• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.167-174
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• 2022

In this paper we investigate the permutation test for the equality of correlation coefficients in several independent populations. Permutation test is a non-parametric testing methodology based upon the exchangeability of observations. Exchangeability is a generalization of the concept of independent, identically distributed random variables. Using permutation method, we may construct asymptotically exact test. This method is asymptotically as powerful as standard parametric tests and is a valuable tool when the sample sizes are small and normality assumption cannot be met. We first review existing parametric approaches to test the equality of correlation coefficients and compare them with the permutation test. At the end, all the approaches are illustrated using Iris data example.

• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.179-187
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• 2023

In this paper, we studied the impact of outliers on the power of the significance tests for Cronbach alpha reliability coefficient. Four variables were varied: sample size, the number of items, the number of outliers and population Cronbach Alpha levels. We simulated data using multivariate normal distribution and used outliers sampled from uniform distribution. To test the significance of Cronbach Alpha Reliability, parametric approach(F statistic) and permutation method were used. Consequently, we observed that the powers of permutation test are equal to or greater than those of F test under all conditions, and also both F test and permutation test lose the power as the number of outliers increases, and that these effects of outliers on the power are enhanced for increasing population alpha levels.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.157-164
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• 2021

Regression analysis is a well-known statistical technique useful to explain the relationship between response variable and predictor variables. In particular, Researchers are interested in comparing the regression coefficients(intercepts and slopes) of the models in two independent populations. The Chow test, proposed by Gregory Chow, is one of the most commonly used methods for comparing regression models and for testing the presence of a structural break in linear models. In this study, we propose the use of permutation method and compare it with Chow test analysis for testing the equality of two independent linear regression models. Then simulation study is conducted to examine the powers of permutation test and Chow test.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.899-910
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• 2016

Asymptotic tests are often not suitable for the analysis of sparse ordered contingency tables as asymptotic p-values may either overestimate or underestimate the true pvalues. In this pater, we describe permutation procedures in which we compute exact or resampling p-values for a weighted specific-category agreement in ordered $k{\times}k$ contingency tables. We use the weighted specific-category kappa proposed by $Kv{\dot{a}}lseth$ to measure the extent to which two independent raters agree on the specific categories. We carried out comparison studies between exact p-values, resampling p-values and asymptotic p-values using $3{\times}3$ contingency data (real and artificial data sets) and $4{\times}4$ artificial contingency data.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.175-182
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• 2020

Fisher's classical method for combining independent p-values from continuous distributions is widely used but it is known to be inadequate for combining p-values from discrete probability distributions. Instead, the discrete analog of Fisher's classical method is used as an alternative for combining p-values from discrete distributions. In this paper, firstly we obtain p-values from discrete probability distributions associated with multi-sample location test data (Fisher-Pitman test and Kruskall-Wallis test data) by permutation method, and secondly combine the permutaion p-values by the discrete analog of Fisher's classical method. And we finally compare the combined p-values from both the discrete analog of Fisher's classical method and Fisher's classical method.