophysics Data System (ADS)

Chan, H. M.; van der Velden, B. H. M.; E Loo, C.; Gilhuijs, K. G. A.

2017-08-01

We present a radiomics model to discriminate between patients at low risk and those at high risk of treatment failure at long-term follow-up based on eigentumors: principal components computed from volumes encompassing tumors in washin and washout images of pre-treatment dynamic contrast-enhanced (DCE-) MR images. Eigentumors were computed from the images of 563 patients from the MARGINS study. Subsequently, a least absolute shrinkage selection operator (LASSO) selected candidates from the components that contained 90% of the variance of the data. The model for prediction of survival after treatment (median follow-up time 86 months) was based on logistic regression. Receiver operating characteristic (ROC) analysis was applied and area-under-the-curve (AUC) values were computed as measures of training and cross-validated performances. The discriminating potential of the model was confirmed using Kaplan-Meier survival curves and log-rank tests. From the 322 principal components that explained 90% of the variance of the data, the LASSO selected 28 components. The ROC curves of the model yielded AUC values of 0.88, 0.77 and 0.73, for the training, leave-one-out cross-validated and bootstrapped performances, respectively. The bootstrapped Kaplan-Meier survival curves confirmed significant separation for all tumors (P  <  0.0001). Survival analysis on immunohistochemical subgroups shows significant separation for the estrogen-receptor subtype tumors (P  <  0.0001) and the triple-negative subtype tumors (P  =  0.0039), but not for tumors of the HER2 subtype (P  =  0.41). The results of this retrospective study show the potential of early-stage pre-treatment eigentumors for use in prediction of treatment failure of breast cancer.

Population Pharmacokinetics of Hydroxychloroquine in Japanese Patients With Cutaneous or Systemic Lupus Erythematosus.

PubMed

Morita, Shigemichi; Takahashi, Toshiya; Yoshida, Yasushi; Yokota, Naohisa

2016-04-01

Hydroxychloroquine (HCQ) is an effective treatment for patients with cutaneous lupus erythematosus (CLE) or systemic lupus erythematosus (SLE) and has been used for these patients in more than 70 nations. However, in Japan, HCQ has not been approved for CLE or SLE. To establish an appropriate therapeutic regimen and to clarify the pharmacokinetics (PK) of HCQ in Japanese patients with CLE with or without SLE (CLE/SLE), a population pharmacokinetic (PopPK) analysis was performed. In a clinical study of Japanese patients with a diagnosis of CLE irrespective of the presence of SLE, blood and plasma drug concentration-time data receiving multiple oral doses of HCQ sulfate (200-400 mg daily) were analyzed using nonlinear mixed-effects model software. The blood and plasma concentrations of HCQ were analyzed using a high-performance liquid chromatography tandem mass spectrometry method. Model evaluation and validation were performed using goodness-of-fit (GOF) plots, visual predictive check, and a bootstrap. The PopPKs of HCQ in the blood and plasma of 90 Japanese patients with CLE/SLE were well described by a 1-compartment model with first-order absorption and absorption lag time. Body weight was a significant (P < 0.001) covariate of oral clearance of HCQ. The final model was assessed using GOF plots, a bootstrap, and visual predictive check, and this model was appropriate. Simulations based on the final model suggested that the recommended daily doses of HCQ sulfate (200-400 mg) based on the ideal body weight in Japanese patients with CLE/SLE were in the similar concentration ranges. The PopPK models derived from both blood and plasma HCQ concentrations of Japanese patients with CLE/SLE were developed and validated. Based on this study, the dosage regimens of HCQ sulfate for Japanese patients with CLE/SLE should be calculated using the individual ideal body weight.

Unidimensional factor models imply weaker partial correlations than zero-order correlations.

PubMed

van Bork, Riet; Grasman, Raoul P P P; Waldorp, Lourens J

2018-06-01

In this paper we present a new implication of the unidimensional factor model. We prove that the partial correlation between two observed variables that load on one factor given any subset of other observed variables that load on this factor lies between zero and the zero-order correlation between these two observed variables. We implement this result in an empirical bootstrap test that rejects the unidimensional factor model when partial correlations are identified that are either stronger than the zero-order correlation or have a different sign than the zero-order correlation. We demonstrate the use of the test in an empirical data example with data consisting of fourteen items that measure extraversion.

Quantifying the risk of extreme aviation accidents

NASA Astrophysics Data System (ADS)

Das, Kumer Pial; Dey, Asim Kumer

2016-12-01

Air travel is considered a safe means of transportation. But when aviation accidents do occur they often result in fatalities. Fortunately, the most extreme accidents occur rarely. However, 2014 was the deadliest year in the past decade causing 111 plane crashes, and among them worst four crashes cause 298, 239, 162 and 116 deaths. In this study, we want to assess the risk of the catastrophic aviation accidents by studying historical aviation accidents. Applying a generalized Pareto model we predict the maximum fatalities from an aviation accident in future. The fitted model is compared with some of its competitive models. The uncertainty in the inferences are quantified using simulated aviation accident series, generated by bootstrap resampling and Monte Carlo simulations.

Testing a multiple mediation model of Asian American college students' willingness to see a counselor.

PubMed

Kim, Paul Youngbin; Park, Irene J K

2009-07-01

Adapting the theory of reasoned action, the present study examined help-seeking beliefs, attitudes, and intent among Asian American college students (N = 110). A multiple mediation model was tested to see if the relation between Asian values and willingness to see a counselor was mediated by attitudes toward seeking professional psychological help and subjective norm. A bootstrapping procedure was used to test the multiple mediation model. Results indicated that subjective norm was the sole significant mediator of the effect of Asian values on willingness to see a counselor. The findings highlight the importance of social influences on help-seeking intent among Asian American college students.

Precision islands in the Ising and O(N ) models

DOE PAGES

Kos, Filip; Poland, David; Simmons-Duffin, David; ...

2016-08-04

We make precise determinations of the leading scaling dimensions and operator product expansion (OPE) coefficients in the 3d Ising, O(2), and O(3) models from the conformal bootstrap with mixed correlators. We improve on previous studies by scanning over possible relative values of the leading OPE coefficients, which incorporates the physical information that there is only a single operator at a given scaling dimension. The scaling dimensions and OPE coefficients obtained for the 3d Ising model, (Δ σ , Δ ϵ , λ σσϵ , λ ϵϵϵ ) = (0.5181489(10), 1.412625(10), 1.0518537(41), 1.532435(19) , give the most precise determinations of thesemore » quantities to date.« less

Statistical inference, the bootstrap, and neural-network modeling with application to foreign exchange rates.

PubMed

White, H; Racine, J

2001-01-01

We propose tests for individual and joint irrelevance of network inputs. Such tests can be used to determine whether an input or group of inputs "belong" in a particular model, thus permitting valid statistical inference based on estimated feedforward neural-network models. The approaches employ well-known statistical resampling techniques. We conduct a small Monte Carlo experiment showing that our tests have reasonable level and power behavior, and we apply our methods to examine whether there are predictable regularities in foreign exchange rates. We find that exchange rates do appear to contain information that is exploitable for enhanced point prediction, but the nature of the predictive relations evolves through time.

On combination of strict Bayesian principles with model reduction technique or how stochastic model calibration can become feasible for large-scale applications

NASA Astrophysics Data System (ADS)

Oladyshkin, S.; Schroeder, P.; Class, H.; Nowak, W.

2013-12-01

Predicting underground carbon dioxide (CO2) storage represents a challenging problem in a complex dynamic system. Due to lacking information about reservoir parameters, quantification of uncertainties may become the dominant question in risk assessment. Calibration on past observed data from pilot-scale test injection can improve the predictive power of the involved geological, flow, and transport models. The current work performs history matching to pressure time series from a pilot storage site operated in Europe, maintained during an injection period. Simulation of compressible two-phase flow and transport (CO2/brine) in the considered site is computationally very demanding, requiring about 12 days of CPU time for an individual model run. For that reason, brute-force approaches for calibration are not feasible. In the current work, we explore an advanced framework for history matching based on the arbitrary polynomial chaos expansion (aPC) and strict Bayesian principles. The aPC [1] offers a drastic but accurate stochastic model reduction. Unlike many previous chaos expansions, it can handle arbitrary probability distribution shapes of uncertain parameters, and can therefore handle directly the statistical information appearing during the matching procedure. We capture the dependence of model output on these multipliers with the expansion-based reduced model. In our study we keep the spatial heterogeneity suggested by geophysical methods, but consider uncertainty in the magnitude of permeability trough zone-wise permeability multipliers. Next combined the aPC with Bootstrap filtering (a brute-force but fully accurate Bayesian updating mechanism) in order to perform the matching. In comparison to (Ensemble) Kalman Filters, our method accounts for higher-order statistical moments and for the non-linearity of both the forward model and the inversion, and thus allows a rigorous quantification of calibrated model uncertainty. The usually high computational costs of accurate filtering become very feasible for our suggested aPC-based calibration framework. However, the power of aPC-based Bayesian updating strongly depends on the accuracy of prior information. In the current study, the prior assumptions on the model parameters were not satisfactory and strongly underestimate the reservoir pressure. Thus, the aPC-based response surface used in Bootstrap filtering is fitted to a distant and poorly chosen region within the parameter space. Thanks to the iterative procedure suggested in [2] we overcome this drawback with small computational costs. The iteration successively improves the accuracy of the expansion around the current estimation of the posterior distribution. The final result is a calibrated model of the site that can be used for further studies, with an excellent match to the data. References [1] Oladyshkin S. and Nowak W. Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion. Reliability Engineering and System Safety, 106:179-190, 2012. [2] Oladyshkin S., Class H., Nowak W. Bayesian updating via Bootstrap filtering combined with data-driven polynomial chaos expansions: methodology and application to history matching for carbon dioxide storage in geological formations. Computational Geosciences, 17 (4), 671-687, 2013.

