• The Korean Journal of Applied Statistics
• /
• v.32 no.3
• /
• pp.331-348
• /
• 2019

Understanding the tumor heterogeneity due to differences in the growth pattern of metastatic tumors and rate of change is important for understanding the sensitivity of tumor cells to drugs and finding appropriate therapies. It is often possible to test for differences in population means using t-test or ANOVA when the group of N samples is distinct. However, these statistical methods can not be used unless the groups are distinguished as the data covered in this paper. Statistical methods have been studied to test heterogeneity between samples. The minimum combination t-test method is one of them. In this paper, we propose a maximum combinatorial t-test method that takes into account combinations that bisect data at different ratios. Also we propose a method based on the idea that examining the heterogeneity of a sample is equivalent to testing whether the number of optimal clusters is one in the cluster analysis. We verified that the proposed methods, maximum combination t-test method and gap statistic, have better type-I error and power than the previously proposed method based on simulation study and obtained the results through real data analysis.

• The Korean Journal of Applied Statistics
• /
• v.30 no.2
• /
• pp.301-309
• /
• 2017

It is often possible to test for differences in population means when two or more samples are extracted from each N population. However, it is not possible to test for the mean difference if one sample is extracted from each population since a sample mean does not exist. But, by dividing a group of samples extracted one by one into two groups and generating a sample mean, we can identify a heterogeneity that may exist within the group by comparing the differences of the groups' mean. Therefore, we propose a minimum combination t-test method that can test the mean difference by the number of combinations that can be divided into two groups. In this paper, we proposed a method to test differences between means to check heterogeneity in a group of extracted samples. We verified the performance of the method by simulation study and obtained the results through real data analysis.

• The Korean Journal of Applied Statistics
• /
• v.14 no.1
• /
• pp.149-160
• /
• 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.

• Communications for Statistical Applications and Methods
• /
• v.16 no.4
• /
• pp.615-625
• /
• 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.

• Journal of Korea Water Resources Association
• /
• v.46 no.3
• /
• pp.219-227
• /
• 2013

Time series data including change, jump, trend and periodicity generally have nonstationarity. Especially, various methods have been proposed to identify the trend about hydrological time series data. However, among various methods, evaluation about capability of each trend test has not been done a lot. Even for the same data, each method may show the different result. In this study, the simulation was performed for identification about the changes in trend analysis according to the statistical characteristics and the capability in the trend analysis. For this purpose, power test for the trend analysis is conducted using Men-Kendall test, Hotelling-Pabst test, t test and Sen test according to the slope, sample size, standard deviation and significance level. As a result, t test has higher statistical power than the others, while Mann-Kendall, Hotelling-Pabst, and Sen tests were similar results.

• Journal of Veterinary Clinics
• /
• v.27 no.2
• /
• pp.170-174
• /
• 2010

With the ease availability of statistical software and powerful computers the application of statistical methods in domestic veterinary journals is on the increase. In parallel with this benefit, statistical errors are not uncommon even in renowned scientific and medical journals. These errors may lead to misinterpretation of the data, thereby, subjected to faulty conclusions. A systematic review of articles published in 8 issues of the Journal of Veterinary Clinics during 2006-2007 was performed to assess the statistical methodology and reporting. Ninety-four (72.9%) articles of the 129 original articles screened included any inferential statistical analysis in the article, including comparison of 3 or more groups (53 or 56.4%), comparison of independent 2 groups (40 or 42.6%), and paired t-test (9 or 9.6%) in order. Of the 94 articles in which statistical analysis was done 62 (or 66.0%) had at least 1 statistical error. Errors included failure to apply or incorrectly applying independent Student's t-test for paired data or vice versa, inappropriate use of t-test for more than 3 groups and failure in chi-square test to consider continuity-correction for small expected frequencies. The common errors in ANOVA were failure to validate assumption of the test, inappropriate post-hoc multiple-comparison and incorrect assumption of independence of data in repeated measures design. Reporting errors included failure to state statistical methods and failure to state specific test if more than 1 test was done. It is suggested that an editorial effort would be necessary to achieve the improvement of appropriate statistical procedures through the publication of statistical guidelines to author(s).

• The Journal of Korean Academic Society of Nursing Education
• /
• v.15 no.1
• /
• pp.120-127
• /
• 2009

Purpose: In the literature power analysis in experimental studies is often executed and reported falsely. This descriptive study was done to promote the correct application of the Cohen(1988)'s power analysis method. Method: Articles of experimental studies from a nursing journal were selected and reviewed to examine the uses of and the reports on the power analysis process. Also, the appropriate power analysis process was discussed with an example of the most common experimental design, an independent two-group design with t test analysis plan. Result: Around half the experimental studies examined reported that they carried out power analysis. Cohen's method was the most frequently utilized but with accuracy in question. Conclusion: Power analysis to estimate sample size is the interplay between alpha, power, and effect size, and other factors in the case of t test analysis. Researchers should have a clear understanding of how to apply the Cohen's power analysis method so they do not produce poorly estimated sample sizes.

• The Korean Journal of Applied Statistics
• /
• v.24 no.6
• /
• pp.1271-1284
• /
• 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.

• The Korean Journal of Applied Statistics
• /
• v.31 no.2
• /
• pp.241-251
• /
• 2018

The analysis of gene sets in microarray has advantages in interpreting biological functions and increasing statistical powers. Many statistical methods have been proposed for detecting significant gene sets that show relations between genes and phenotypes, but there is no consensus about which is the best to perform gene sets analysis and permutation based tests are considered as standard tools. When many gene sets are tested simultaneously, a large number of random permutations are needed for multiple testing with a high computational cost. In this paper, several parametric approximations are considered as alternatives of the permutation distribution and the moment based gene set test has shown the best performance for providing p-values of the permutation test closely and quickly on a general framework.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.5
• /
• pp.895-904
• /
• 2012

This study aims to examine the effects of a 12-week compound exercise program (aerobic exercise+weight training) on obese middle school girls' leptin and insulin before and after the exercise. This is achieved by dividing obese middle school girls whose body fat percentage is over 30% into a compound exercise group (n=20) and a control group (n=20) and conducting comparative analysis on them.After the Shapiro-Wilk normality test of the variables, a two-sample t-test was performed to see if the variables have the same mean between the compound exercise and control groups. A paired t-test was also performed to see if the changes in the variables before and after the compound exercise program were statistically significant. For all the statistical analysis, the significance level was set at ${\alpha}=0.05$. The results of this study showed the leptin and insulin levels in the combined exercise group had been significantly decreased. The regular 12 weeks of combined exercise is considered to have a positive impact on leptin and insulin levels in obese schoolgirls.