• Journal of the Korean Data and Information Science Society
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• v.6 no.1
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• pp.73-83
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• 1995

We construct the approximate bootstrap confidence intervals for reliability (R) when the distributions of strength and stress are both normal. Also we propose percentile, bias correct (BC), bias correct acceleration (BCa), and percentile-t intervals for R. We compare with the accuracy of the proposed bootstrap confidence intervals and classical confidence interval based on asymptotic normal distribution through Monte Carlo simulation. Results indicate that the confidence intervals by bootstrap method work better than classical confidence interval. In particular, confidence intervals by BC and BCa method work well for small sample and/or large value of true reliability.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.75-81
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• 2004

We selected, in previous research, a major DNA Marker 235bp of ILSTS035 microsatellite locus in progeny test Hanwoo chromosome 6. We apply a major DNA Marker 235bp to perormance valuation Hanwoo chomosome 6. We use bootstrap BCa method and calculate confidence interval. A major DNA Marker 235bp is verified that it does not have environmental effect but affects primely economic trait factor.

• Journal of Environmental Science International
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• v.20 no.3
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• pp.321-327
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• 2011

Bootstrap methods is the computer-based resampling method that estimates the standard errors and confidence intervals of summary statistics using the plug-in principle for assessing the accuracy or uncertainty of statistical estimates, and the BCa method among the Bootstrap methods is known much superior to other Bootstrap methods in respect of the standards of statistical validation. Therefore this study suggests the method of the representation and treatment of uncertainty in flood risk assessment and water resources planning from the construction and application of rainfall frequency analysis model considersing the uncertainty based on the nonparametric BCa method among the Bootstrap methods for the assessement of the estimation of probability rainfall and the effect of uncertainty considering the uncertainty of the parameter estimation of probability in the rainfall frequency analysis that is the most fundamental in flood risk assessement and water resources planning.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1406-1411
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• 2009

Bootstrap 기법은 통계학적 추정치의 정확도 또는 불확실성을 평가하기 위한 컴퓨터 기반 리샘플링 기법으로서 플러그인 원칙을 이용하여 요약통계치의 표준오차 및 신뢰구간을 추정하며, Bootstrap 기법 중 BCa 기법은 다른 Bootstrap 기법들에 비해 적합도 기준면에서 훨씬 우수한 결과를 나타내는 것으로 알려져 있다. 본 논문에서는 강우빈도해석에서 확률분포의 매개변수 추정에 대한 불확실성 고려한 확률강우량의 산정 및 불확실성의 영향을 평가하기 위하여 Bootstrap 기법 중 비매개변수적 BCa 기법에 기반한 불확실성을 고려한 강우빈도해석모델 구축 및 적용을 통해 홍수위험평가 및 수자원 계획 등에 있어서 불확실성 표현 및 처리기법을 제시하였다.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.41-47
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• 2004

DNA marker 95bp and 100bp are selected as major DNA markers of the BM4311 microsatellite locus in progeny test Hanwoo chromosome 6 linkage map. This document is tried to know whether DNA marker 95bp and 100bp are also major DNA markers in Hanwoo performance valuation in chromosome 6 linkage map. The bootstrap BCa method will be used to calculate confidence interval for DNA markers.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.759-772
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• 2003

K-Means and Web mining modelling have been tried for finding major DNA marker of BM4311 microsatellite loci in Hanwoo Chromosome 6 linkage map. Furthermore, a major DNA mining by bootstrap simulations(BCa) has been applied.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.41-47
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• 2003

한우 6번 염색체 유전자 지도에서 한우의 질을 높이기 위한 QTL(quantitative trait loci)분석을 실시하여 선별된 Loci 값을 Permutation Test를 이용하여 계산하였다. 한편, 경제적으로 주요한 한우의 특성부위(질적부위와 육량등)에 따른, 우수 경제형질 DNA marker를 K-평균 군집법을 실시 파악하였다. 이들 QTL과 K-평균법에 의해 한우의 염색체 6번, ILST035의 주요 경제 형질별 DNA marker들을 선별하여, Bootstrap BCa방법을 이용하여 각 DNA marker들의 신뢰구간을 구했다.