Design, development and validation of software for modelling dietary exposure to food chemicals and nutrients.

PubMed

McNamara, C; Naddy, B; Rohan, D; Sexton, J

2003-10-01

The Monte Carlo computational system for stochastic modelling of dietary exposure to food chemicals and nutrients is presented. This system was developed through a European Commission-funded research project. It is accessible as a Web-based application service. The system allows and supports very significant complexity in the data sets used as the model input, but provides a simple, general purpose, linear kernel for model evaluation. Specific features of the system include the ability to enter (arbitrarily) complex mathematical or probabilistic expressions at each and every input data field, automatic bootstrapping on subjects and on subject food intake diaries, and custom kernels to apply brand information such as market share and loyalty to the calculation of food and chemical intake.

Benchmark dose analysis via nonparametric regression modeling

PubMed Central

Piegorsch, Walter W.; Xiong, Hui; Bhattacharya, Rabi N.; Lin, Lizhen

2013-01-01

Estimation of benchmark doses (BMDs) in quantitative risk assessment traditionally is based upon parametric dose-response modeling. It is a well-known concern, however, that if the chosen parametric model is uncertain and/or misspecified, inaccurate and possibly unsafe low-dose inferences can result. We describe a nonparametric approach for estimating BMDs with quantal-response data based on an isotonic regression method, and also study use of corresponding, nonparametric, bootstrap-based confidence limits for the BMD. We explore the confidence limits’ small-sample properties via a simulation study, and illustrate the calculations with an example from cancer risk assessment. It is seen that this nonparametric approach can provide a useful alternative for BMD estimation when faced with the problem of parametric model uncertainty. PMID:23683057

Prospects for steady-state scenarios on JET

NASA Astrophysics Data System (ADS)

Litaudon, X.; Bizarro, J. P. S.; Challis, C. D.; Crisanti, F.; DeVries, P. C.; Lomas, P.; Rimini, F. G.; Tala, T. J. J.; Akers, R.; Andrew, Y.; Arnoux, G.; Artaud, J. F.; Baranov, Yu F.; Beurskens, M.; Brix, M.; Cesario, R.; DeLa Luna, E.; Fundamenski, W.; Giroud, C.; Hawkes, N. C.; Huber, A.; Joffrin, E.; Pitts, R. A.; Rachlew, E.; Reyes-Cortes, S. D. A.; Sharapov, S. E.; Zastrow, K. D.; Zimmermann, O.; JET EFDA contributors, the

2007-09-01

In the 2006 experimental campaign, progress has been made on JET to operate non-inductive scenarios at higher applied powers (31 MW) and density (nl ~ 4 × 1019 m-3), with ITER-relevant safety factor (q95 ~ 5) and plasma shaping, taking advantage of the new divertor capabilities. The extrapolation of the performance using transport modelling benchmarked on the experimental database indicates that the foreseen power upgrade (~45 MW) will allow the development of non-inductive scenarios where the bootstrap current is maximized together with the fusion yield and not, as in present-day experiments, at its expense. The tools for the long-term JET programme are the new ITER-like ICRH antenna (~15 MW), an upgrade of the NB power (35 MW/20 s or 17.5 MW/40 s), a new ITER-like first wall, a new pellet injector for edge localized mode control together with improved diagnostic and control capability. Operation with the new wall will set new constraints on non-inductive scenarios that are already addressed experimentally and in the modelling. The fusion performance and driven current that could be reached at high density and power have been estimated using either 0D or 1-1/2D validated transport models. In the high power case (45 MW), the calculations indicate the potential for the operational space of the non-inductive regime to be extended in terms of current (~2.5 MA) and density (nl > 5 × 1019 m-3), with high βN (βN > 3.0) and a fraction of the bootstrap current within 60-70% at high toroidal field (~3.5 T).

Cosmological explosions from cold dark matter perturbations

NASA Technical Reports Server (NTRS)

Scherrer, Robert J.

1992-01-01

The cosmological-explosion model is examined for a universe dominated by cold dark matter in which explosion seeds are produced from the growth of initial density perturbations of a given form. Fragmentation of the exploding shells is dominated by the dark-matter potential wells rather than the self-gravity of the shells, and particular conditions are required for the explosions to bootstrap up to very large scales. The final distribution of dark matter is strongly correlated with the baryons on small scales, but uncorrelated on large scales.

Classifier performance prediction for computer-aided diagnosis using a limited dataset.

PubMed

Sahiner, Berkman; Chan, Heang-Ping; Hadjiiski, Lubomir

2008-04-01

In a practical classifier design problem, the true population is generally unknown and the available sample is finite-sized. A common approach is to use a resampling technique to estimate the performance of the classifier that will be trained with the available sample. We conducted a Monte Carlo simulation study to compare the ability of the different resampling techniques in training the classifier and predicting its performance under the constraint of a finite-sized sample. The true population for the two classes was assumed to be multivariate normal distributions with known covariance matrices. Finite sets of sample vectors were drawn from the population. The true performance of the classifier is defined as the area under the receiver operating characteristic curve (AUC) when the classifier designed with the specific sample is applied to the true population. We investigated methods based on the Fukunaga-Hayes and the leave-one-out techniques, as well as three different types of bootstrap methods, namely, the ordinary, 0.632, and 0.632+ bootstrap. The Fisher's linear discriminant analysis was used as the classifier. The dimensionality of the feature space was varied from 3 to 15. The sample size n2 from the positive class was varied between 25 and 60, while the number of cases from the negative class was either equal to n2 or 3n2. Each experiment was performed with an independent dataset randomly drawn from the true population. Using a total of 1000 experiments for each simulation condition, we compared the bias, the variance, and the root-mean-squared error (RMSE) of the AUC estimated using the different resampling techniques relative to the true AUC (obtained from training on a finite dataset and testing on the population). Our results indicated that, under the study conditions, there can be a large difference in the RMSE obtained using different resampling methods, especially when the feature space dimensionality is relatively large and the sample size is small. Under this type of conditions, the 0.632 and 0.632+ bootstrap methods have the lowest RMSE, indicating that the difference between the estimated and the true performances obtained using the 0.632 and 0.632+ bootstrap will be statistically smaller than those obtained using the other three resampling methods. Of the three bootstrap methods, the 0.632+ bootstrap provides the lowest bias. Although this investigation is performed under some specific conditions, it reveals important trends for the problem of classifier performance prediction under the constraint of a limited dataset.

Assessing neural activity related to decision-making through flexible odds ratio curves and their derivatives.

PubMed

Roca-Pardiñas, Javier; Cadarso-Suárez, Carmen; Pardo-Vazquez, Jose L; Leboran, Victor; Molenberghs, Geert; Faes, Christel; Acuña, Carlos

2011-06-30

It is well established that neural activity is stochastically modulated over time. Therefore, direct comparisons across experimental conditions and determination of change points or maximum firing rates are not straightforward. This study sought to compare temporal firing probability curves that may vary across groups defined by different experimental conditions. Odds-ratio (OR) curves were used as a measure of comparison, and the main goal was to provide a global test to detect significant differences of such curves through the study of their derivatives. An algorithm is proposed that enables ORs based on generalized additive models, including factor-by-curve-type interactions to be flexibly estimated. Bootstrap methods were used to draw inferences from the derivatives curves, and binning techniques were applied to speed up computation in the estimation and testing processes. A simulation study was conducted to assess the validity of these bootstrap-based tests. This methodology was applied to study premotor ventral cortex neural activity associated with decision-making. The proposed statistical procedures proved very useful in revealing the neural activity correlates of decision-making in a visual discrimination task. Copyright © 2011 John Wiley & Sons, Ltd.

Quantification of Uncertainty in the Flood Frequency Analysis

NASA Astrophysics Data System (ADS)

Kasiapillai Sudalaimuthu, K.; He, J.; Swami, D.

2017-12-01

Flood frequency analysis (FFA) is usually carried out for planning and designing of water resources and hydraulic structures. Owing to the existence of variability in sample representation, selection of distribution and estimation of distribution parameters, the estimation of flood quantile has been always uncertain. Hence, suitable approaches must be developed to quantify the uncertainty in the form of prediction interval as an alternate to deterministic approach. The developed framework in the present study to include uncertainty in the FFA discusses a multi-objective optimization approach to construct the prediction interval using ensemble of flood quantile. Through this approach, an optimal variability of distribution parameters is identified to carry out FFA. To demonstrate the proposed approach, annual maximum flow data from two gauge stations (Bow river at Calgary and Banff, Canada) are used. The major focus of the present study was to evaluate the changes in magnitude of flood quantiles due to the recent extreme flood event occurred during the year 2013. In addition, the efficacy of the proposed method was further verified using standard bootstrap based sampling approaches and found that the proposed method is reliable in modeling extreme floods as compared to the bootstrap methods.

Causal nexus between energy consumption and carbon dioxide emission for Malaysia using maximum entropy bootstrap approach.

