scholarly journals Comparison of Bootstrap Confidence Intervals Using Monte Carlo Simulations

Water ◽  
2018 ◽  
Vol 10 (2) ◽  
pp. 166 ◽  
Author(s):  
Roberto Flowers-Cano ◽  
Ruperto Ortiz-Gómez ◽  
Jesús León-Jiménez ◽  
Raúl López Rivera ◽  
Luis Perera Cruz
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Confidence Intervals ◽  
Bootstrap Confidence Intervals

Percentile and Percentile-t Bootstrap Confidence Intervals: A Practical Comparison

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Christopher J. Elias
Keyword(s):  
Monte Carlo ◽  
Confidence Intervals ◽  
Monte Carlo Study ◽  
Bootstrap Confidence Intervals ◽  
Bootstrap Percentile

AbstractThis paper employs a Monte Carlo study to compare the performance of equal-tailed bootstrap percentile-

scholarly journals Describing the Pearson 𝑅 distribution of aggregate data

2020 ◽  
Vol 26 (1) ◽  
pp. 17-32
Author(s):  
David J. Torres
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Confidence Intervals ◽  
Random Sampling ◽  
Aggregate Data ◽  
Ecological Fallacy ◽  
Ecological Studies ◽  
The Difference ◽  
The Individual ◽  
Individual Scores

AbstractEcological studies and epidemiology need to use group averaged data to make inferences about individual patterns. However, using correlations based on averages to estimate correlations of individual scores is subject to an “ecological fallacy”. The purpose of this article is to create distributions of Pearson R correlation values computed from grouped averaged or aggregate data using Monte Carlo simulations and random sampling. We show that, as the group size increases, the distributions can be approximated by a generalized hypergeometric distribution. The expectation of the constructed distribution slightly underestimates the individual Pearson R value, but the difference becomes smaller as the number of groups increases. The approximate normal distribution resulting from Fisher’s transformation can be used to build confidence intervals to approximate the Pearson R value based on individual scores from the Pearson R value based on the aggregated scores.

scholarly journals Statistical Inference for the Inverted Scale Family under General Progressive Type-II Censoring

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 731
Author(s):  
Jing Gao ◽  
Kehan Bai ◽  
Wenhao Gui
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Monte Carlo Simulations ◽  
Maximum Likelihood Estimator ◽  
Confidence Intervals ◽  
Interval Estimation ◽  
Bayesian Estimator ◽  
Small Samples ◽  
Likelihood Estimator ◽  
Scale Family

Two estimation problems are studied based on the general progressively censored samples, and the distributions from the inverted scale family (ISF) are considered as prospective life distributions. One is the exact interval estimation for the unknown parameter θ , which is achieved by constructing the pivotal quantity. Through Monte Carlo simulations, the average 90 % and 95 % confidence intervals are obtained, and the validity of the above interval estimation is illustrated with a numerical example. The other is the estimation of R = P ( Y < X ) in the case of ISF. The maximum likelihood estimator (MLE) as well as approximate maximum likelihood estimator (AMLE) is obtained, together with the corresponding R-symmetric asymptotic confidence intervals. With Bootstrap methods, we also propose two R-asymmetric confidence intervals, which have a good performance for small samples. Furthermore, assuming the scale parameters follow independent gamma priors, the Bayesian estimator as well as the HPD credible interval of R is thus acquired. Finally, we make an evaluation on the effectiveness of the proposed estimations through Monte Carlo simulations and provide an illustrative example of two real datasets.

scholarly journals Why statistical testing and confidence intervals should not be used in comparative life cycle assessments based on Monte Carlo simulations

2020 ◽  
Vol 25 (11) ◽  
pp. 2101-2105 ◽  
Author(s):  
Claudia von Brömssen ◽  
Elin Röös
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Life Cycle ◽  
Monte Carlo Simulations ◽  
Hypothesis Testing ◽  
Confidence Intervals ◽  
Statistical Testing ◽  
Life Cycle Assessments ◽  
Simulation Results

Abstract In the last years, it has been suggested to use statistical inferential methods, such as hypothesis testing or confidence intervals, to compare different products, services, or systems within comparative life cycle assessments based on Monte Carlo simulation results. However, the use of statistical inferential methods in such settings is fundamentally incorrect and should not be continued. In this article, we explain why and look closer at some related topics.

Low-voltage EDS of magnesium ferrite Dendrites in a FEG-SEM

1996 ◽  
Vol 54 ◽  
pp. 478-479
Author(s):  
Matthew T. Johnson ◽  
Ian M. Anderson ◽  
Jim Bentley ◽  
C. Barry Carter
Keyword(s):  
Monte Carlo ◽  
Quantitative Analysis ◽  
Monte Carlo Simulations ◽  
Cross Section ◽  
Spatial Resolution ◽  
Low Voltage ◽  
Magnesium Ferrite ◽  
X Ray ◽  
Interaction Volume ◽  
Ferrite Spinel

Energy-dispersive X-ray spectrometry (EDS) performed at low (≤ 5 kV) accelerating voltages in the SEM has the potential for providing quantitative microanalytical information with a spatial resolution of ∼100 nm. In the present work, EDS analyses were performed on magnesium ferrite spinel [(MgxFe1−x)Fe2O4] dendrites embedded in a MgO matrix, as shown in Fig. 1. spatial resolution of X-ray microanalysis at conventional accelerating voltages is insufficient for the quantitative analysis of these dendrites, which have widths of the order of a few hundred nanometers, without deconvolution of contributions from the MgO matrix. However, Monte Carlo simulations indicate that the interaction volume for MgFe2O4 is ∼150 nm at 3 kV accelerating voltage and therefore sufficient to analyze the dendrites without matrix contributions.Single-crystal {001}-oriented MgO was reacted with hematite (Fe2O3) powder for 6 h at 1450°C in air and furnace cooled. The specimen was then cleaved to expose a clean cross-section suitable for microanalysis.

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