• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.717-732
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• 2011

We consider bootstrap confidence intervals for high quantiles of heavy-tailed distribution. A semi-parametric method is compared with the non-parametric and the parametric method through simulation study.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.355-366
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• 2002

Nonparametric statistical inference for the parameter of logit model were examined. Usually nonparametric approach is milder than parametric approach based on normal theory assumption. We compared the two nonparametric methods for legit model, the bootstrap and random permutation in the sense of coverage probability. Monte Carlo simulation is conducted for small sample cases. Empirical power of hypothesis test and coverage probability for confidence interval estimation were presented for simple and multiple legit model respectively. An example were also introduced.

• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.283-292
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• 2008

Smoothing parameter or bandwidth plays a key role in nonparametric classification based on kernel density estimation. We consider choosing smoothing parameter in nonparametric classification, which optimize the Bayes risk. Hall and Kang (2005) clarified the theoretical properties of smoothing parameter in terms of minimizing Bayes risk and derived the optimal order of it. Bootstrap method was used in their exploring numerical properties. We compare cross-validation and bootstrap method numerically in terms of optimal order of bandwidth. Effects on misclassification rate are also examined. We confirm that bootstrap method is superior to cross-validation in both cases.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.1179-1185
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• 2003

시뮬레이션 상의 입력모델에 대한 기존의 연구는 과거의 자료를 바탕으로 선형의 모수적인 (parametric) 모형을 개발하는데 초점을 두고 있다. 그러나 이 경우에는 입력이 매우 복잡한 형태를 가지면 모수적인 모형을 잦는 것이 불가능해지므로 비모수적인(non-parametric) 접근방법이 절실한 실정이다 예로 인터넷 트래픽 모델의 시뮬레이션 수행시 입력으로 제공되는 단위 시간당 요구되는 웹 페이지의 수 같은 경우 데이터들 간데 종속관계가 매우 심하고 복잡하여 모수적 모형을 세우는데 어려움이 있다. 이러한 시스템들을 시뮬레이션 방법으로 분석 하고자 할 때, 기존의 trace-driven 시뮬레이션 방법이나 모수적 모형을 찾아 다수의 사실적인 시뮬레이션 입력 자료를 확보하는 것은 현실적으로 어려움이 있다. 따라서. 비모수적인 방법으로 다수의 사실적인 시뮬레이션 입력 자료를 생성하는 것이 필요하다. 이러한 비모수적인 방법에 대한 평가기준 설정은 시뮬레이션 상의 입력 모델에 대한 타당성을 제시한다는 점에서 또한 매우 중요하다. 본 논문에서는 붓트스트 랩의 방법중의 하나인 임계값 붓트스트랩을 이용하여 시뮬레이션 입력 자료 생성 방법을 개발하였고 Turing test를 통해 붓스트랩으로 생성산 입력 시나리오를 검증하였다.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.221-226
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• 1995

본 연구는 붓스트랩에 의한 U-통계량의 분산추정방법을 제안하고, 추정량의 일치성을 증명하였다. 결과적으로 붓스트랩 추정량으로 표준화한 U-통계량의 값이 표준정규분포에 근사함을 보였다. 또한 실제적인 비모수검정에서 이를 응용하여 검정력과 특성을 연구하였다.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.461-473
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• 2014

We consider condence intervals for high quantiles of heavy-tailed distribution. The asymptotic condence intervals based on the limiting distribution of estimators are considered together with bootstrap condence intervals. We can also apply a non-parametric, parametric and semi-parametric approach to each of these two kinds of condence intervals. We considered 11 condence intervals and compared their performance in actual coverage probability and the length of condence intervals. Simulation study shows that two condence intervals (the semi-parametric asymptotic condence interval and the semi-parametric bootstrap condence interval using pivotal quantity) are relatively more stable under the criterion of actual coverage probability.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.375-382
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• 2010

In this study, we propose a robust estimator of the multivariate location parameter by means of the spatial median based on data trimming which extending trimmed mean in the univariate setup. The trimming quantity of this estimator is determined by the bootstrap method, and its covariance matrix is estimated by using the double bootstrap method. This extends the work of Jhun et al. (1993) to the multivariate case. Monte Carlo study shows that the proposed trimmed spatial median estimator yields better efficiency than a spatial median, while its covariance matrix based on double bootstrap overcomes the under-estimating problem occurred on single bootstrap method.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.611-622
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• 2021

With a wide recognition that financial time series typically exhibits asymmetry patterns in volatility so called leverage effects, various asymmetric GARCH(1, 1) processes have been introduced to investigate asymmetric volatilities. A lot of researches have also been directed to non-stationary volatilities to deal with frequent high ups and downs in financial time series. This article is concerned with both asymmetric and non-stationary GARCH-type models. As a subsequent paper of Choi et al. (2020), we review various asymmetric and non-stationary GARCH(1, 1) processes, and in turn propose how to compare competing models using a parametric bootstrap methodology. As an illustration, Dow Jones Industrial Average (DJIA) is analyzed.

• The Korean Journal of Applied Statistics
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• v.3 no.1
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• pp.121-141
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• 1990

Computer intensive bootstrap methods are studied as a tool of statistics. Practical calculation and theoretical justification problem of the methods in estimating the sampling distribution and construction confidence region of parameters are discussed through several examples. Statistical meaning of the methods are also considered.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.249-265
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• 1995

본 논문에서는 확률분포가 알려져 있지 않은 두 모집단 중 어느 하나로 새로운 관측치를 분류할 때 오분류확률이 분석자에 의해 사전에 정해진 수준에 부합할 수 있도록 커널 판별함수의 임계치를 결정하였다. 정해진 오분류확률을 만족시키기 위한 판별함수의 임계치는 붓스트랩(bootstrap)기법을 판별 함수에 적용시켜 계산된다. 본 논문에서 제시도된 방법은 모집단에 대한 모수적 가정이 없으므로 어느 분포에도 적용가능하며, 모집단이 정규분포, 대수정규분포, 이산형과 연속형 변수가 혼합된 분포의 경우 모의실험을 통하여 그 성능에 대한 검증을 하였다.