PubMed

Gul, Sehrish; Zou, Xiang; Hassan, Che Hashim; Azam, Muhammad; Zaman, Khalid

2015-12-01

This study investigates the relationship between energy consumption and carbon dioxide emission in the causal framework, as the direction of causality remains has a significant policy implication for developed and developing countries. The study employed maximum entropy bootstrap (Meboot) approach to examine the causal nexus between energy consumption and carbon dioxide emission using bivariate as well as multivariate framework for Malaysia, over a period of 1975-2013. This is a unified approach without requiring the use of conventional techniques based on asymptotical theory such as testing for possible unit root and cointegration. In addition, it can be applied in the presence of non-stationary of any type including structural breaks without any type of data transformation to achieve stationary. Thus, it provides more reliable and robust inferences which are insensitive to time span as well as lag length used. The empirical results show that there is a unidirectional causality running from energy consumption to carbon emission both in the bivariate model and multivariate framework, while controlling for broad money supply and population density. The results indicate that Malaysia is an energy-dependent country and hence energy is stimulus to carbon emissions.

Uncertainty Estimates of Psychoacoustic Thresholds Obtained from Group Tests

NASA Technical Reports Server (NTRS)

Rathsam, Jonathan; Christian, Andrew

2016-01-01

Adaptive psychoacoustic test methods, in which the next signal level depends on the response to the previous signal, are the most efficient for determining psychoacoustic thresholds of individual subjects. In many tests conducted in the NASA psychoacoustic labs, the goal is to determine thresholds representative of the general population. To do this economically, non-adaptive testing methods are used in which three or four subjects are tested at the same time with predetermined signal levels. This approach requires us to identify techniques for assessing the uncertainty in resulting group-average psychoacoustic thresholds. In this presentation we examine the Delta Method of frequentist statistics, the Generalized Linear Model (GLM), the Nonparametric Bootstrap, a frequentist method, and Markov Chain Monte Carlo Posterior Estimation and a Bayesian approach. Each technique is exercised on a manufactured, theoretical dataset and then on datasets from two psychoacoustics facilities at NASA. The Delta Method is the simplest to implement and accurate for the cases studied. The GLM is found to be the least robust, and the Bootstrap takes the longest to calculate. The Bayesian Posterior Estimate is the most versatile technique examined because it allows the inclusion of prior information.

Relationships Between Self-Efficacy, Self-Esteem and Procrastination in Undergraduate Psychology Students

PubMed Central

Hajloo, Nader

2014-01-01

Objective: The present study aimed to review the relationships between procrastination and two self-factors self-efficacy and self-esteem. Methods: Participants were 140 undergraduates Psychology students enrolled in Mohagheg Ardabili University, Ardabil, Iran. Instruments used for collecting the required data were the student-version of the General Procrastination Scale (GP-S), General Self-Efficacy Scale (GSE) and Rosenberg’s Self-Esteem Scale (SES). Results: Using causal modeling, two models were compared; a model with self-esteem as a mediator versus a model with procrastination as a mediator. The self-esteem mediator model accounted for 21% of the variance in procrastination. The significance of the mediation effect was found by bootstrapping method. Conclusion: The relationship of procrastination with self-esteem and self-efficacy was revealed among undergraduate psychology students. PMID:25780374

Relationships between self-efficacy, self-esteem and procrastination in undergraduate psychology students.

PubMed

Hajloo, Nader

2014-01-01

The present study aimed to review the relationships between procrastination and two self-factors self-efficacy and self-esteem. Participants were 140 undergraduates Psychology students enrolled in Mohagheg Ardabili University, Ardabil, Iran. Instruments used for collecting the required data were the student-version of the General Procrastination Scale (GP-S), General Self-Efficacy Scale (GSE) and Rosenberg's Self-Esteem Scale (SES). Using causal modeling, two models were compared; a model with self-esteem as a mediator versus a model with procrastination as a mediator. The self-esteem mediator model accounted for 21% of the variance in procrastination. The significance of the mediation effect was found by bootstrapping method. The relationship of procrastination with self-esteem and self-efficacy was revealed among undergraduate psychology students.

Soft sensor modeling based on variable partition ensemble method for nonlinear batch processes

NASA Astrophysics Data System (ADS)

Wang, Li; Chen, Xiangguang; Yang, Kai; Jin, Huaiping

2017-01-01

Batch processes are always characterized by nonlinear and system uncertain properties, therefore, the conventional single model may be ill-suited. A local learning strategy soft sensor based on variable partition ensemble method is developed for the quality prediction of nonlinear and non-Gaussian batch processes. A set of input variable sets are obtained by bootstrapping and PMI criterion. Then, multiple local GPR models are developed based on each local input variable set. When a new test data is coming, the posterior probability of each best performance local model is estimated based on Bayesian inference and used to combine these local GPR models to get the final prediction result. The proposed soft sensor is demonstrated by applying to an industrial fed-batch chlortetracycline fermentation process.

Concept Innateness, Concept Continuity, and Bootstrapping

PubMed Central

Carey, Susan

2011-01-01

The commentators raised issues relevant to all three important theses of The Origin of Concepts (TOOC). Some questioned the very existence of innate representational primitives, and others questioned my claims about their richness and whether they should be thought of as concepts. Some questioned the existence of conceptual discontinuity in the course of knowledge acquisition and others argued that discontinuity is much more common than portrayed in TOOC. Some raised issues with my characterization of Quinian bootstrapping, and others questioned the dual factor theory of concepts motivated by my picture of conceptual development. PMID:23264705

Crossing symmetry in alpha space

NASA Astrophysics Data System (ADS)

Hogervorst, Matthijs; van Rees, Balt C.

2017-11-01

We initiate the study of the conformal bootstrap using Sturm-Liouville theory, specializing to four-point functions in one-dimensional CFTs. We do so by decomposing conformal correlators using a basis of eigenfunctions of the Casimir which are labeled by a complex number α. This leads to a systematic method for computing conformal block decompositions. Analyzing bootstrap equations in alpha space turns crossing symmetry into an eigenvalue problem for an integral operator K. The operator K is closely related to the Wilson transform, and some of its eigenfunctions can be found in closed form.

Direct measurement of fast transients by using boot-strapped waveform averaging

NASA Astrophysics Data System (ADS)

Olsson, Mattias; Edman, Fredrik; Karki, Khadga Jung

2018-03-01

An approximation to coherent sampling, also known as boot-strapped waveform averaging, is presented. The method uses digital cavities to determine the condition for coherent sampling. It can be used to increase the effective sampling rate of a repetitive signal and the signal to noise ratio simultaneously. The method is demonstrated by using it to directly measure the fluorescence lifetime from Rhodamine 6G by digitizing the signal from a fast avalanche photodiode. The obtained lifetime of 4.0 ns is in agreement with the known values.

Gratitude mediates the effect of emotional intelligence on subjective well-being: A structural equation modeling analysis.

PubMed

Geng, Yuan

2016-11-01

This study investigated the relationship among emotional intelligence, gratitude, and subjective well-being in a sample of university students. A total of 365 undergraduates completed the emotional intelligence scale, the gratitude questionnaire, and the subjective well-being measures. The results of the structural equation model showed that emotional intelligence is positively associated with gratitude and subjective well-being, that gratitude is positively associated with subjective well-being, and that gratitude partially mediates the positive relationship between emotional intelligence and subjective well-being. Bootstrap test results also revealed that emotional intelligence has a significant indirect effect on subjective well-being through gratitude.

Studies of transformational leadership: evaluating two alternative models of trust and satisfaction.

PubMed

Yang, Yi-Feng

2014-06-01

This study evaluates the influence of leadership style and employee trust in their leaders on job satisfaction. 341 personnel (164 men, 177 women; M age = 33.5 yr., SD = 5.1) from four large insurance companies in Taiwan completed the transformational leadership behavior inventory, the leadership trust scale and a short version of the Minnesota (Job) Satisfaction Questionnaire. A bootstrapping mediation and structural equation modeling revealed that the effect of transformational leadership on job satisfaction was mediated by leadership trust. This study highlights the importance of leadership trust in leadership-satisfaction relationships, and provides managers with practical ways to enhance job satisfaction.

Effects of the TRPV1 antagonist ABT-102 on body temperature in healthy volunteers: pharmacokinetic/pharmacodynamic analysis of three phase 1 trials

PubMed Central

Othman, Ahmed A; Nothaft, Wolfram; Awni, Walid M; Dutta, Sandeep

2013-01-01

Aim To characterize quantitatively the relationship between ABT-102, a potent and selective TRPV1 antagonist, exposure and its effects on body temperature in humans using a population pharmacokinetic/pharmacodynamic modelling approach. Methods Serial pharmacokinetic and body temperature (oral or core) measurements from three double-blind, randomized, placebo-controlled studies [single dose (2, 6, 18, 30 and 40 mg, solution formulation), multiple dose (2, 4 and 8 mg twice daily for 7 days, solution formulation) and multiple-dose (1, 2 and 4 mg twice daily for 7 days, solid dispersion formulation)] were analyzed. nonmem was used for model development and the model building steps were guided by pre-specified diagnostic and statistical criteria. The final model was qualified using non-parametric bootstrap and visual predictive check. Results The developed body temperature model included additive components of baseline, circadian rhythm (cosine function of time) and ABT-102 effect (Emax function of plasma concentration) with tolerance development (decrease in ABT-102 Emax over time). Type of body temperature measurement (oral vs. core) was included as a fixed effect on baseline, amplitude of circadian rhythm and residual error. The model estimates (95% bootstrap confidence interval) were: baseline oral body temperature, 36.3 (36.3, 36.4)°C; baseline core body temperature, 37.0 (37.0, 37.1)°C; oral circadian amplitude, 0.25 (0.22, 0.28)°C; core circadian amplitude, 0.31 (0.28, 0.34)°C; circadian phase shift, 7.6 (7.3, 7.9) h; ABT-102 Emax, 2.2 (1.9, 2.7)°C; ABT-102 EC50, 20 (15, 28) ng ml−1; tolerance T50, 28 (20, 43) h. Conclusions At exposures predicted to exert analgesic activity in humans, the effect of ABT-102 on body temperature is estimated to be 0.6 to 0.8°C. This effect attenuates within 2 to 3 days of dosing. PMID:22966986

Development of population pharmacokinetics model of icotinib with non-linear absorption characters in healthy Chinese volunteers to assess the CYP2C19 polymorphism and food-intake effect.

