The bootstrap method in space physics: Error estimation for the minimum variance analysis

1995 ◽  
Vol 22 (3) ◽  
pp. 307-310 ◽  
Author(s):  
H. Kawano ◽  
T. Higuchi
Keyword(s):  
Error Estimation ◽  
Bootstrap Method ◽  
Minimum Variance ◽  
Space Physics ◽  
Variance Analysis ◽  
The Bootstrap Method

scholarly journals Statistical error estimation of the Feynman-α method using the bootstrap method

2015 ◽  
Vol 53 (9) ◽  
pp. 1447-1453 ◽  
Author(s):  
Tomohiro Endo ◽  
Akio Yamamoto ◽  
Takahiro Yagi ◽  
Cheol Ho Pyeon
Keyword(s):  
Error Estimation ◽  
Bootstrap Method ◽  
Statistical Error ◽  
The Bootstrap Method

scholarly journals Statistical Estimates of the Pulsar Glitch Activity

Universe ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 8
Author(s):  
Alessandro Montoli ◽  
Marco Antonelli ◽  
Brynmor Haskell ◽  
Pierre Pizzochero
Keyword(s):  
Linear Regression ◽  
Upper Bound ◽  
Bootstrap Method ◽  
Waiting Times ◽  
Statistical Estimates ◽  
Equal Variance ◽  
Linear Fit ◽  
The Moment ◽  
The Bootstrap Method

A common way to calculate the glitch activity of a pulsar is an ordinary linear regression of the observed cumulative glitch history. This method however is likely to underestimate the errors on the activity, as it implicitly assumes a (long-term) linear dependence between glitch sizes and waiting times, as well as equal variance, i.e., homoscedasticity, in the fit residuals, both assumptions that are not well justified from pulsar data. In this paper, we review the extrapolation of the glitch activity parameter and explore two alternatives: the relaxation of the homoscedasticity hypothesis in the linear fit and the use of the bootstrap technique. We find a larger uncertainty in the activity with respect to that obtained by ordinary linear regression, especially for those objects in which it can be significantly affected by a single glitch. We discuss how this affects the theoretical upper bound on the moment of inertia associated with the region of a neutron star containing the superfluid reservoir of angular momentum released in a stationary sequence of glitches. We find that this upper bound is less tight if one considers the uncertainty on the activity estimated with the bootstrap method and allows for models in which the superfluid reservoir is entirely in the crust.

The Bootstrap-Method: Discussion and Proposal for Improvement / Das bootstrap-Verfahren: Diskussion und Verbesserungsvorschlag

1998 ◽  
Vol 217 (1) ◽  
Author(s):  
Hans Schneeberger
Keyword(s):  
Bootstrap Method ◽  
Law School ◽  
Mean Deviation ◽  
Alternative Method ◽  
Doubling Method ◽  
The Mean ◽  
The Bootstrap Method

SummaryWith Efron’s law-school example the bootstrap method is compared with an alternative method, called doubling. It is shown, that the mean deviation of the estimator is always smaller for the doubling method.

Application of the bootstrap method to quantify uncertainty in seismic hazard estimates

1992 ◽  
Vol 82 (1) ◽  
pp. 104-119
Author(s):  
Michéle Lamarre ◽  
Brent Townshend ◽  
Haresh C. Shah
Keyword(s):  
Confidence Interval ◽  
Seismic Hazard ◽  
Statistical Method ◽  
Bootstrap Method ◽  
Earthquake Magnitude ◽  
Earthquake Catalog ◽  
Uncertainty Measure ◽  
Attenuation Models ◽  
The Bootstrap Method

Abstract This paper describes a methodology to assess the uncertainty in seismic hazard estimates at particular sites. A variant of the bootstrap statistical method is used to combine the uncertainty due to earthquake catalog incompleteness, earthquake magnitude, and recurrence and attenuation models used. The uncertainty measure is provided in the form of a confidence interval. Comparisons of this method applied to various sites in California with previous studies are used to confirm the validity of the method.

Standard Error Estimation of 3PL IRT True Score Equating With an MCMC Method

2008 ◽  
Vol 33 (3) ◽  
pp. 257-278 ◽  
Author(s):  
Yuming Liu ◽  
E. Matthew Schulz ◽  
Lei Yu
Keyword(s):  
Bootstrap Method ◽  
Standard Errors ◽  
True Score ◽  
Mcmc Method ◽  
Test Form ◽  
True Score Equating ◽  
Standard Error Estimation ◽  
Equivalent Test ◽  
Mean Square Errors ◽  
The Bootstrap Method

A Markov chain Monte Carlo (MCMC) method and a bootstrap method were compared in the estimation of standard errors of item response theory (IRT) true score equating. Three test form relationships were examined: parallel, tau-equivalent, and congeneric. Data were simulated based on Reading Comprehension and Vocabulary tests of the Iowa Tests of Basic Skills®. For parallel and congeneric test forms within valid IRT true score ranges, the pattern and magnitude of standard errors of IRT true score equating estimated by the MCMC method were very close to those estimated by the bootstrap method. For tau-equivalent test forms, the pattern of standard errors estimated by the two methods was also similar. Bias and mean square errors of equating produced by the MCMC method were smaller than those produced by the bootstrap method; however, standard errors were larger. In educational testing, the MCMC method may be used as an additional or alternative procedure to the bootstrap method when evaluating the precision of equating results.

scholarly journals ACCURACY AND ERROR FACTORS OF SENSITOMETRIC CURVE USING THE BOOTSTRAP METHOD

1991 ◽  
Vol 47 (6) ◽  
pp. 811-817 ◽  
Author(s):  
AKIO OGURA ◽  
HIDEHARU NIIDA ◽  
KENICHI OGAWA ◽  
YOSHINORI KOMAI ◽  
HIDEHIKO TODOROKI ◽  
...  
Keyword(s):  
Bootstrap Method ◽  
The Bootstrap Method

Random Weighting Estimation of One-Sided Confidence Intervals in Discrete Distributions

2013 ◽  
pp. 92-102
Author(s):  
Yalin Jiao ◽  
Yongmin Zhong ◽  
Shesheng Gao ◽  
Bijan Shirinzadeh
Keyword(s):  
Confidence Intervals ◽  
Coverage Probability ◽  
Bootstrap Method ◽  
Experimental Results ◽  
Discrete Distributions ◽  
Estimation Accuracy ◽  
Weighting Method ◽  
Random Weighting Method ◽  
Random Weighting ◽  
The Bootstrap Method

This paper presents a new random weighting method for estimation of one-sided confidence intervals in discrete distributions. It establishes random weighting estimations for the Wald and Score intervals. Based on this, a theorem of coverage probability is rigorously proved by using the Edgeworth expansion for random weighting estimation of the Wald interval. Experimental results demonstrate that the proposed random weighting method can effectively estimate one-sided confidence intervals, and the estimation accuracy is much higher than that of the bootstrap method.

New Metric for Minimum Variance Analysis Validation in the Study of Interplanetary Magnetic Clouds

Solar Physics ◽  
2020 ◽  
Vol 295 (3) ◽  
Author(s):  
Rosemeire Aparecida Rosa Oliveira ◽  
Marcos William da Silva Oliveira ◽  
Arian Ojeda-González ◽  
Victor De La Luz
Keyword(s):  
Minimum Variance ◽  
Variance Analysis ◽  
Magnetic Clouds
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