Comparison of Bootstrap Confidence Intervals Using Monte Carlo Simulations

Flowers-Cano, Roberto S.; Ortíz-Gómez, Ruperto; León-Jiménez, J. E.; Rivera, Ruben; Cruz, Luis A. Perera

doi:10.3390/w10020166

Cited by 16 publications

(6 citation statements)

References 25 publications

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“…is based on the FF of the distribution, using Chow's relation, for a 95% confidence level and a Gaussian assumption. The classic Bootstrap procedure [44][45][46][47][48] is another way for estimating the confidence interval, although it is more time consuming and has certain drawbacks. The C.I.…”

Section: Discussionmentioning

confidence: 99%

Assessing Flood Risk: LH-Moments Method and Univariate Probability Distributions in Flood Frequency Analysis

Ilinca,

Stanca,

Anghel

2023

Water

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This study examines all of the equations necessary to derive the parameters for seven probability distributions of three parameters typically used in flood frequency research, namely the Pearson III (PE3), the generalized extreme value (GEV), the Weibull (W3), the log-normal (LN3), the generalized Pareto Type II (PG), the Rayleigh (RY) and the log-logistic (LL3) distributions, using the higher-order linear moments method (LH-moments). The analysis represents the expansion of previous research whose results were presented in previous materials, and is part of hydrological research aimed at developing a standard for calculating maximum flows based on L-moments and LH-moments. The given methods for calculating the parameters of the examined distributions are used to calculate the maximum flows on Romania’s Prigor River. For both methods, the criterion for selecting the most suitable distribution is represented by the diagram of the L-skewness–L-kurtosis and LH-skewness–LH-kurtosis. The results for Prigor River show that the PG distribution is the best model for the L-moments method, the theoretical values of the statistical indicators being 0.399 and 0.221. The RY distribution is the best model for the LH-moments technique, with values of 0.398 and 0.192 for the two statistical indicators.

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Section: Discussionmentioning

confidence: 99%

Assessing Flood Risk: LH-Moments Method and Univariate Probability Distributions in Flood Frequency Analysis

Ilinca,

Stanca,

Anghel

2023

Water

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“…We estimated the SIRs for each AESI by dividing the sum of all observed events by the sum of all expected events. Corresponding 95% CIs were calculated using the parametric percentile bootstrap method [ 34 ] based on 100,000 draws to account for variation in the observed and expected incidence rates. The observed and expected incidence were treated as Poisson and normal random variables, respectively.…”

Section: Methodsmentioning

confidence: 99%

Adverse Events Following the BNT162b2 mRNA COVID-19 Vaccine (Pfizer-BioNTech) in Aotearoa New Zealand

et al. 2023

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Introduction In February 2021, New Zealand began its largest ever immunisation programme with the BNT162b2 mRNA coronavirus disease 2019 (COVID-19) vaccine. Objective We aimed to understand the association between 12 adverse events of special interest (AESIs) and a primary dose of BNT162b2 in the New Zealand population aged ≥5 years from 19 February 2021 through 10 February 2022. Methods Using national electronic health records, the observed rates of AESIs within a risk period (1–21 days) following vaccination were compared with the expected rates based on background data (2014–2019). Standardised incidence ratios (SIRs) were estimated for each AESI with 95% confidence intervals (CIs) using age group-specific background rates. The risk difference was calculated to estimate the excess or reduced number of events per 100,000 persons vaccinated in the risk period. Results As of 10 February 2022, 4,277,163 first doses and 4,114,364 second doses of BNT162b2 had been administered to the eligible New Zealand population aged ≥5 years. The SIRs for 11 of the 12 selected AESIs were not statistically significantly increased post vaccination. The SIR (95% CI) for myo/pericarditis following the first dose was 2.3 (1.8–2.7), with a risk difference (95% CI) of 1.3 (0.9–1.8), per 100,000 persons vaccinated, and 4.0 (3.4–4.6), with a risk difference of 3.1 (2.5–3.7), per 100,000 persons vaccinated following the second dose. The highest SIR was 25.6 (15.5–37.5) in the 5–19 years age group, following the second dose of the vaccine, with an estimated five additional myo/pericarditis cases per 100,000 persons vaccinated. A statistically significant increased SIR of single organ cutaneous vasculitis (SOCV) was also observed following the first dose of BNT162b2 in the 20–39 years age group only. Conclusions A statistically significant association between BNT162b2 vaccination and myo/pericarditis was observed. This association has been confirmed internationally. BNT162b2 was not found to be associated with the other AESIs investigated, except for SOCV following the first dose of BNT162b2 in the 20–39 years age group only, providing reassurances around the safety of the vaccine. Supplementary Information The online version contains supplementary material available at 10.1007/s40264-023-01332-1.

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“…). This calculation assumes that the distribution of the estimator of  is approximately normal (Flowers-Cano et al, 2018). However, there are several situations in which the assumption of normality is violated.…”

Section: Bootstrap Methodsmentioning

confidence: 99%

"Confidence Intervals using Bootstrap Methods for the Parameter of the zero-truncated Poisson-Shanker Distribution and their Application"

Kaskasamkul,

Panichkitkosolkul

2023

JASET.KMUTNB

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Count data with non-zero values can be modeled using the zero-truncated Poisson-Shanker distribution in a variety of situations. However, the confidence interval estimation for the parameter has not yet been examined. In this paper, confidence interval estimation based on percentile bootstrap, simple bootstrap, biased-corrected and accelerated bootstrap, and bootstrap-t methods was examined in terms of coverage probability, lower and upper error rate, and average interval length through Monte Carlo simulation. The simulation results indicate that attaining the nominal confidence level using the bootstrap methods were not possible for small sample sizes, regardless of the other settings. Furthermore, in cases when the sample size was large, there was no significant difference observed in the performances of the all methods. In several scenarios, the bias-corrected and accelerated bootstrap method exhibited superior performance compared to the other methods. Last, we used the bootstrap methods to calculate the confidence interval for the parameter of the zero-truncated Poisson-Shanker distribution via three numerical examples, and the results of these examples matched those from the simulation study.

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Comparison of Bootstrap Confidence Intervals Using Monte Carlo Simulations

Cited by 16 publications

References 25 publications

Assessing Flood Risk: LH-Moments Method and Univariate Probability Distributions in Flood Frequency Analysis

Assessing Flood Risk: LH-Moments Method and Univariate Probability Distributions in Flood Frequency Analysis

Adverse Events Following the BNT162b2 mRNA COVID-19 Vaccine (Pfizer-BioNTech) in Aotearoa New Zealand

"Confidence Intervals using Bootstrap Methods for the Parameter of the zero-truncated Poisson-Shanker Distribution and their Application"

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