PubMed

Hu, Pei; Chen, Jia; Liu, Dongyang; Zheng, Xin; Zhao, Qian; Jiang, Ji

2015-07-01

Icotinib is a potent and selective inhibitor of epidermal growth factor receptors (EGFR) approved to treat non-small cell lung cancer (NSCLC). However, its high variability may impede its application. The objectives of this analysis were to assess plasma pharmacokinetics and identify covariates that may explain variability in icotinib absorption and/or disposition following single dose of icotinib in healthy volunteers. Data from two clinical studies (n = 22) were analyzed. One study was designed as three-period and Latin-squared (six sequence) trial to evaluate dose proportionality, and the other one was designed as two-way crossover trial to evaluate food effect on pharmacokinetics (PK) characters. Icotinib concentrations in plasma were analyzed using non-linear mixed-effects model (NONMEM) method. The model was used to assess influence of food, demographic characteristics, measurements of blood biochemistry, and CYP2C19 genotype on PK characters of icotinib in humans. The final model was diagnosed by goodness-of-fit plots and evaluated by visual predictive check (VPC) and bootstrap methods. A two-compartment model with saturated absorption character was developed to capture icotinib pharmacokinetics. Typical value of clearance, distribution clearance, central volume of distribution, maximum absorption rate were 29.5 L/h, 24.9 L/h, 18.5 L, 122.2 L and 204,245 μg/h, respectively. When icotinib was administrated with food, bioavailability was estimated to be increased by 48%. Inter-occasion variability was identified to affect on maximum absorption rate constant in food-effect study. CL was identified to be significantly influenced by age, albumin concentration (ALB), and CYP2C19 genotype. No obvious bias was found by VPC and bootstrap methods. The developed model can capture icotinib pharmacokinetics well in healthy volunteers. Food intake can increase icotinib exposure. Three covariates, age, albumin concentration, and CYP2C19 genotype, were identified to significantly affect icotinib PK profiles in healthy subjects.

Assessing operating characteristics of CAD algorithms in the absence of a gold standard

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roy Choudhury, Kingshuk; Paik, David S.; Yi, Chin A.

2010-04-15

Purpose: The authors examine potential bias when using a reference reader panel as ''gold standard'' for estimating operating characteristics of CAD algorithms for detecting lesions. As an alternative, the authors propose latent class analysis (LCA), which does not require an external gold standard to evaluate diagnostic accuracy. Methods: A binomial model for multiple reader detections using different diagnostic protocols was constructed, assuming conditional independence of readings given true lesion status. Operating characteristics of all protocols were estimated by maximum likelihood LCA. Reader panel and LCA based estimates were compared using data simulated from the binomial model for a range ofmore » operating characteristics. LCA was applied to 36 thin section thoracic computed tomography data sets from the Lung Image Database Consortium (LIDC): Free search markings of four radiologists were compared to markings from four different CAD assisted radiologists. For real data, bootstrap-based resampling methods, which accommodate dependence in reader detections, are proposed to test of hypotheses of differences between detection protocols. Results: In simulation studies, reader panel based sensitivity estimates had an average relative bias (ARB) of -23% to -27%, significantly higher (p-value <0.0001) than LCA (ARB -2% to -6%). Specificity was well estimated by both reader panel (ARB -0.6% to -0.5%) and LCA (ARB 1.4%-0.5%). Among 1145 lesion candidates LIDC considered, LCA estimated sensitivity of reference readers (55%) was significantly lower (p-value 0.006) than CAD assisted readers' (68%). Average false positives per patient for reference readers (0.95) was not significantly lower (p-value 0.28) than CAD assisted readers' (1.27). Conclusions: Whereas a gold standard based on a consensus of readers may substantially bias sensitivity estimates, LCA may be a significantly more accurate and consistent means for evaluating diagnostic accuracy.« less

Evaluating experimental design for soil-plant model selection using a Bootstrap Filter and Bayesian model averaging

NASA Astrophysics Data System (ADS)

Wöhling, T.; Schöniger, A.; Geiges, A.; Nowak, W.; Gayler, S.

2013-12-01

The objective selection of appropriate models for realistic simulations of coupled soil-plant processes is a challenging task since the processes are complex, not fully understood at larger scales, and highly non-linear. Also, comprehensive data sets are scarce, and measurements are uncertain. In the past decades, a variety of different models have been developed that exhibit a wide range of complexity regarding their approximation of processes in the coupled model compartments. We present a method for evaluating experimental design for maximum confidence in the model selection task. The method considers uncertainty in parameters, measurements and model structures. Advancing the ideas behind Bayesian Model Averaging (BMA), we analyze the changes in posterior model weights and posterior model choice uncertainty when more data are made available. This allows assessing the power of different data types, data densities and data locations in identifying the best model structure from among a suite of plausible models. The models considered in this study are the crop models CERES, SUCROS, GECROS and SPASS, which are coupled to identical routines for simulating soil processes within the modelling framework Expert-N. The four models considerably differ in the degree of detail at which crop growth and root water uptake are represented. Monte-Carlo simulations were conducted for each of these models considering their uncertainty in soil hydraulic properties and selected crop model parameters. Using a Bootstrap Filter (BF), the models were then conditioned on field measurements of soil moisture, matric potential, leaf-area index, and evapotranspiration rates (from eddy-covariance measurements) during a vegetation period of winter wheat at a field site at the Swabian Alb in Southwestern Germany. Following our new method, we derived model weights when using all data or different subsets thereof. We discuss to which degree the posterior mean outperforms the prior mean and all individual posterior models, how informative the data types were for reducing prediction uncertainty of evapotranspiration and deep drainage, and how well the model structure can be identified based on the different data types and subsets. We further analyze the impact of measurement uncertainty und systematic model errors on the effective sample size of the BF and the resulting model weights.

Evaluation of the Williams-type spring wheat model in North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Leduc, S. (Principal Investigator)

1982-01-01

The Williams type model, developed similarly to previous models of C.V.D. Williams, uses monthly temperature and precipitation data as well as soil and topological variables to predict the yield of the spring wheat crop. The models are statistically developed using the regression technique. Eight model characteristics are examined in the evaluation of the model. Evaluation is at the crop reporting district level, the state level and for the entire region. A ten year bootstrap test was the basis of the statistical evaluation. The accuracy and current indication of modeled yield reliability could show improvement. There is great variability in the bias measured over the districts, but there is a slight overall positive bias. The model estimates for the east central crop reporting district in Minnesota are not accurate. The estimate of yield for 1974 were inaccurate for all of the models.

Traffic fatality indicators in Brazil: State diagnosis based on data envelopment analysis research.

PubMed

Bastos, Jorge Tiago; Shen, Yongjun; Hermans, Elke; Brijs, Tom; Wets, Geert; Ferraz, Antonio Clóvis Pinto

2015-08-01

The intense economic growth experienced by Brazil in recent decades and its consequent explosive motorization process have evidenced an undesirable impact: the increasing and unbroken trend in traffic fatality numbers. In order to contribute to road safety diagnosis on a national level, this study presents a research into two main indicators available in Brazil: mortality rate (represented by fatalities per capita) and fatality rate (represented by two sub-indicators, i.e., fatalities per vehicle and fatalities per vehicle kilometer traveled). These indicators were aggregated into a composite indicator or index through a multiple layer data envelopment analysis (DEA) composite indicator model, which looks for the optimum combination of indicators' weights for each decision-making unit, in this case 27 Brazilian states. The index score represents the road safety performance, based on which a ranking of states can be made. Since such a model has never been applied for road safety evaluation in Brazil, its parameters were calibrated based on the experience of more consolidated European Union research in ranking its member countries using DEA techniques. Secondly, cluster analysis was conducted aiming to provide more realistic performance comparisons and, finally, the sensitivity of the results was measured through a bootstrapping method application. It can be concluded that by combining fatality indicators, defining clusters and applying bootstrapping procedures a trustworthy ranking can be created, which is valuable for nationwide road safety planning. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cost-effectiveness of surgical decompression for space-occupying hemispheric infarction.

