• Journal of the Korean Statistical Society
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• 제21권1호
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• pp.27-34
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• 1992

This paper provides a method of obtaining a lower bound on the probability of correct selection for a two-stage selection procedure. The resulting lower bound sharpens that by Tamhane and Bechhofer (1979) for the normal means problem with a common known variance. The design constants associated with the lower bound are computed and the results of the performance comparisons are given.

• 응용통계연구
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• 제33권4호
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• pp.439-449
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• 2020

메타 분석은 여러 연구 결과들을 종합시켜주는 분석 방법 중 하나이다. 이 때 수집되는 연구 문헌들은 소규모 연구인 경우 통계적으로 유의한 결과를 보이는 연구가 출간될 확률이 높기 때문에, 선택 편향의 특수한 경우인 출판 편향이 종종 발생한다. 선택 편향을 보정하는 방법에는 조건부 가능도와 가중 추정 방정식이 있는데 이 방법들은 실제 얻기 힘든 정확한 선택 확률 모형을 필요로한다. 반면 쌍별 유사가능도 접근법은 선택 확률 모형을 정확히 알 수 없는 경우에도 선택 편향을 보정할 수 있는 방법으로 제안되었다. 본 논문은 메타분석에서 쌍별 유사가능도 접근법의 성능과 문제점을 수치적으로 연구한다.

• Journal of the Korean Statistical Society
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• 제18권1호
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• pp.26-37
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• 1989

When we select the t best out of k populations in the indifference zone formulation, a lower confidence bound on the probability of a correct selection is derived for families with monotone likelihood ratio. The result is applied to the normal means problem when the variance is common, and to the normal variances problem. Tables to implement the confidence bound for the normal variances problem are provided.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.38-59
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• 2022

Web services instances can be classified into two categories, namely trusted and untrusted from users. A web service with high throughput (TP) and low response time (RT) instance values is a trusted web service. Web services are not trustworthy due to the mismatch in the guaranteed instance values and the actual values achieved by users. To perform web services selection from users' attained TP and RT values, we need to verify the correct prediction of trusted and untrusted instances from invoked web services. This accurate prediction of web services instances is used to perform the selection of web services. We propose to construct fuzzy rules to label web services instances correctly. This paper presents web services selection using a well-known machine learning algorithm, namely REPTree, for the correct prediction of trusted and untrusted instances. Performance comparison of REPTree with five machine learning models is conducted on web services datasets. We have performed experiments on web services datasets using a ten k-fold cross-validation method. To evaluate the performance of the REPTree classifier, we used accuracy metrics (Sensitivity and Specificity). Experimental results showed that web service (WS1) gained top selection score with the (47.0588%) trusted instances, and web service (WS2) was selected the least with (25.00%) trusted instances. Evaluation results of the proposed web services selection approach were found as (asymptotic sig. = 0.019), demonstrating the relationship between final selection and recommended trust score of web services.

• Communications for Statistical Applications and Methods
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• 제24권2호
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• pp.129-142
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• 2017

In this paper we consider the problem of the model selection/discrimination among three different positively skewed lifetime distributions. Lindley, Weibull, and gamma distributions have been used to effectively analyze positively skewed lifetime data. This paper assesses how much closer the Lindley distribution gets to Weibull and gamma distributions. We consider three techniques that involve the likelihood ratio test, asymptotic likelihood ratio test, and minimum Kolmogorov distance as optimality criteria to diagnose the appropriate fitting model among the three distributions for a given data set. Monte Carlo simulation study is performed for computing the probability of correct selection based on the considered optimality criteria among these families of distributions for various choices of sample sizes and shape parameters. It is observed that overall, the Lindley distribution is closer to Weibull distribution in the sense of likelihood ratio and Kolmogorov criteria. A real data set is presented and analyzed for illustrative purposes.

• Journal of the Korean Statistical Society
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• 제32권2호
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• pp.151-162
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• 2003

In this paper, we propose two selection procedures for selecting populations better than a control population. The bestness is defined in terms of location parameter. One of the procedures is based on two-sample linear rank statistics whereas the other one is based on a comparatively simple statistic, and is useful when testing time is expensive so that an early termination of an experiment is desirable. The proposed selection procedures are seen to be strongly monotone. Performance of the proposed procedures is assessed through simulation study.

• Journal of the Korean Statistical Society
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• 제25권2호
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• pp.195-203
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• 1996

A bootstrap method is used to correct the apparent downward bias of a naive plug-in bootstrap model selection criterion, which is shown to enjoy a high degree of accuracy. Comparison of bootstrap method with the asymptotic method is made through an illustrative example.

• 한국융합학회논문지
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• 제10권11호
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• pp.149-159
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• 2019

식스시그마는 시장과 고객의 패러다임과 트렌드의 변화에 맞추어 모든 사업의 프로세스와 전략을 개선하는 경영 혁신운동이다. 식스시그마 프로젝트 선정에 관한 기존의 연구는 있으나 불완전한 정보환경 하에서 프로젝트 선정을 위한 연구는 거의 없다. 본 연구의 목적은 불완전한 정보 하에서 올바른 프로젝트 선정을 위해 통합 MCDM 기법을 적용 방법을 제안하는 것이다. 식스시그마 프로젝트 선정을 위해 4단계인 1) 평가기준 간 가중치 결정 2) 팀 멤버 간 전문역량의 상대적 중요도 결정 3) 프로젝트 선호도 척도 산정 4) 최종 프로젝트 우선순위 결정 등을 위해 그룹 Fuzzy AHP, 불완전한 정보환경 하에서의 비퍼지화 TrFN 변환, GRA의 통합기법을 제안하였다. 본 연구에서 제안한 식스시그마 프로젝트 선정단계의 적용방안에 대한 이해를 돕기 위해 수치예가 제시되었다.

• Communications for Statistical Applications and Methods
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• 제1권1호
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• pp.41-51
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• 1994

We consider nonparametric procedures for the selection and ranking problems. Tukey's generalized lambda distribution is condidered as the distribution for the score function because the distribution can approximate many well-known contionuous distributions. Also we compare these procedures in terms of efficiency, defined by the ratio of a probability of a correct selection divided by the expected selected subset size.

• Communications for Statistical Applications and Methods
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• 제9권3호
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• pp.809-824
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• 2002

we propose two methods which separate the variable selection step and the split-point selection step. We call these two algorithms as CHITES method and F&CHITES method. They adapted some of the best characteristics of CART, CHAID, and QUEST. In the first step the variable, which is most significant to predict the target class values, is selected. In the second step, the exhaustive search method is applied to find the splitting point based on the selected variable in the first step. We compared the proposed methods, CART, and QUEST in terms of variable selection bias and power, error rates, and training times. The proposed methods are not only unbiased in the null case, but also powerful for selecting correct variables in non-null cases.