• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.415-421
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• 2002

Because most common assumption is normality in statistical analysis, testing normality is very important. We propose a new plot and test statistic to test for normality based on the modified Lorenz curve that is proved to be a powerful tool to measure the income inequality within a population of income receivers. We also compare the proposed test statistics with the W test (Shapiro and Wilk (1965)), TL test (Kang and Cho (1999)) in terms of the power of test through by Monte Carlo method. The proposed test is more usually powerful than the other tests except some case.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.883-891
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• 2011

For survival data we sometimes want to test a log normality hypothesis that can be changed into normality by transforming the survival data. Hence the Shapiro-Wilk type statistic for normality is generalized to randomly censored data based on the Kaplan-Meier product limit estimate of the distribution function. Koziol and Green (1976) derived Cram$\acute{e}$r-von Mises statistic's randomly censored version under the simpl hypothesis. These two test statistics are compared through a simulation study. As for the distribution of censoring variables, we consider Koziol and Green (1976)'s model and other similar models. Through the simulation results, we can see that the power of the proposed statistic is higher than that of Koziol-Green statistic and that the proportion of the censored observations (rather than the distribution of censoring variables) has a strong influence on the power of the proposed statistic.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.79-89
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• 2007

A test for normality introduced by Arizono and Ohta(1989) is based on fullback-Leibler discrimination information. The test statistic is derived from the discrimination information estimated using sample entropy of Vasicek(1976) and the maximum likelihood estimator of the variance. However, these estimators are biased and so it is reasonable to make use of unbiased estimators to accurately estimate the discrimination information. In this paper, Arizono-Ohta test for normality is improved. The derived test statistic is based on the bias-corrected entropy estimator and the uniformly minimum variance unbiased estimator of the variance. The properties of the improved KL test are investigated and Monte Carlo simulation is performed for power comparison.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.223-232
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• 2002

The present paper develops a test of the multivariate normality based on nonlinear transformations and the likelihood function. For checking the normality, we test the shape parameter which indexes the family of transformations. A score test and a parametric bootstrap test are used to evaluate the discrepancy between the data and a multivariate normal distribution. In order to compare the performance of our test with the existing tests, a simulation study was carried out for several situations where nuisance parameters have to be estimated. The results showed that the proposed method is superior to the existing methods.

• 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.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.37-42
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• 2005

통계학의 주요 관심인 표본의 정규성 검정을 위해 통계패키지에서 사용하고 있는 Q-Q(quantile-quantile) 플롯을 중도절단표본에서 사용함으로 발생하는 문제점을 알아보고 이를 보완하여 수정된 Q-Q플롯과 수정된 Normalized Sample Lorenz Curve(NSLC)을 제시한다. 예제로 Hodgkin's disease 데이터를 중도절단하여 새로 제시한 Normalized Sample Lorenz Curve을 그려보았다.

• The Korean Journal of Applied Statistics
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• v.14 no.2
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• pp.223-231
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• 2001

다변량분석 기법을 위해서는 자료가 정규성(normality)가정을 만족해야한다. 본 연구에서는 GUI환경에서 일변량 및 다변량자료의 정규성검정, 이상치제거 및 변수변환을 하는 시스템을 Visual Basic 언어로서 구축하여 사용자들이 보다 편리하게 사용할 수 있음을 소개 하고자 한다.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.253-255
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• 2000

다변량분석 기법을 사용하기 위해서는 자료가 정규성(normality)가정을 만족해야한다. 본 연구에서는 GUI(graphic user interface)환경 하에서 일변량(univariate)과 다변량자료(multivariate data)의 정규성검정, 이상치(outliers)제거 및 변수변환(variable transformation)을 지원하는 시스템을 구축하여 사용자들이 보다 편리하게 사용할 수 있음을 소개 하고자 한다.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.243-248
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• 2003

Fattorini(1986)의 통계량은 Shapiro와 Wilk의 일변량 정규분포를 위한 검정통계량을 다변량으로 확장한 것이다. 본 논문에서는 Kim과 Bickel(2003)에서 제안한 이변량 정규분포를 위한 검정통계량을 Fattorini(1986)의 방법을 이용하여 이변량 이상인 경우에도 실제적으로 사용가능하도록 일반화하였다. 제안된 통계량은 Fattorini(1986) 통계량의 근사통계량으로 생각할 수 있으며 표본의 크기가 클 때도 사용가능하다.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.429-436
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• 1999

We suggest test statistics for normality using Q-Q plot and P-P plot and obtain empirical quantities of these statistics. Also the power comparison with Shapiro-Wilk's W is conducted by Monte Carlo study.