PubMed

Hofmeijer, Jeannette; van der Worp, H Bart; Kappelle, L Jaap; Eshuis, Sara; Algra, Ale; Greving, Jacoba P

2013-10-01

Surgical decompression reduces mortality and increases the probability of a favorable functional outcome after space-occupying hemispheric infarction. Its cost-effectiveness is uncertain. We assessed clinical outcomes, costs, and cost-effectiveness for the first 3 years in patients who were randomized to surgical decompression or best medical treatment within 48 hours after symptom onset in the Hemicraniectomy After Middle Cerebral Artery Infarction With Life-Threatening Edema Trial (HAMLET). Data on medical consumption were derived from case record files, hospital charts, and general practitioners. We calculated costs per quality-adjusted life year (QALY). Uncertainty was assessed with bootstrapping. A Markov model was constructed to estimate costs and health outcomes after 3 years. Of 39 patients enrolled within 48 hours, 21 were randomized to surgical decompression. After 3 years, 5 surgical (24%) and 14 medical patients (78%) had died. In the first 3 years after enrollment, operated patients had more QALYs than medically treated patients (mean difference, 1.0 QALY [95% confidence interval, 0.6-1.4]), but at higher costs (mean difference, €127,000 [95% confidence interval, 73,100-181,000]), indicating incremental costs of €127,000 per QALY gained. Ninety-eight percent of incremental cost-effectiveness ratios replicated by bootstrapping were >€80,000 per QALY gained. Markov modeling suggested costs of ≈€60,000 per QALY gained for a patient's lifetime. Surgical decompression for space-occupying infarction results in an increase in QALYs, but at very high costs. http://www.controlled-trials.com. Unique identifier: ISRCTN94237756.

Advances in the high bootstrap fraction regime on DIII-D towards the Q  =  5 mission of ITER steady state

NASA Astrophysics Data System (ADS)

Qian, J. P.; Garofalo, A. M.; Gong, X. Z.; Ren, Q. L.; Ding, S. Y.; Solomon, W. M.; Xu, G. S.; Grierson, B. A.; Guo, W. F.; Holcomb, C. T.; McClenaghan, J.; McKee, G. R.; Pan, C. K.; Huang, J.; Staebler, G. M.; Wan, B. N.

2017-05-01

Recent EAST/DIII-D joint experiments on the high poloidal beta {β\\text{P}} regime in DIII-D have extended operation with internal transport barriers (ITBs) and excellent energy confinement (H 98y2 ~ 1.6) to higher plasma current, for lower q 95  ⩽  7.0, and more balanced neutral beam injection (NBI) (torque injection  <  2 Nm), for lower plasma rotation than previous results (Garofalo et al, IAEA 2014, Gong et al 2014 IAEA Int. Conf. on Fusion Energy). Transport analysis and experimental measurements at low toroidal rotation suggest that the E  ×  B shear effect is not key to the ITB formation in these high {β\\text{P}} discharges. Experiments and TGLF modeling show that the Shafranov shift has a key stabilizing effect on turbulence. Extrapolation of the DIII-D results using a 0D model shows that with the improved confinement, the high bootstrap fraction regime could achieve fusion gain Q  =  5 in ITER at {β\\text{N}} ~ 2.9 and q 95 ~ 7. With the optimization of q(0), the required improved confinement is achievable when using 1.5D TGLF-SAT1 for transport simulations. Results reported in this paper suggest that the DIII-D high {β\\text{P}} scenario could be a candidate for ITER steady state operation.

Filipino Americans and racism: A multiple mediation model of coping.

PubMed

Alvarez, Alvin N; Juang, Linda P

2010-04-01

Although the literature on Asian Americans and racism has been emerging, few studies have examined how coping influences one's encounters with racism. To advance the literature, the present study focused on the psychological impact of Filipino Americans' experiences with racism and the role of coping as a mediator using a community-based sample of adults (N = 199). Two multiple mediation models were used to examine the mediating effects of active, avoidance, support-seeking, and forbearance coping on the relationship between perceived racism and psychological distress and self-esteem, respectively. Separate analyses were also conducted for men and women given differences in coping utilization. For men, a bootstrap procedure indicated that active, support-seeking, and avoidance coping were mediators of the relationship between perceived racism and psychological distress. Active coping was negatively associated with psychological distress, whereas both support seeking and avoidance were positively associated with psychological distress. A second bootstrap procedure for men indicated that active and avoidance coping mediated the relationship between perceived racism and self-esteem such that active coping was positively associated with self-esteem, and avoidance was negatively associated with self-esteem. For women, only avoidance coping had a significant mediating effect that was associated with elevations in psychological distress and decreases in self-esteem. The results highlight the importance of examining the efficacy of specific coping responses to racism and the need to differentiate between the experiences of men and women. PsycINFO Database Record (c) 2010 APA, all rights reserved.

A study of hierarchical structure on South China industrial electricity-consumption correlation

NASA Astrophysics Data System (ADS)

Yao, Can-Zhong; Lin, Ji-Nan; Liu, Xiao-Feng

2016-02-01

Based on industrial electricity-consumption data of five southern provinces of China from 2005 to 2013, we study the industrial correlation mechanism with MST (minimal spanning tree) and HT (hierarchical tree) models. First, we comparatively analyze the industrial electricity-consumption correlation structure in pre-crisis and after-crisis period using MST model and Bootstrap technique of statistical reliability test of links. Results exhibit that all industrial electricity-consumption trees of five southern provinces of China in pre-crisis and after-crisis time are in formation of chain, and the "center-periphery structure" of those chain-like trees is consistent with industrial specialization in classical industrial chain theory. Additionally, the industrial structure of some provinces is reorganized and transferred in pre-crisis and after-crisis time. Further, the comparative analysis with hierarchical tree and Bootstrap technique demonstrates that as for both observations of GD and overall NF, the industrial electricity-consumption correlation is non-significant clustered in pre-crisis period, whereas it turns significant clustered in after-crisis time. Therefore we propose that in perspective of electricity-consumption, their industrial structures are directed to optimized organization and global correlation. Finally, the analysis of distance of HTs verifies that industrial reorganization and development may strengthen market integration, coordination and correlation of industrial production. Except GZ, other four provinces have a shorter distance of industrial electricity-consumption correlation in after-crisis period, revealing a better performance of regional specialization and integration.

Soft-tissue anatomy of the extant hominoids: a review and phylogenetic analysis

PubMed Central

Gibbs, S; Collard, M; Wood, B

2002-01-01

This paper reports the results of a literature search for information about the soft-tissue anatomy of the extant non-human hominoid genera, Pan, Gorilla, Pongo and Hylobates, together with the results of a phylogenetic analysis of these data plus comparable data for Homo. Information on the four extant non-human hominoid genera was located for 240 out of the 1783 soft-tissue structures listed in the Nomina Anatomica. Numerically these data are biased so that information about some systems (e.g. muscles) and some regions (e.g. the forelimb) are over-represented, whereas other systems and regions (e.g. the veins and the lymphatics of the vascular system, the head region) are either under-represented or not represented at all. Screening to ensure that the data were suitable for use in a phylogenetic analysis reduced the number of eligible soft-tissue structures to 171. These data, together with comparable data for modern humans, were converted into discontinuous character states suitable for phylogenetic analysis and then used to construct a taxon-by-character matrix. This matrix was used in two tests of the hypothesis that soft-tissue characters can be relied upon to reconstruct hominoid phylogenetic relationships. In the first, parsimony analysis was used to identify cladograms requiring the smallest number of character state changes. In the second, the phylogenetic bootstrap was used to determine the confidence intervals of the most parsimonious clades. The parsimony analysis yielded a single most parsimonious cladogram that matched the molecular cladogram. Similarly the bootstrap analysis yielded clades that were compatible with the molecular cladogram; a (Homo, Pan) clade was supported by 95% of the replicates, and a (Gorilla, Pan, Homo) clade by 96%. These are the first hominoid morphological data to provide statistically significant support for the clades favoured by the molecular evidence. PMID:11833653

Gyrokinetic neoclassical study of the bootstrap current in the tokamak edge pedestal with fully non-linear Coulomb collisions

DOE PAGES

Hager, Robert; Chang, C. S.

2016-04-08

As a follow-up on the drift-kinetic study of the non-local bootstrap current in the steep edge pedestal of tokamak plasma by Koh et al. [Phys. Plasmas 19, 072505 (2012)], a gyrokinetic neoclassical study is performed with gyrokinetic ions and drift-kinetic electrons. Besides the gyrokinetic improvement of ion physics from the drift-kinetic treatment, a fully non-linear Fokker-Planck collision operator—that conserves mass, momentum, and energy—is used instead of Koh et al.'s linearized collision operator in consideration of the possibility that the ion distribution function is non-Maxwellian in the steep pedestal. An inaccuracy in Koh et al.'s result is found in the steepmore » edge pedestal that originated from a small error in the collisional momentum conservation. The present study concludes that (1) the bootstrap current in the steep edge pedestal is generally smaller than what has been predicted from the small banana-width (local) approximation [e.g., Sauter et al., Phys. Plasmas 6, 2834 (1999) and Belli et al., Plasma Phys. Controlled Fusion 50, 095010 (2008)], (2) the plasma flow evaluated from the local approximation can significantly deviate from the non-local results, and (3) the bootstrap current in the edge pedestal, where the passing particle region is small, can be dominantly carried by the trapped particles in a broad trapped boundary layer. In conclusion, a new analytic formula based on numerous gyrokinetic simulations using various magnetic equilibria and plasma profiles with self-consistent Grad-Shafranov solutions is constructed.« less

Gyrokinetic neoclassical study of the bootstrap current in the tokamak edge pedestal with fully non-linear Coulomb collisions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hager, Robert; Chang, C. S.

