• Journal of the Korean Statistical Society
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• 제30권3호
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• pp.499-509
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• 2001

We investigate the influence of observations on a test of additional information about discrimination using the influence function and the derivative influence measures. the influence function for the test statistic is derived and this sample versions are used for influence analysis. The derivative influence measures for the test statistic under a perturbation scheme are derived. It will be seen that the influence function method and the derivative influence measures yield the same result. Furthermore, we will derive the relationships between the influence function and the derivative influence measures when the sample size is large. an illustrative example is given and we will compare the results provided by the influence function method and the derivative influence measures.

• Communications for Statistical Applications and Methods
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• 제23권5호
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• pp.433-444
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• 2016

When we observe binary responses in a cluster (such as rat lab-subjects), they are usually correlated to each other. In clustered binomial counts, the independence assumption is violated and we encounter an extra-variation. In the presence of extra-variation, the ordinary statistical analyses of binomial data are inappropriate to apply. In testing the homogeneity of proportions between several treatment groups, the classical Pearson chi-squared test has a severe flaw in the control of Type I error rates. We focus on modifying the chi-squared statistic by incorporating variance inflation factors. We suggest a method to adjust data in terms of dispersion estimate based on a quasi-likelihood model. We explain the testing procedure via an illustrative example as well as compare the performance of a modified chi-squared test with competitive statistics through a Monte Carlo study.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.385-392
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• 2000

Many tests for multivariate normality are based on the spherical coordinates of the scaled residuals of multivariate observations. Moore and Stubblebine's (1981) Pearson chi-square test is based on the radii of the scaled residuals, or equivalently the sample Mahalanobis distances of the observations from the sample mean vector. The chi-square statistic does not have a limiting chi-square distribution since the unknown parameters are estimated from ungrouped data. We will derive a simple closed form of the Rao-Robson chi-square test statistic and provide a self-contained proof that it has a limiting chi-square distribution. We then provide an illustrative example of application to a real data with a simulation study to show the accuracy in finite sample of the limiting distribution.

• International Journal of Reliability and Applications
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• 제6권1호
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• pp.53-64
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• 2005

In this paper, we propose a nonparametric test procedure for the multivariate, grouped and right censored data for two sample problem. For the construction of the test statistic, we use the linear rank statistics for each component and apply the permutation principle for obtaining the null distribution. For the large sample case, the asymptotic distribution is derived under the null hypothesis with the additional assumption that two censoring distributions are also equal. Finally, we illustrate our procedure with an example and discuss some concluding remarks. In appendices, we derive the expression of the covariance matrix and prove the asymptotic distribution.

• ETRI Journal
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• 제9권3호
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• pp.3-12
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• 1987

본 논문은 백색 가우스 부가잡음 채널에서 FSK협대역 신호를 수신하는 전형적인 통신 시스팀 및 이론적 문제를 다룬다. 변형된 자기상관 시퀀스를 이용하여 보다 개선된 수신성능을 꾀하기 위한 기술로서 시뮬레이션을 통해 제안된 시스팀 성능을 평가한다.

• Journal of the Korean Statistical Society
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• 제23권1호
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• pp.135-149
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• 1994

We discussed a comparison of distribution-free two-sample procedures based on placements or ranks. Iterative asymptotic distribution of both two-sample procedures is studies and small sample Monte Carlo simulation results are presented. Also, we proposed the Hodges-Lehmann type location estimator based on linear placement statistics.

• Communications for Statistical Applications and Methods
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• 제22권1호
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• pp.81-93
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• 2015

Validity of the stationary bootstrap of Politis and Romano (1994) is proved for U-statistics under strong mixing. Weak and strong consistencies are established for the stationary bootstrap of U-statistics. The theory is applied to a symmetry test which is a U-statistic regarding a kernel density estimator. The theory enables the bootstrap confidence intervals of the means of the U-statistics. A Monte-Carlo experiment for bootstrap confidence intervals confirms the asymptotic theory.

• Communications for Statistical Applications and Methods
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• 제6권2호
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• pp.357-366
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• 1999

Let us assume that two different log-linear models are selected by various model selection methods. When these are non-hierarchical it is not easy to choose one of these models. In this paper the well-known Cox's statistic is applied to compare these non-hierarchical log-linear models. Since it is impossible to obtain the analytic solution about the problem we proposed a alternative method by extending Pesaran and pesaran's (1993) simulation approach. We find that the values of proposed test statistic and the estimates are very much stable with some empirical results.

• 응용통계연구
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• 제24권2호
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• pp.383-391
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• 2011

이 논문에서는 역가우스분포의 적합을 위한 변형된 엔트로피 기반 검정을 제시한다. 이 검정은 자료생성분포와 역가우스분포의 엔트로피 차이에 기초를 두고 있으며 검정통계량은 엔트로피 차이의 추정량을 사용한다. 엔트로피 차이의 추정량은 자료생성분포에 대한 엔트로피 추정량으로 Vasicek의 표본엔트로피와 역가우스분포에 대한 엔트로피 추정량로 균일최소분산불편추정량을 사용하여 얻는다. 모의실험을 통해 얻은 표본크기와 윈도크기에 따른 검정통계량의 기각값들을 표의 형태로 제공한다. 제안한 검정의 검정력 알아보기 위해 여러 대립분포와 표본크기에 대해서 모의실험을 수행하고 기존의 엔트로피 기반 검정과 비교한다.

• 응용통계연구
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• 제6권2호
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• pp.289-302
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• 1993

본 연구에서는 지수분포성 적합도검정 문제를 다루는데, 모수가 미지인 상황에서 제1종단일 우측중도절단 표본과 제2종우측중도절단 표본인 경우에 각각 적용될 수 있는 새로운 검정통 계량을 제안하였다. 미지의 모수 문제를 해결하기 위해서 K-변환을 고려하였으며 중도절단 균일표본을 완결 균일표본으로 전환시키기 위해서 Rosenblatt 변환을 적용하였다. 전환된 완결 균일표본의 적합도검정을 위한 통계량은 분포함수와 경험분포함수의 편차의 $L_1-norm$ 으로 정의되었다. 검정 통계량의 임계치는 Monte Carlo simulation에 의해 구 했으며, 잘 알려진 Pettit 검정법과 검정력을 비교하였는데, 새로 제안된 검정통계량의 검정 력이 대체로 우수한 것으로 평가되었다.