• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.523-536
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• 2021

Although there exist numerous measures of association, most of them are lacking in generality in that they do not intend to measure the association between heterogeneous type of random variables. On the other hand, many statistical analyzes dealing with complex data sets require a very sophisticate measure of association. In this note, the p-value of independence tests is utilized to obtain a measure of association. The proposed measure of association have some consistency in measuring association between various types of random variables.

• The Korean Journal of Applied Statistics
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• v.4 no.1
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• pp.33-45
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• 1991

In the analysis of changepoint model the situation where single observation is taken at each time point has been considered. In an effort to extend this to the general situation, we may consider the changepoint model with more than one observation at each time point. These tests are developed without assuming any particular form for the underlying distribution, we propose the one-sided and two-sided nonparametric tests by extending the tests that have been considered in the changepoint model with single observation at each time point and obtain their asymptotic null distributions. We compare the empirical powers among the extended changepoint tests under one-sided or two-sided alternatives. We also compare the powers of the extended changepoint tests with those of the original test via the Monte Carlo simulation.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.39-57
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• 2001

Categorical data has sometimes misclassification errors. If this data will be analyzed, then estimated cell probabilities could be biased and the standard Pearson X2 tests may have inflated true type I error rates. On the other hand, if we regard wellclassified data with misclassified one, then we might spend lots of cost and time on adjustment of misclassification. It is a necessary and important step to ask whether categorical data is misclassified before analyzing data. In this paper, when data is misclassified at one of two variables for two-dimensional contingency table and marginal sums of a well-classified variable are fixed. We explore to partition marginal sums into each cells via the concepts of Bound and Collapse of Sebastiani and Ramoni (1997). The double sampling scheme (Tenenbein 1970) is used to obtain informations of misclassification. We propose test statistics in order to solve misclassification problems and examine behaviors of the statistics by simulation studies.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.657-663
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• 2012

There are very small values and/or very big values which get out of the normal range for survey data in various fields. The reasons of occurrence for outlier are two. One of them is the error in process of data input and the other is the strange response of the respondent. If the data has outliers, then the summary statistics such as the mean and the variance produce misleading information. Therefore, researcher should be careful in detecting the outlier in data. In particular, it is very important problem for clinical fields because the cost of experiment is very high. This article introduce the Grubb test and Cochran test to detect outliers in the data and we apply this method for clinical data.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1039-1055
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• 2014

The distribution of a test statistic under a null hypothesis depends on the unknown distribution of the data and thus is unknown as well. Conditional tests replace the unknown null distribution by the conditional null distribution, that is, the distribution of the test statistic given the observed data. This approach is known as permutation tests and was developed by Fisher (Fisher, 1935). Theoretical framework for permutation tests was given by Strasser and Weber(1999). The coin package developed by Hothon et al. (2006, 2008) implements a unified approach for conditional inference via the generic independence test. Because convenient functions for the most prominent problems are available, users will not have to use the extremely flexible procedure. In this article we briefly review the underlying theory from Strasser and Weber (1999) and explain how to transform the data to perform the generic function independence test. Finally it was illustrated with a few real data sets.

• 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.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.599-605
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• 1998

본 연구에서는 단순구조 순환신경망을 이용한 신경망예측과 전통적인 시계열예측 방법을 이용하여, 순환변동이 있는 시계열자료의 단기예측 오차를 비교한다. 순환신경망모형의 입력자료를 변화시키는 개선된 학습방법을 적용하여 시계열자료를 학습하고, 신경망예측의 결과는 선형 AR(9)모형, 비선형 SETAR모형 그리고 이들의 결합모형을 이용한 예측결과와 비교한다. 실증분석에 적용된 시계열자료는 1700년부터 1987년 까지의 태양흑점 자료이며 예측에 이용된 검정자료는 1980년부터 8년 간의 자료이다.

• 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 Korean Data and Information Science Society
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• v.28 no.4
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• pp.733-742
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• 2017

There are many areas of applications where Gumbel distribution are employed such as environmental sciences, system reliability and hydrology. The goodness-of-fit test for Gumbel distribution is very important in environmental sciences, system reliability and hydrology data analysis. Therefore, we propose the two test statistics to test goodness-of-fit for the Gumbel distribution based on the generalized Lorenz curve. We compare the new test statistic with the Anderson - Darling test, Cramer - vonMises test, and modified Anderson - Darling test in terms of the power of the test through by Monte Carlo method. As a result, the new test statistics are more powerful than the other test statistics. Also, we propose new graphic method to goodness-of-fit test for the Gumbel distribution based on the generalized Lorenz curve.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.1-11
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• 2014

The Weighted Logrank test and its special case, Logrank test are widely used to compare survival distributions; however, these methods are inappropriate when the sample size is small or censoring distributions are not equal since they use test statistics from approximate distributions. A permutation test can be an alternative for small sample cases; however, this should be used only when censoring distributions are equal. To handle cases with small sample size and unequal censoring distributions, the permutation-imputation method was developed to compare two survival distributions. In this paper, approximate method, permutation method and permutation-imputation method were compared using a Logrank test and Prentice-Wilcoxon test for three or more survival distributions comparison.