As a follow-up on the drift-kinetic study of the non-local bootstrap current in the steep edge pedestal of tokamak plasma by Koh et al. [Phys. Plasmas 19, 072505 (2012)], a gyrokinetic neoclassical study is performed with gyrokinetic ions and drift-kinetic electrons. Besides the gyrokinetic improvement of ion physics from the drift-kinetic treatment, a fully non-linear Fokker-Planck collision operator—that conserves mass, momentum, and energy—is used instead of Koh et al.'s linearized collision operator in consideration of the possibility that the ion distribution function is non-Maxwellian in the steep pedestal. An inaccuracy in Koh et al.'s result is found in the steepmore » edge pedestal that originated from a small error in the collisional momentum conservation. The present study concludes that (1) the bootstrap current in the steep edge pedestal is generally smaller than what has been predicted from the small banana-width (local) approximation [e.g., Sauter et al., Phys. Plasmas 6, 2834 (1999) and Belli et al., Plasma Phys. Controlled Fusion 50, 095010 (2008)], (2) the plasma flow evaluated from the local approximation can significantly deviate from the non-local results, and (3) the bootstrap current in the edge pedestal, where the passing particle region is small, can be dominantly carried by the trapped particles in a broad trapped boundary layer. In conclusion, a new analytic formula based on numerous gyrokinetic simulations using various magnetic equilibria and plasma profiles with self-consistent Grad-Shafranov solutions is constructed.« less

Gyrokinetic neoclassical study of the bootstrap current in the tokamak edge pedestal with fully non-linear Coulomb collisions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hager, Robert, E-mail: rhager@pppl.gov; Chang, C. S., E-mail: cschang@pppl.gov

As a follow-up on the drift-kinetic study of the non-local bootstrap current in the steep edge pedestal of tokamak plasma by Koh et al. [Phys. Plasmas 19, 072505 (2012)], a gyrokinetic neoclassical study is performed with gyrokinetic ions and drift-kinetic electrons. Besides the gyrokinetic improvement of ion physics from the drift-kinetic treatment, a fully non-linear Fokker-Planck collision operator—that conserves mass, momentum, and energy—is used instead of Koh et al.'s linearized collision operator in consideration of the possibility that the ion distribution function is non-Maxwellian in the steep pedestal. An inaccuracy in Koh et al.'s result is found in the steepmore » edge pedestal that originated from a small error in the collisional momentum conservation. The present study concludes that (1) the bootstrap current in the steep edge pedestal is generally smaller than what has been predicted from the small banana-width (local) approximation [e.g., Sauter et al., Phys. Plasmas 6, 2834 (1999) and Belli et al., Plasma Phys. Controlled Fusion 50, 095010 (2008)], (2) the plasma flow evaluated from the local approximation can significantly deviate from the non-local results, and (3) the bootstrap current in the edge pedestal, where the passing particle region is small, can be dominantly carried by the trapped particles in a broad trapped boundary layer. A new analytic formula based on numerous gyrokinetic simulations using various magnetic equilibria and plasma profiles with self-consistent Grad-Shafranov solutions is constructed.« less

Effect of non-normality on test statistics for one-way independent groups designs.

PubMed

Cribbie, Robert A; Fiksenbaum, Lisa; Keselman, H J; Wilcox, Rand R

2012-02-01

The data obtained from one-way independent groups designs is typically non-normal in form and rarely equally variable across treatment populations (i.e., population variances are heterogeneous). Consequently, the classical test statistic that is used to assess statistical significance (i.e., the analysis of variance F test) typically provides invalid results (e.g., too many Type I errors, reduced power). For this reason, there has been considerable interest in finding a test statistic that is appropriate under conditions of non-normality and variance heterogeneity. Previously recommended procedures for analysing such data include the James test, the Welch test applied either to the usual least squares estimators of central tendency and variability, or the Welch test with robust estimators (i.e., trimmed means and Winsorized variances). A new statistic proposed by Krishnamoorthy, Lu, and Mathew, intended to deal with heterogeneous variances, though not non-normality, uses a parametric bootstrap procedure. In their investigation of the parametric bootstrap test, the authors examined its operating characteristics under limited conditions and did not compare it to the Welch test based on robust estimators. Thus, we investigated how the parametric bootstrap procedure and a modified parametric bootstrap procedure based on trimmed means perform relative to previously recommended procedures when data are non-normal and heterogeneous. The results indicated that the tests based on trimmed means offer the best Type I error control and power when variances are unequal and at least some of the distribution shapes are non-normal. © 2011 The British Psychological Society.

Statistical methods for detecting periodic fragments in DNA sequence data

PubMed Central

2011-01-01

Background Period 10 dinucleotides are structurally and functionally validated factors that influence the ability of DNA to form nucleosomes, histone core octamers. Robust identification of periodic signals in DNA sequences is therefore required to understand nucleosome organisation in genomes. While various techniques for identifying periodic components in genomic sequences have been proposed or adopted, the requirements for such techniques have not been considered in detail and confirmatory testing for a priori specified periods has not been developed. Results We compared the estimation accuracy and suitability for confirmatory testing of autocorrelation, discrete Fourier transform (DFT), integer period discrete Fourier transform (IPDFT) and a previously proposed Hybrid measure. A number of different statistical significance procedures were evaluated but a blockwise bootstrap proved superior. When applied to synthetic data whose period-10 signal had been eroded, or for which the signal was approximately period-10, the Hybrid technique exhibited superior properties during exploratory period estimation. In contrast, confirmatory testing using the blockwise bootstrap procedure identified IPDFT as having the greatest statistical power. These properties were validated on yeast sequences defined from a ChIP-chip study where the Hybrid metric confirmed the expected dominance of period-10 in nucleosome associated DNA but IPDFT identified more significant occurrences of period-10. Application to the whole genomes of yeast and mouse identified ~ 21% and ~ 19% respectively of these genomes as spanned by period-10 nucleosome positioning sequences (NPS). Conclusions For estimating the dominant period, we find the Hybrid period estimation method empirically to be the most effective for both eroded and approximate periodicity. The blockwise bootstrap was found to be effective as a significance measure, performing particularly well in the problem of period detection in the presence of eroded periodicity. The autocorrelation method was identified as poorly suited for use with the blockwise bootstrap. Application of our methods to the genomes of two model organisms revealed a striking proportion of the yeast and mouse genomes are spanned by NPS. Despite their markedly different sizes, roughly equivalent proportions (19-21%) of the genomes lie within period-10 spans of the NPS dinucleotides {AA, TT, TA}. The biological significance of these regions remains to be demonstrated. To facilitate this, the genomic coordinates are available as Additional files 1, 2, and 3 in a format suitable for visualisation as tracks on popular genome browsers. Reviewers This article was reviewed by Prof Tomas Radivoyevitch, Dr Vsevolod Makeev (nominated by Dr Mikhail Gelfand), and Dr Rob D Knight. PMID:21527008

Parameter uncertainty and nonstationarity in regional extreme rainfall frequency analysis in Qu River Basin, East China

NASA Astrophysics Data System (ADS)

Zhu, Q.; Xu, Y. P.; Gu, H.

2014-12-01

Traditionally, regional frequency analysis methods were developed for stationary environmental conditions. Nevertheless, recent studies have identified significant changes in hydrological records, leading to the 'death' of stationarity. Besides, uncertainty in hydrological frequency analysis is persistent. This study aims to investigate the impact of one of the most important uncertainty sources, parameter uncertainty, together with nonstationarity, on design rainfall depth in Qu River Basin, East China. A spatial bootstrap is first proposed to analyze the uncertainty of design rainfall depth estimated by regional frequency analysis based on L-moments and estimated on at-site scale. Meanwhile, a method combining the generalized additive models with 30-year moving window is employed to analyze non-stationarity existed in the extreme rainfall regime. The results show that the uncertainties of design rainfall depth with 100-year return period under stationary conditions estimated by regional spatial bootstrap can reach 15.07% and 12.22% with GEV and PE3 respectively. On at-site scale, the uncertainties can reach 17.18% and 15.44% with GEV and PE3 respectively. In non-stationary conditions, the uncertainties of maximum rainfall depth (corresponding to design rainfall depth) with 0.01 annual exceedance probability (corresponding to 100-year return period) are 23.09% and 13.83% with GEV and PE3 respectively. Comparing the 90% confidence interval, the uncertainty of design rainfall depth resulted from parameter uncertainty is less than that from non-stationarity frequency analysis with GEV, however, slightly larger with PE3. This study indicates that the spatial bootstrap can be successfully applied to analyze the uncertainty of design rainfall depth on both regional and at-site scales. And the non-stationary analysis shows that the differences between non-stationary quantiles and their stationary equivalents are important for decision makes of water resources management and risk management.

A structured analysis of uncertainty surrounding modeled impacts of groundwater-extraction rules

NASA Astrophysics Data System (ADS)

Guillaume, Joseph H. A.; Qureshi, M. Ejaz; Jakeman, Anthony J.

