• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.129-137
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• 2002

Two-sample problem is frequently discussed problem in statistics. In this paper we consider the hypothese methods for the general two-sample problem and suggest the bootstrap methods. And we show that the modified Kolmogorov-Smirnov test is more efficient than the Kolmogorov-Smirnov test.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.477-489
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• 1998

위험률 변화점모형에 대해 변화점의 최우추정을 고려하였다. 추정량의 점근분포 및 붓스트랩 분포의 성질을 알아보고 변화점의 신뢰구간을 제안한다. 변화점의 위치 및 변화점을 전후하여 위험률의 값에 따라 모의실험을 수행하고 포함확률을 조사하였다. 추정량의 점근분포가 매우 복잡하기 때문에 이를 직접 이용한 변화점의 통계적 추론이 매우 어려운 점을 감안할 때 제안된 방법은 바람직한 대안이 될 수 있다.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.167-175
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• 1997

확률화 블럭 계획법에서 동위회귀를 이용하여 우산형 대립가설에 대한 비모수검정법을 제안하고자 한다. 제안된 검정통계량은 Mack과 Wolfe (1981)의 통계량에서 처리들에 가중치를 준 형태가 되며, 동위회귀를 이용하여 확률변수인 가중치를 구하고 붓스트랩을 이용한 소표본에서 모의 실험을 통하여 몇가지 사례 및 분포에 대해 제안된 통계량의 검정력을 알아본다.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1996.10a
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• pp.205-210
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• 1996

The bootstrap procedure is suggested as a useful method for point and interval estimation of system availability . Its validity and robustness has been shown in special , but representative case, by various sampling experiments. Alternative to the bootstrap suggest themselves (e.g. a variation of the 'F' technique, but remain to be evaluated, as do variations on the bootstrap itself.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.301-309
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• 2009

Multidimensional scaling(MDS) is a statistical multivariate analysis technique that is often used in information visualization for exploring similarities or dissimilarities in data. In order to analyse and visualize data, MDS measures the dissimilarities between objects and uses them or their mean if they are repeatedly measured. When there exist outliers or when the variation of data is too large, we can hardly get reliable results on the research using MDS. In this paper, we consider the MDS based on bootstrap method when the variation of data is large. Standardized residual sum of squares is considered as measuring goodness-of-fit of the model. A real data analysis is include to examine our approach.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.163-175
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• 1998

The change of hazard rates at some unknown time point has been the interest of many statisticians. But it was restricted to the constant hazard rates which correspond to the exponential distribution. In this paper we generalize the change-point model in which any specific functional forms of hazard rates are net assumed. The assumed model includes various types of changes before and after the unknown time point. The Nelson estimator of cumulative hazard function is introduced. We estimate the change-point maximizing slope changes of Nelson estimator. Consistency and asymptotic distribution of bootstrap estimator are obtained using the martingale theory. Through a Monte Carlo study we check the performance of the proposed method. We also explain the proposed method using the Stanford Heart Transplant Data set.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.409-422
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• 2007

Filtering genes that do not appear to contribute to regulation prior to the statistical analysis of time course gene expression data can reduce the dimensions of data and the possibility of misinterpretation due to noise or lack of variation. In this paper, we compare six different functions for filtering genes with flat pattern under the percentile criterion on an observed sample and that on a bootstrap sample. The result of applying to the yeast cell cycle data shows that the variance function is most similar in both samples.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.977-991
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• 2008

This study is about the k-nearest neighbor-based approach for the estimation of mutual information when the type of target variable is categorical and continuous. The results of Monte-Carlo simulation and experiments with real-world data show that k=1 is preferable. In practical application with real world data, our study shows that jittering and bootstrapping is needed.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1235-1243
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• 2010

This study proposes a trimmed LAD(least absolute deviation) estimators for multi-dimensional contingency tables and suggests an algorithm to estimate it. In addition, a method to determine the trimming quantity of the estimators is suggested. A Monte Carlo study shows that the propose method yields a better trimming rate and coverage rate than the previously suggest method based on the determinant of the covariance matrix.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.223-232
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• 2002

The present paper develops a test of the multivariate normality based on nonlinear transformations and the likelihood function. For checking the normality, we test the shape parameter which indexes the family of transformations. A score test and a parametric bootstrap test are used to evaluate the discrepancy between the data and a multivariate normal distribution. In order to compare the performance of our test with the existing tests, a simulation study was carried out for several situations where nuisance parameters have to be estimated. The results showed that the proposed method is superior to the existing methods.