• The Korean Journal of Physiology and Pharmacology
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• v.13 no.5
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• pp.367-371
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• 2009

The estimation of 90% parametric confidence intervals (CIs) of mean AUC and Cmax ratios in bioequivalence (BE) tests are based upon the assumption that formulation effects in log-transformed data are normally distributed. To compare the parametric CIs with those obtained from nonparametric methods we performed repeated estimation of bootstrap-resampled datasets. The AUC and Cmax values from 3 archived datasets were used. BE tests on 1,000 resampled data sets from each archived dataset were performed using SAS (Enterprise Guide Ver.3). Bootstrap nonparametric 90% CIs of formulation effects were then compared with the parametric 90% CIs of the original datasets. The 90% CIs of formulation effects estimated from the 3 archived datasets were slightly different from nonparametric 90% CIs obtained from BE tests on resampled datasets. Histograms and density curves of formulation effects obtained from resampled datasets were similar to those of normal distribution. However, in 2 of 3 resampled log (AUC) datasets, the estimates of formulation effects did not follow the Gaussian distribution. Bias-corrected and accelerated (BCa) CIs, one of the nonparametric CIs of formulation effects, shifted outside the parametric 90% CIs of the archived datasets in these 2 non-normally distributed resampled log (AUC) datasets. Currently, the 80~125% rule based upon the parametric 90% CIs is widely accepted under the assumption of normally distributed formulation effects in log-transformed data. However, nonparametric CIs may be a better choice when data do not follow this assumption.

• Journal of Integrative Natural Science
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• v.15 no.1
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• pp.9-18
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• 2022

This research examines the power of bootstrap two-sample test, and compares it with the powers of two-sample t-test and Wilcoxon rank sum test, through simulation. For simulation work, a positively skewed and heavy tailed distribution was selected as a population distribution, the chi-square distributions with three degrees of freedom, χ²₃. For two independent samples, the fist sample was selected from χ²₃. The second sample was selected independently from the same χ²₃ as the first sample, and calculated d+ax for each sampled value x, a randomly selected value from χ²₃. The d in d+ax has from 0 to 5 by 0.5 interval, and the a has from 1.0 to 1.5 by 0.1 interval. The powers of three methods were evaluated for the sample sizes 10,20,30,40,50. The null hypothesis was the two population medians being equal for Bootstrap two-sample test and Wilcoxon rank sum test, and the two population means being equal for the two-sample t-test. The powers were obtained using r program language; wilcox.test() in r base package for Wilcoxon rank sum test, t.test() in r base package for the two-sample t-test, boot.two.bca() in r wBoot pacakge for the bootstrap two-sample test. Simulation results show that the power of Wilcoxon rank sum test is the best for all 330 (n,a,d) combinations and the power of two-sample t-test comes next, and the power of bootstrap two-sample comes last. As the results, it can be recommended to use the classic inference methods if there are widely accepted and used methods, in terms of time, costs, sometimes power.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.1-12
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• 2006

The aim of this study is to analyse a survey data on the number of charitable donations using a mixture of two Poisson regression models. The survey was conducted in 2002 by Volunteer 21, an nonprofit organization, based on Koreans, who were older than 20. The mixture of two Poisson distributions is used to model the number of donations based on the empirical distribution of the data. The mixture of two Poisson distributions implies the whole population is subdivided into two groups, one with lesser number of donations and the other with larger number of donations. We fit the mixture of Poisson regression models on the number of donations to identify significant covariates. The expectation-maximization algorithm is employed to estimate the parameters. We computed 95% bootstrap confidence interval based on bias-corrected and accelerated method and used then for selecting significant explanatory variables. As a result, the income variable with four categories and the volunteering variable (1: experience of volunteering, 0: otherwise) turned out to be significant with the positive regression coefficients both in the lesser and the larger donation groups. However, the regression coefficients in the lesser donation group were larger than those in larger donation group.