2012-08-01

Integrating economic and groundwater models for groundwater-management can help improve understanding of trade-offs involved between conflicting socioeconomic and biophysical objectives. However, there is significant uncertainty in most strategic decision-making situations, including in the models constructed to represent them. If not addressed, this uncertainty may be used to challenge the legitimacy of the models and decisions made using them. In this context, a preliminary uncertainty analysis was conducted of a dynamic coupled economic-groundwater model aimed at assessing groundwater extraction rules. The analysis demonstrates how a variety of uncertainties in such a model can be addressed. A number of methods are used including propagation of scenarios and bounds on parameters, multiple models, block bootstrap time-series sampling and robust linear regression for model calibration. These methods are described within the context of a theoretical uncertainty management framework, using a set of fundamental uncertainty management tasks and an uncertainty typology.

Quantification of variability and uncertainty for air toxic emission inventories with censored emission factor data.

PubMed

Frey, H Christopher; Zhao, Yuchao

2004-11-15

Probabilistic emission inventories were developed for urban air toxic emissions of benzene, formaldehyde, chromium, and arsenic for the example of Houston. Variability and uncertainty in emission factors were quantified for 71-97% of total emissions, depending upon the pollutant and data availability. Parametric distributions for interunit variability were fit using maximum likelihood estimation (MLE), and uncertainty in mean emission factors was estimated using parametric bootstrap simulation. For data sets containing one or more nondetected values, empirical bootstrap simulation was used to randomly sample detection limits for nondetected values and observations for sample values, and parametric distributions for variability were fit using MLE estimators for censored data. The goodness-of-fit for censored data was evaluated by comparison of cumulative distributions of bootstrap confidence intervals and empirical data. The emission inventory 95% uncertainty ranges are as small as -25% to +42% for chromium to as large as -75% to +224% for arsenic with correlated surrogates. Uncertainty was dominated by only a few source categories. Recommendations are made for future improvements to the analysis.

A symbol of uniqueness: the cluster bootstrap for the 3-loop MHV heptagon

DOE PAGES

Drummond, J. M.; Papathanasiou, G.; Spradlin, M.

2015-03-16

Seven-particle scattering amplitudes in planar super-Yang-Mills theory are believed to belong to a special class of generalised polylogarithm functions called heptagon functions. These are functions with physical branch cuts whose symbols may be written in terms of the 42 cluster A-coordinates on Gr(4, 7). Motivated by the success of the hexagon bootstrap programme for constructing six-particle amplitudes we initiate the systematic study of the symbols of heptagon functions. We find that there is exactly one such symbol of weight six which satisfies the MHV last-entry condition and is finite in the 7 ll 6 collinear limit. This unique symbol ismore » both dihedral and parity-symmetric, and remarkably its collinear limit is exactly the symbol of the three-loop six-particle MHV amplitude, although none of these properties were assumed a priori. It must therefore be the symbol of the threeloop seven-particle MHV amplitude. The simplicity of its construction suggests that the n-gon bootstrap may be surprisingly powerful for n > 6.« less

Review of Orbital Propellant Transfer Techniques and the Feasibility of a Thermal Bootstrap Propellant Transfer Concepts

NASA Technical Reports Server (NTRS)

Yoshikawa, H. H.; Madison, I. B.

1971-01-01

This study was performed in support of the NASA Task B-2 Study Plan for Space Basing. The nature of space-based operations implies that orbital transfer of propellant is a prime consideration. The intent of this report is (1) to report on the findings and recommendations of existing literature on space-based propellant transfer techniques, and (2) to determine possible alternatives to the recommended methods. The reviewed literature recommends, in general, the use of conventional liquid transfer techniques (i.e., pumping) in conjunction with an artificially induced gravitational field. An alternate concept that was studied, the Thermal Bootstrap Transfer Process, is based on the compression of a two-phase fluid with subsequent condensation to a liquid (vapor compression/condensation). This concept utilizes the intrinsic energy capacities of the tanks and propellant by exploiting temperature differentials and available energy differences. The results indicate the thermodynamic feasibility of the Thermal Bootstrap Transfer Process for a specific range of tank sizes, temperatures, fill-factors and receiver tank heat transfer coefficients.

The intrinsic dependence structure of peak, volume, duration, and average intensity of hyetographs and hydrographs

NASA Astrophysics Data System (ADS)

Serinaldi, Francesco; Kilsby, Chris G.

2013-06-01

The information contained in hyetographs and hydrographs is often synthesized by using key properties such as the peak or maximum value Xp, volume V, duration D, and average intensity I. These variables play a fundamental role in hydrologic engineering as they are used, for instance, to define design hyetographs and hydrographs as well as to model and simulate the rainfall and streamflow processes. Given their inherent variability and the empirical evidence of the presence of a significant degree of association, such quantities have been studied as correlated random variables suitable to be modeled by multivariate joint distribution functions. The advent of copulas in geosciences simplified the inference procedures allowing for splitting the analysis of the marginal distributions and the study of the so-called dependence structure or copula. However, the attention paid to the modeling task has overlooked a more thorough study of the true nature and origin of the relationships that link Xp,V,D, and I. In this study, we apply a set of ad hoc bootstrap algorithms to investigate these aspects by analyzing the hyetographs and hydrographs extracted from 282 daily rainfall series from central eastern Europe, three 5 min rainfall series from central Italy, 80 daily streamflow series from the continental United States, and two sets of 200 simulated universal multifractal time series. Our results show that all the pairwise dependence structures between Xp,V,D, and I exhibit some key properties that can be reproduced by simple bootstrap algorithms that rely on a standard univariate resampling without resort to multivariate techniques. Therefore, the strong similarities between the observed dependence structures and the agreement between the observed and bootstrap samples suggest the existence of a numerical generating mechanism based on the superposition of the effects of sampling data at finite time steps and the process of summing realizations of independent random variables over random durations. We also show that the pairwise dependence structures are weakly dependent on the internal patterns of the hyetographs and hydrographs, meaning that the temporal evolution of the rainfall and runoff events marginally influences the mutual relationships of Xp,V,D, and I. Finally, our findings point out that subtle and often overlooked deterministic relationships between the properties of the event hyetographs and hydrographs exist. Confusing these relationships with genuine stochastic relationships can lead to an incorrect application of multivariate distributions and copulas and to misleading results.

A cross-cultural investigation into the relationships among parental support for basic psychological needs, sense of uniqueness, and happiness.

PubMed

Simşek, Omer Faruk; Demir, Melikşah

2014-01-01

A significant number of empirical studies have reported that parental support for basic psychological needs is a robust correlate of adolescent happiness. Yet, less is known about the mechanisms responsible for this link. The present study proposed a model suggesting that personal sense of uniqueness explains why satisfaction of basic psychological needs in parent-child relationships is related to happiness. This mediational model was tested among late adolescents in Turkey and the United States. Analyses relying on structural equation modeling and bootstrapping supported the model in both cultures. Implications of the findings for theory and cross-cultural research are discussed. Directions for future research that could improve our understanding of the dynamic interplay between basic needs, sense of uniqueness and well-being are provided.

Comparison of Histograms for Use in Cloud Observation and Modeling

NASA Technical Reports Server (NTRS)

Green, Lisa; Xu, Kuan-Man

2005-01-01

Cloud observation and cloud modeling data can be presented in histograms for each characteristic to be measured. Combining information from single-cloud histograms yields a summary histogram. Summary histograms can be compared to each other to reach conclusions about the behavior of an ensemble of clouds in different places at different times or about the accuracy of a particular cloud model. As in any scientific comparison, it is necessary to decide whether any apparent differences are statistically significant. The usual methods of deciding statistical significance when comparing histograms do not apply in this case because they assume independent data. Thus, a new method is necessary. The proposed method uses the Euclidean distance metric and bootstrapping to calculate the significance level.

Analytic Guided-Search Model of Human Performance Accuracy in Target- Localization Search Tasks

NASA Technical Reports Server (NTRS)

Eckstein, Miguel P.; Beutter, Brent R.; Stone, Leland S.

2000-01-01

Current models of human visual search have extended the traditional serial/parallel search dichotomy. Two successful models for predicting human visual search are the Guided Search model and the Signal Detection Theory model. Although these models are inherently different, it has been difficult to compare them because the Guided Search model is designed to predict response time, while Signal Detection Theory models are designed to predict performance accuracy. Moreover, current implementations of the Guided Search model require the use of Monte-Carlo simulations, a method that makes fitting the model's performance quantitatively to human data more computationally time consuming. We have extended the Guided Search model to predict human accuracy in target-localization search tasks. We have also developed analytic expressions that simplify simulation of the model to the evaluation of a small set of equations using only three free parameters. This new implementation and extension of the Guided Search model will enable direct quantitative comparisons with human performance in target-localization search experiments and with the predictions of Signal Detection Theory and other search accuracy models.

On the thresholds in modeling of high flows via artificial neural networks - A bootstrapping analysis

NASA Astrophysics Data System (ADS)

Panagoulia, D.; Trichakis, I.

