• Title, Summary, Keyword: 이표본 위치검정

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Sample Size Determination for One-Sample Location Tests (일표본 위치검정에서의 표본크기 결정)

  • Yeo, In-Kwon
    • The Korean Journal of Applied Statistics
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    • v.28 no.3
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    • pp.573-581
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    • 2015
  • We study problems of sample size determination for one-sample location tests. A simulation study shows that sample size calculations based on approximated distribution do not achieve the nominal level of power. We investigate sample size determinations based on exact distribution and with a power that attains the nominal level.

Sample size comparison for two independent populations (독립인 두 모집단 설계에서의 표본수 비교)

  • Ko, Hae-Won;Kim, Dong-Jae
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.6
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    • pp.1243-1251
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    • 2010
  • For clinical trials, it is common to compare the placebo and new drug. The method of calculating a sample size for two independent populations are the t-test that is used for parametric methods, and the Wilcoxon rank-sum test that is used in the non-parametric methods. In this paper, we propose a method that is using Kim's (1994) statistic power based on the linear placement statistic, which was proposed by Orban and Wolfe (1982). We also compare the sample size for the proposed method with that for using Wang et al. (2003)'s sample size formula which is based on Wilcoxon rank-sum test, and with that of t-test for parametric methods.

Sample size determination based on placements for non-inferiority trials (비열등성 시험에서 위치 방법에 기초한 표본 수 결정)

  • Kim, Jiyeon;Kim, Dongjae
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.6
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    • pp.1349-1357
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    • 2013
  • In clinical research, sample size determination is one of the most important things. There are parametric method using t-test and non-parametric method suggested by Kim and Kim (2007) based on Wilcoxon's rank sum test for determining sample size in non-inferiority trials. In this paper, we propose sample size calculation method based on placements method suggested by Orban and Wolfe (1982) and using the power calculated by Kim (1994) in non-inferiority trials. We also compare proposed sample size with that using Kim and Kim (2007)'s formula and that of t-test for parametric methods. As the result, sample size calculated by proposed method based on placements is the smallest. Therefore, proposed method based on placements is better than parametric methods in case that it's hard to assume specific distribution function for population and also more efficient in terms of time and cost than method based on Wilcoxon's rank sum test.

Power comparison of distribution-free two sample goodness-of-fit tests (이표본 분포 동일성에 대한 분포무관 검정법 간 검정력 비교 연구)

  • Kim, Seon Bin;Lee, Jae Won
    • The Korean Journal of Applied Statistics
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    • v.30 no.4
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    • pp.513-528
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    • 2017
  • Statistics are often used to test two samples if they have been drawn from the same underlying distribution. In this paper, we introduce several well-known distribution-free tests to compare distributions and conduct an extensive Monte-Carlo simulation to specify their behaviors. We consider various circumstances of when two distributions vary in (1) location, (2) scale, (3) symmetry, (4) kurtosis, (5) tail weight. A practical guideline for two-sample goodness-of-fit test is presented based on the simulation result.

Kullback-Leibler Information-Based Tests of Fit for Inverse Gaussian Distribution (역가우스분포에 대한 쿨백-라이블러 정보 기반 적합도 검정)

  • Choi, Byung-Jin
    • The Korean Journal of Applied Statistics
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    • v.24 no.6
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    • pp.1271-1284
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    • 2011
  • The entropy-based test of fit for the inverse Gaussian distribution presented by Mudholkar and Tian(2002) can only be applied to the composite hypothesis that a sample is drawn from an inverse Gaussian distribution with both the location and scale parameters unknown. In application, however, a researcher may want a test of fit either for an inverse Gaussian distribution with one parameter known or for an inverse Gaussian distribution with both the two partameters known. In this paper, we introduce tests of fit for the inverse Gaussian distribution based on the Kullback-Leibler information as an extension of the entropy-based test. A window size should be chosen to implement the proposed tests. By means of Monte Carlo simulations, window sizes are determined for a wide range of sample sizes and the corresponding critical values of the test statistics are estimated. The results of power analysis for various alternatives report that the Kullback-Leibler information-based goodness-of-fit tests have good power.

Window Configurations Comparison Based on Statistical Edge Detection in Images (영상에서 윈도우 배치에 따른 통계적 에지검출 비교)

  • Lim, Dong-Hoon
    • Communications for Statistical Applications and Methods
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    • v.16 no.4
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    • pp.615-625
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    • 2009
  • In this paper we describe Wilcoxon test and T-test that are well-known in two-sample location problem for detecting edges under different window configurations. The choice of window configurations is an important factor in determining the performance and the expense of edge detectors. Our edge detectors are based on testing the mean values of local neighborhoods obtained under the edge model using an edge-height parameter. We compare three window configurations based on statistical tests in terms of qualitative measures with the edge maps and objective, quantitative measures as well as CPU time for detecting edge.

Two-sample Tests for Edge Detection in Noisy Images (잡음영상에서 에지검출을 위한 이표본 검정법)

  • 임동훈;박은희
    • The Korean Journal of Applied Statistics
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    • v.14 no.1
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    • pp.149-160
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    • 2001
  • In this paper we employ two-sample location tests such as Wilcoxon test and T test for detecting edges in noisy images. For this, we compute a test statistic on pixel gray levels obtained using an edge-height parameter and compare it with a threshold determined by a significance level. Experimental results applied to sample images are given and performances of these tests in terms of the objective measure are compared.

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Trend Comparison of Repeated Measures Data between Two Groups (반복측정 자료에서 개체기울기를 이용한 집단간의 차이 검정법)

  • Hwang, Kum-Na;Kim, Dong-Jae
    • The Korean Journal of Applied Statistics
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    • v.19 no.3
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    • pp.565-578
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    • 2006
  • Repeated measurement data between two group is often used in the field of medicine study. In this paper, we suggest a method for comparison of the trend between two groups based on repeated measurement data. First, we estimate regression coefficient of linear regression model from each subject and generate samples using the regression coefficient estimated previous. And then, we test the difference between two groups by unpaired t-test, Wilcoxon rank sum test and placement test using generated samples. Monte Carlo Simulation is adapted to examine the power and experimental significance levels of several methods in various combinations.

지수분포의 검정을 위한 수정된 W-통계량

  • 김남현
    • Proceedings of the Korean Statistical Society Conference
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    • pp.141-146
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    • 2000
  • Shapiro와 Wilk(1972)는 위치모수와 척도모수가 미지인 경우 지수분포의 검정통계량을 제안하였다. 그것은 척도모수의 일반화 최소제곱추정량과 표본분산의 비로 구성되었다. 그러나 이 검정통계량은 일치성을 갖지 않는다. 본 논문에서는 척도모수의 두개의 점근유효추정량으로 구성된 통계량을 고려하고 이의 극한분포를 구하였다. 또한 두 개의 통계량의 검정력을 비교한 결과 제안된 통계량이 변동계수가 1보다 크거나 같은 분포에서 더 좋은 검정력을 가짐을 볼 수 있었다.

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Nonparametric method in one-way layout based on joint placement (일원배치법에서 결합위치를 이용한 비모수 검정법)

  • Jeon, Kyoung-Ah;Kim, Dongjae
    • The Korean Journal of Applied Statistics
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    • v.29 no.4
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    • pp.729-739
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    • 2016
  • Kruskal and Wallis (1952) proposed a nonparametric method to test the differences between more than three independent treatments. This procedure uses rank in mixed sample combined with more than three unlike populations. This paper proposes a the new procedure based on joint placements for a one-way layout as extension of the joint placements described in Chung and Kim (2007). A Monte Carlo simulation study is adapted to compare the power of the proposed method with previous methods.