• 대한산업공학회지
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• 제23권4호
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• pp.755-767
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• 1997

Analyzing autocorrelated data set is still an open problem. Developing on easy and efficient method for severe positive correlated data set, which is common in simulation output, is vital for the simulation society. Bootstrap is on easy and powerful tool for constructing non-parametric inferential procedures in modern statistical data analysis. Conventional bootstrap algorithm requires iid assumption in the original data set. Proper choice of resampling units for generating replicates has much to do with the structure of the original data set, iid data or autocorrelated. In this paper, a new bootstrap resampling scheme is proposed to analyze the autocorrelated data set : the Threshold Bootstrap. A thorough literature search of bootstrap method focusing on the case of autocorrelated data set is also provided. Theoretical foundations of Threshold Bootstrap is studied and compared with other leading bootstrap sampling techniques for autocorrelated data sets. The performance of TB is reported using M/M/1 queueing model, else the comparison of other resampling techniques of ARMA data set is also reported.

• Journal of the Korean Statistical Society
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• 제30권1호
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• pp.1-20
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• 2001

We present how bootstrap methods can be used to conduct inference on the rates of Poisson distributions when only the grouped data are available. A theoretical justification for the validity of bootstrap is given with an illustration of proposed method using a data set obtained fro ma pathology laboratory test. Traditional asymptotic methods are compared with bootstrap methods in computing the estimated standard errors and achieved significance levels for one sample and two sample tests. Bootstrap methods are shown to possess a nice property that he small sample distribution of the relevant statistics can be readily obtained from the bootstrap copies.

• 응용통계연구
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• 제34권4호
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• pp.611-622
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• 2021

본 논문에서는 변동성의 비대칭성과 비정상성을 동시에 고려하고 있다. 다양한 변동성 모형을 분석하고 있으며 모수적-붓스트랩을 통한 예측분포를 이용하여 변동성 모형의 예측 성능을 비교하고 있다. 오차항 분포로서 표준정규분포 및 표준화 t-분포를 고려하였으며 1-시차 후 예측과 2-시차 후 예측을 미국의 다우지수 사례를 통해 설명하였다.

• 응용통계연구
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• 제35권1호
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• pp.1-17
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• 2022

본 논문에서는 모수적 부트스트랩을 이용한 두 차등정보보호 히스토그램의 동질성 검정을 제안한다. 제안된 검정 방법은 차등정보보호 히스토그램과 적용된 차등정보보호 수준 정보만 있을 때에도 사용 가능하며, 비교하고자 하는 두 히스토그램에 적용된 차등정보보호의 수준이 다를 때에도 사용할 수 있다는 장점이 있다. 검정 방법의 성능을 평가하기 위해 미국과 한국의 연령별 인구분포 자료를 사용하고, 제 1종 오류의 확률이 잘 통제됨과 높은 검정력을 확인한다.

• 한국산업경영시스템학회:학술대회논문집
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• 한국산업경영시스템학회 2002년도 춘계학술대회
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• pp.365-370
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• 2002

In this study, according to MARMA(1,0) model which was suggested by Seppala, in case of existing autocorrelation in X control chart and EWMA control chart, the standard method and the non-parametric bootstrap method were compared and analysed using the bootstrap method which use the resampling prediction residual.

• 산업경영시스템학회지
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• 제25권4호
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• pp.1-7
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• 2002

Statistical process control is used widely as an effective tool to solve the quality problems in practice fields. All the control charts used in statistical process control are parametric methods, suppose that the process distributes normal and observations are independent. But these assumptions, practically, are often violated if the test of normality of the observations is rejected and/or the serial correlation is existed within observed data. Thus, in this study, to screening process, the Combined Shewhart - CUSUM quality control chart is described and evaluated that used bootstrap method. In this scheme the CUSUM chart will quickly detect small shifts form the goal while the addition of Shewhart limits increases the speed of detecting large shifts. Therefor, the CSC control chart is detected both small and large shifts in process, and the simulation results for its performance are exhibited. The bootstrap CSC control chart proposed in this paper is superior to the standard method for both normal and skewed distribution, and brings in terms of ARL to the same result.