2012-04-01

Considering the growing interest in simulating hydrological phenomena with artificial neural networks (ANNs), it is useful to figure out the potential and limits of these models. In this study, the main objective is to examine how to improve the ability of an ANN model to simulate extreme values of flow utilizing a priori knowledge of threshold values. A three-layer feedforward ANN was trained by using the back propagation algorithm and the logistic function as activation function. By using the thresholds, the flow was partitioned in low (x < μ), medium (μ ≤ x ≤ μ + 2σ) and high (x > μ + 2σ) values. The employed ANN model was trained for high flow partition and all flow data too. The developed methodology was implemented over a mountainous river catchment (the Mesochora catchment in northwestern Greece). The ANN model received as inputs pseudo-precipitation (rain plus melt) and previous observed flow data. After the training was completed the bootstrapping methodology was applied to calculate the ANN confidence intervals (CIs) for a 95% nominal coverage. The calculated CIs included only the uncertainty, which comes from the calibration procedure. The results showed that an ANN model trained specifically for high flows, with a priori knowledge of the thresholds, can simulate these extreme values much better (RMSE is 31.4% less) than an ANN model trained with all data of the available time series and using a posteriori threshold values. On the other hand the width of CIs increases by 54.9% with a simultaneous increase by 64.4% of the actual coverage for the high flows (a priori partition). The narrower CIs of the high flows trained with all data may be attributed to the smoothing effect produced from the use of the full data sets. Overall, the results suggest that an ANN model trained with a priori knowledge of the threshold values has an increased ability in simulating extreme values compared with an ANN model trained with all the data and a posteriori knowledge of the thresholds.

Integration of manatee life-history data and population modeling

USGS Publications Warehouse

Eberhardt, L.L.; O'Shea, Thomas J.; O'Shea, Thomas J.; Ackerman, B.B.; Percival, H. Franklin

1995-01-01

Aerial counts and the number of deaths have been a major focus of attention in attempts to understand the population status of the Florida manatee (Trichechus manatus latirostris). Uncertainties associated with these data have made interpretation difficult. However, knowledge of manatee life-history attributes increased and now permits the development of a population model. We describe a provisional model based on the classical approach of Lotka. Parameters in the model are based on data from'other papers in this volume and draw primarily on observations from the Crystal River, Blue Spring, and Adantic Coast areas. The model estimates X (the finite rate ofincrease) at each study area, and application ofthe delta method provides estimates of variance components and partial derivatives ofX with respectto key input parameters (reproduction, adult survival, and early survival). In some study areas, only approximations of some parameters are available. Estimates of X and coefficients of variation (in parentheses) of manatees were 1.07 (0.009) in the Crystal River, 1.06 (0.012) at Blue Spring, and 1.01 (0.012) on the Atlantic Coast. Changing adult survival has a major effect on X. Early-age survival has the smallest effect. Bootstrap comparisons of population growth estimates from trend counts in the Crystal River and at Blue Spring and the reproduction and survival data suggest that the higher, observed rates from counts are probably not due to chance. Bootstrapping for variance estimates based on reproduction and survival data from manatees at Blue Spring and in the Crystal River provided estimates of X, adult survival, and rates of reproduction that were similar to those obtained by other methods. Our estimates are preliminary and suggestimprovements for future data collection and analysis. However, results support efforts to reduce mortality as the most effective means to promote the increased growth necessary for the eventual recovery of the Florida manatee population.

Three-dimensional quantitative structure-activity relationship CoMSIA/CoMFA and LeapFrog studies on novel series of bicyclo [4.1.0] heptanes derivatives as melanin-concentrating hormone receptor R1 antagonists.

PubMed

Morales-Bayuelo, Alejandro; Ayazo, Hernan; Vivas-Reyes, Ricardo

2010-10-01

Comparative molecular similarity indices analysis (CoMSIA) and comparative molecular field analysis (CoMFA) were performed on a series of bicyclo [4.1.0] heptanes derivatives as melanin-concentrating hormone receptor R1 antagonists (MCHR1 antagonists). Molecular superimposition of antagonists on the template structure was performed by database alignment method. The statistically significant model was established on sixty five molecules, which were validated by a test set of ten molecules. The CoMSIA model yielded the best predictive model with a q(2) = 0.639, non cross-validated R(2) of 0.953, F value of 92.802, bootstrapped R(2) of 0.971, standard error of prediction = 0.402, and standard error of estimate = 0.146 while the CoMFA model yielded a q(2) = 0.680, non cross-validated R(2) of 0.922, F value of 114.351, bootstrapped R(2) of 0.925, standard error of prediction = 0.364, and standard error of estimate = 0.180. CoMFA analysis maps were employed for generating a pseudo cavity for LeapFrog calculation. The contour maps obtained from 3D-QSAR studies were appraised for activity trends for the molecules analyzed. The results show the variability of steric and electrostatic contributions that determine the activity of the MCHR1 antagonist, with these results we proposed new antagonists that may be more potent than previously reported, these novel antagonists were designed from the addition of highly electronegative groups in the substituent di(i-C(3)H(7))N- of the bicycle [4.1.0] heptanes, using the model CoMFA which also was used for the molecular design using the technique LeapFrog. The data generated from the present study will further help to design novel, potent, and selective MCHR1 antagonists. Copyright (c) 2010 Elsevier Masson SAS. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sanchez-Nieto, Beatriz, E-mail: bsanchez@fis.puc.cl; Goset, Karen C.; Caviedes, Ivan

Purpose: To propose multivariate predictive models for changes in pulmonary function tests ({Delta}PFTs) with respect to preradiotherapy (pre-RT) values in patients undergoing RT for breast cancer and lymphoma. Methods and Materials: A prospective study was designed to measure {Delta}PFTs of patients undergoing RT. Sixty-six patients were included. Spirometry, lung capacity (measured by helium dilution), and diffusing capacity of carbon monoxide tests were used to measure lung function. Two lung definitions were considered: paired lung vs. irradiated lung (IL). Correlation analysis of dosimetric parameters (mean lung dose and the percentage of lung volume receiving more than a threshold dose) and {Delta}PFTsmore » was carried out to find the best dosimetric predictor. Chemotherapy, age, smoking, and the selected dose-volume parameter were considered as single and interaction terms in a multivariate analysis. Stability of results was checked by bootstrapping. Results: Both lung definitions proved to be similar. Modeling was carried out for IL. Acute and late damage showed the highest correlations with volumes irradiated above {approx}20 Gy (maximum R{sup 2} = 0.28) and {approx}40 Gy (maximum R{sup 2} = 0.21), respectively. RT alone induced a minor and transitory restrictive defect (p = 0.013). Doxorubicin-cyclophosphamide-paclitaxel (Taxol), when administered pre-RT, induced a late, large restrictive effect, independent of RT (p = 0.031). Bootstrap values confirmed the results. Conclusions: None of the dose-volume parameters was a perfect predictor of outcome. Thus, different predictor models for {Delta}PFTs were derived for the IL, which incorporated other nondosimetric parameters mainly through interaction terms. Late {Delta}PFTs seem to behave more serially than early ones. Large restrictive defects were demonstrated in patients pretreated with doxorubicin-cyclophosphamide-paclitaxel.« less

On-ground casualty risk reduction by structural design for demise

NASA Astrophysics Data System (ADS)

Lemmens, Stijn; Funke, Quirin; Krag, Holger

2015-06-01

In recent years, awareness concerning the on-ground risk posed by un-controlled re-entering space systems has increased. On average over the past decade, an object with mass above 800 kg re-enters every week from which only a few, e.g. ESA's GOCE in 2013 and NASA's UARS in 2011, appeared prominent in international media. Space agencies and nations have discussed requirements to limit the on-ground risk for future missions. To meet the requirements, the amount of debris falling back on Earth has to be limited in number, mass and size. Design for demise (D4D) refers to all measures taken in the design of a space object to increase the potential for demise of the object and its components during re-entry. SCARAB (Spacecraft Atmospheric Re-entry and Break-Up) is ESA's high-fidelity tool which analyses the thermal and structural effects of atmospheric re-entry on spacecraft with a finite-element approach. For this study, a model of a representative satellite is developed in SCARAB to serve as test-bed for D4D analyses on a structural level. The model is used as starting point for different D4D approaches based on increasing the exposure of the satellite components to the aero-thermal environment, as a way to speed up the demise. Statistical bootstrapping is applied to the resulting on-ground fragment lists in order to compare the different re-entry scenarios and to determine the uncertainties of the results. Moreover, the bootstrap results can be used to analyse the casualty risk estimator from a theoretical point of view. The risk reductions for the analysed D4D techniques are presented with respect to the reference scenario for the modelled representative satellite.

On-Ground Casualty Risk Reduction by Structural Design for Demise

NASA Astrophysics Data System (ADS)

Lemmens, Stijn; Krag, Holger; Funke, Quirin

In recent years, awareness concerning the risk posed by un-controlled re-entering spacecraft on ground has increased. Some re-entry events such as ESA's GOCE in 2013 and NASA's UARS appeared prominent in international media. Space agencies and nations, in cooperation within the Inter-Agency Space Debris Coordination Committee (IADC), have established a requirements to limited the on-ground risk for future missions. To meet the requirements, the amount of debris falling back on Earth has to be limited in number, mass and size. Design for demise (D4D) refers to all measures taken in the design of a space object to increase the potential for demise of the object and its components during re-entry. SCARAB (Spacecraft Atmospheric Re-entry and Break-Up) is ESA's high-fidelity tool which analyses the thermal and structural effects of atmospheric re-entry on spacecraft in a finite-element approach. For this study, a model of a representative satellite is developed in Scarab to serve as test-bed for D4D analysis on a structural level. The model is used as starting point for different D4D approaches based on increasing the exposure of the satellite components to the aero-thermal environment, as a way to speed up the demise. Statistical bootstrapping is applied to the resulting on-ground fragment lists in order to compare the different re-entry scenarios and to determine the uncertainties of the results. Moreover, the bootstrap results can be used to analyse the casualty risk estimator from a theoretical point of view. The risk reductions for the analysed D4D techniques are presented w.r.t. the reference scenario for the modelled representative satellite.