• Journal of the Korean Data and Information Science Society
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• 제8권2호
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• pp.261-270
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• 1997

This paper is proposed the parametric empirical Bayes(EB) confidence intervals which corrects the deficiencies in the naive EB confidence intervals of the scale parameter in the Weibull distribution under item-censoring scheme. In this case, the bootstrap EB confidence intervals are obtained by the parametric bootstrap introduced by Laird and Louis(1987). The comparisons among the bootstrap and the naive EB confidence intervals through Monte Carlo study are also presented.

• 대한수의학회지
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• 제47권2호
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• pp.259-263
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• 2007

The choice of input distribution in quantitative risk assessments modeling is of great importance to get unbiased overall estimates, although it is difficult to characterize them in situations where data available are too sparse or small. The present study is particularly concerned with accommodation of uncertainties commonly encountered in the practice of modeling. The authors applied parametric and non-parametric bootstrap simulation methods which consist of re-sampling with replacement, in together with the classical Student-t statistics based on the normal distribution. The implications of these methods were demonstrated through an empirical analysis of trade volume from the amount of chicken and pork meat imported to Korea during the period of 1998-2005. The results of bootstrap method were comparable to the classical techniques, indicating that bootstrap can be an alternative approach in a specific context of trade volume. We also illustrated on what extent the bias corrected and accelerated non-parametric bootstrap method produces different estimate of interest, as compared by non-parametric bootstrap method.

• Journal of the Korean Data and Information Science Society
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• 제10권1호
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• pp.155-162
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• 1999

This paper is concerned with the empirical Bayes estimation of one of the two shape parameters(${\theta}$) in the Burr(${\beta},\;{\theta}$) type XII failure model based on type-II censored data. We obtain the bootstrap empirical Bayes confidence intervals of ${\theta}$ by the parametric bootstrap introduced by Laird and Louis(1987). The comparisons among the bootstrap and the naive empirical Bayes confidence intervals through Monte Carlo study are also presented.

• Communications for Statistical Applications and Methods
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• 제18권6호
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• pp.717-732
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• 2011

꼬리가 두꺼운 분포의 고분위수에 대한 신뢰구간을 구할 때 적절한 붓스트랩 방법은 무엇인가에 대해 알아보았다. 비모수적 방법과 모수적 방법, 그리고 준모수적 방법의 성능을 모의실험을 통해 비교하였다.

• Communications for Statistical Applications and Methods
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• 제4권2호
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• pp.523-532
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• 1997

We propose several estimators of the reliability function R of the two-parameter exponential distribution, and then compare those estimator in terms of the mean square error (MSE) through Monte Carlo method. We also consider the parametric bootstrap estimation. Using the parametric bootstrap estimator, we obtain the bootstrap confidence intervals for reliability function and compare the proposed bootstrap confidence intervals in terms of the length and the coverage probability through Monte Carlo method.

• Communications for Statistical Applications and Methods
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• 제27권2호
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• pp.177-187
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• 2020

This article is concerned with the issue of forecasting and evaluation of threshold-asymmetric volatility models for time series of count data. In particular, threshold integer-valued models with conditional Poisson and conditional negative binomial distributions are highlighted. Based on the parametric bootstrap method, some evaluation measures are discussed in terms of one-step ahead forecasting. A parametric bootstrap procedure is explained from which directional measure, magnitude measure and expected cost of misclassification are discussed to evaluate competing models. The cholera data in Bangladesh from 1988 to 2016 is analyzed as a real application.

• Journal of the Korean Statistical Society
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• 제27권4호
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• pp.397-405
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• 1998

We develop parametric bootstrap model selection criteria in an example to fit a random sample to either a general normal distribution or a normal distribution with prespecified mean. We apply the bootstrap methods in two ways; one considers the direct substitution of estimated parameter for the unknown parameter, and the other focuses on the bias correction. These bootstrap model selection criteria are compared with AIC. We illustrate that all the selection rules reduce to the one sample t-test, where the cutoff points converge to some certain points as the sample size increases.

• The Korean Journal of Physiology and Pharmacology
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• 제13권5호
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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.

• 한국습지학회지
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• 제20권2호
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• pp.105-115
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• 2018

수문기상자료의 빈도해석은 풍수해에 따른 대응 및 시설물의 설계기준에 있어 중요한 요소 중 하나이다. 일반적으로 수문기상자료에 대한 빈도해석의 경우 관측자료는 통계적으로 정상성을 가진다고 가정하고, 확률분포의 매개변수를 고려하는 매개변수적 방법을 적용하고 있다. 이러한, 매개변수적 빈도해석을 위해서는 신뢰성 있는 충분한 자료의 수집이 필요하지만, 강수량과 다르게 적설량의 경우 계절적 특성과 함께 최근에는 기후변화로 인한 적설량 관측일수 및 평균 최심신적설량이 감소하기 때문에 부족한 자료에 대한 문제점을 보완할 필요가 있다. 이에 본 연구에서는 매개변수 빈도해석 방법과 부족한 자료의 문제점을 보완할 수 있는 표본 재추출 기법인 Bootstrap방법과 SIR(Sampling Importance Resampling)알고리즘을 적용하여 적설량의 빈도해석을 실시하였다. 58개 기상관측소에 대해 재추출된 일 최대 최심신적설량 자료를 이용한 비매개변수적 빈도해석을 통해 확률적설량을 산정하고 이를 비교 분석하였다. 빈도별 확률적설량의 증감률을 검토한 결과 매개변수적 빈도해석과 비매개변수적 빈도해석에서 증감률을 나타내는 지점들이 대부분 일치하는 것으로 나타났다. 확률적설량은 관측 자료와 Bootstrap방법에서 -19.2%~3.9%, Bootstrap방법과 SIR알고리즘에서 -7.7%~137.8% 정도의 차이를 보였다. 표본 재추출 기법은 관측표본이 적은 적설량의 빈도해석 및 불확실성 범위의 제시가 가능함을 확인할 수 있었고, 이는 여름철 태풍과 같이 계절적 특성을 지닌 다른 자연재난의 해석에도 적용될 수 있을 것으로 판단된다.

• Journal of the Korean Statistical Society
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• 제28권3호
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• pp.359-370
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• 1999

We considered a change-point hazard rate model generalizing constant hazard rate model. This type of model is very popular in the sense that the Weibull and exponential distributions formulating survival time data are the special cases of it. Maximum likelihood estimation and the asymptotic properties such as the consistency and its limiting distribution of the change-point estimator were discussed. A parametric bootstrap method for finding confidence intervals of the unknown change-point was also suggested and the proposed method is explained through a practical example.