• 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.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.707-717
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• 2016

Nonparametric procedures in a randomized block design was proposed by Friedman (1937) as a general alternative as well as suggested as a test for ordered alternatives by Page (1963). These methods are used for the rank of treatments in each block. In this paper, we proposed nonparametric procedures using aligned method proposed by Hodges and Lehmann (1962) to reduce among block information and based on placement suggested by Kim (1999) in a randomized block design. We also perform a Monte Carlo study to compare the empirical powers of the proposed procedures and established method.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1399-1409
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• 2016

Nonparametric methods in randomized block design were suggested by Friedman (1937) for general alternatives and were also proposed by Page (1963) for ordered alternatives in one-way layout; in addition, K-sample rank tests for umbrella alternatives were suggested by Mack and Wolfe (1981). In this paper, we proposed a nonparametric method of umbrella alternatives for randomized block design using the aligned method proposed by Hodges and Lehmann (1962) to use block information and using placement suggested by Kim (1999). Monte Carlo simulation was also adapted to compare the power of the proposed procedure with previous methods.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.1027-1036
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• 2017

Multiple comparisons are required to confirm whether or not something is significant if the null hypothesis to test whether the difference between more than three treatments is rejected in a one-way layout. There are both parametric multiple comparison method Tukey (1953) and Nonparametric multiple comparison method based on Kruskal-Wallis (1952).This procedure is applied to a mixed sample of all data and then an average ranking is used for each of three or more treatments. In this paper, a new nonparametric multiple comparison procedure based on joint placements for a one-way layout as extension of the joint placements described in Chung and Kim (2007) was proposed. Monte Carlo simulation is also adapted to compare the family wise error rate (FWE) and the power of the proposed method with previous methods.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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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.

• The Korean Journal of Applied Statistics
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• v.23 no.3
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• pp.487-495
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• 2010

This paper develops a goodness of fit test statistic to test if the progressively Type II censored sample comes from an exponential distribution with origin known. The test is based on normalizing spacings and Stephens (1978)' modified Shapiro and Wilk (1972) test for exponentiality. The modification is for the case where the origin is known. We applied the same modification to Kim (2001a)'s statistic, which is based on the ratio of two asymptotically efficient estimates of scale. The simulation results show that Kim (2001a)'s statistic has higher power than Stephens' modified Shapiro and Wilk statistic for almost all cases.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1181-1189
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• 2015

The treatment effect in clinical tests depending on dose of the drug; however, it can show a decreasing trend in fixed dose level due to side effects. The trend is known as an umbrella pattern; in addition, the method for the umbrella alternative is quite useful when the tendency is predicted in advance. In this paper, we propose a nonparametric method of umbrella alternatives for a one-way layout by using linear placement described in Orban and Wolfe (1982). The Monte Carlo simulation is adapted to compare the power of proposed procedure with previous methods.

• 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.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2805-2812
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• 2018

The methylation score, expressed as a percentage of the methylation status data derived from the iterative sequencing process, has a value between 0 and 1. It is contrary to the assumption of normal distribution that simply applying the t-test to examine the difference in population-specific methylation scores in these data. In addition, since the result may vary depending on the number of repetitions of sequencing in the process of methylation score generation, a method that can analyze such errors is also necessary. In this paper, we introduce the symbolic data analysis and the interval K-S test method which convert observation data into interval data including uncertainty rather than one numerical data. In addition, it is possible to analyze the characteristics of methylation score by using Beta distribution without using normal distribution in the process of converting into interval data. For the data analysis, the nature of the proposed method was examined using sequencing data of actual patients and normal persons. While the t-test is only possible for the location test, it is found that the interval type K-S statistic can be used to test not only the location parameter but also the heterogeneity of the distribution function.

• 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.