• 한국임상수의학회지
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• 제27권1호
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• pp.66-70
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• 2010

Reference interval is critical for interpreting laboratory results, monitoring response to therapy and predicting the prognosis of the patients in clinical settings. The aim of the present study was to update established reference intervals for routine hematologic and serum chemistry values for a population of clinically healthy dogs (range, 1-8 years) seen in an animal hospital. Blood was obtained by venipuncture while animals were physically restrained, and samples were analyzed for 9 chemistries on MS9-5H (Melot Schloesing Lab, France) and 6 hematology on Vet Test 8008 (IDEXX, USA). Data from 105 dogs (52 males and 53 females) for hematology and 113 dogs (37 males and 76 females) for chemistry were used to determine reference intervals using the parametric, nonparametric and bootstrap methods. Prior to analysis, all parameters were tested for normal distribution using Anderson-Darling criterion. Of the 9 biochemical analytes, alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, creatinine, total protein, and glucose concentrations did not fit normal distribution for both original and transformed data. All but eosinophil count satisfied normal distribution for either original or transformed data. Parametric method can be used for original cholesterol concentrations, RBC, WBC, and neutrophil counts. This technique can also be used for power-transformed values of blood urea nitrogen concentrations and for logarithm of lymphocyte and monocyte counts. Non-parametric or bootstrap method was the preferred choice for the remaining 7 biochemical parameters and eosinophil count as they did not follow normal distributions. All three statistical techniques performed in similar reference intervals. When establishing reference intervals for clinical laboratory data, it is essential to assess the distribution of the original data to increase the accuracy of the interval, and non-parametric or bootstrap methods are of alternative for the data that do not fit normal distribution.

• 산업경영시스템학회지
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• 제23권57호
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• pp.123-129
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• 2000

The Bootstrap method proposed by Efron is non-parametric method which doesn't depend on the estimation of prior distribution refer to population. A typical statistical process control chart which is generally used is developed under the assumption that observations follow mutually independent and identically distributed within a sample and between samples. However, autocorrelation greatly affect the developed control chart under the assumption that observations are mutually independent. Many researchers showed that the result which was analyzed by using a typical control chart for the observations which has the correlation violated to the independence assumption can not be true. Therefore, we compared the standard method with bootstrap method and then evaluated them for x control chart and EWMA control chart by using bootstrap method which was proposed by Efron in the AR(1) model when the observations have correlation.

• 한국수자원학회논문집
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• 제36권3호
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• pp.399-411
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• 2003

Nash의 관측평균순간단위도의 신뢰구간을 결정하는 기법을 개발하였다. 이 방법은 두 매개변수를 Box-Cox 변환과 유역의 상사성관계식을 이용하여 이변수정규분포의 확률변수화하고 이들의 선형 상관관계를 이용한 통계적 추정과정과 더불어 parametric bootstrap 방법을 이용한 단위도의 신뢰구간 산정 등으로 구성된다. 또한 이 방법은 미계측유역에 대한 단위도 추정에도 이용이 가능한 특징을 갖고 있다. 위천유역에 대하여 제안된 방법을 적용한 결과 제시된 방법론은 단위도의 불확실성을 평가하고 그리고 미계측 유역에 대한 매개변수 추정에 있어서 적절한 대안임을 확인할 수 있었다.

• 응용통계연구
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• 제22권1호
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• pp.153-161
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• 2009

We consider a logistic model with random effects as the superpopulation for estimating the small area pro-portions. The best linear unbiased predictor under linear mired model is popular in small area estimation. We use this type of estimator under logistic mixed motel for the small area proportions, on which the estimation of mean squared error is also discussed. Two kinds of estimation methods, the parametric bootstrap and the linear approximation will be compared through a Monte Carlo study in the respects of the normality assumption on the random effects distribution and also the magnitude of sample sizes on the approximation.