• Journal of the Korean Data and Information Science Society
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• 제13권1호
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• pp.113-119
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• 2002

A powerful and easily computed goodness-of-fit test for Pareto distribution which does not depend on the unknown location and scale parameters is proposed based on the transformed sample Lorenz curve. We compare the power of the proposed test statistic with the other goodness-of-fit tests for Pareto distribution against various alternatives through Monte Carlo methods.

• Communications for Statistical Applications and Methods
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• 제16권4호
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• pp.697-705
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• 2009

The Pearson chi-squared statistic or the deviance statistic is widely used in assessing the goodness-of-fit of the generalized linear models. But these statistics are not proper in the situation of continuous explanatory variables which results in the sparseness of cell frequencies. We propose a goodness-of-fit test statistic for the cumulative logit models with ordinal responses. We consider the grouping of a dataset based on the ordinal scores obtained by fitting the assumed model. We propose the Pearson chi-squared type test statistic, which is obtained from the cross-classified table formed by the subgroups of ordinal scores and the response categories. Because the limiting distribution of the chi-squared type statistic is intractable we suggest the parametric bootstrap testing procedure to approximate the distribution of the proposed test statistic.

• Journal of applied mathematics & informatics
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• 제26권1_2호
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• pp.177-189
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• 2008

In this paper, we have considered an investigation on goodness of fit tests based on divergence measures. In the case of categorical data, under certain regularity conditions, we obtained asymptotic distribution of these tests. Also, we have proposed a modified test that improves the rate of convergence. In continuous case, we used our modified entropy estimator [10], for Kullback-Leibler information estimation. A comparative study based on simulation results is discussed also.

• Communications for Statistical Applications and Methods
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• 제7권1호
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• pp.277-284
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• 2000

The transformed sample Lorenz curve provides a powerful and easily computed goodness-of-fit test for exponentiality which does not depend on the unknown scale parameter. We compare the power of the transformed sample Lorenz curve statistic with the other goodness-of-fit tests for exponentiality against various alternatives through Monte Carlo methods and discuss the results.

• International Journal of Reliability and Applications
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• 제9권1호
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• pp.71-78
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• 2008

Based on the goodness of fit approach, a new test is presented for testing exponentiality versus exponential better (worse) than used (EBU (EWU)) class of life distributions. The new test is much simpler to compute, asymptotically normal, enjoys good power and performs better than previous tests in terms of power and Pitman asymptotic efficiencies for several alternatives.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.483-497
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• 2001

When one fits a parametric density function to a data set, it is usually advisable to test the goodness of the postulated model. In this paper we study the nonparametric tests for testing the null hypothesis against general alternatives, when the null hypothesis specifies the density function up to unknown parameters. We modify the test statistic which was proposed by the first author and his colleagues. Asymptotic distribution of the modified statistic is derived and its performance is compared with some other tests through simulation.

• Journal of the Korean Data and Information Science Society
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• 제28권4호
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• pp.733-742
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• 2017

통계학에서 사용되어지고 있는 Gumbel 분포는 환경과학, 시스템 신뢰성, 수문학과 같은 분야에서 많이 응용되고 있다. 따라서 환경과학, 시스템 신뢰성, 수문학과 관련된 자료를 분석함에 있어서 분석에 사용되어지는 자료가 Gumbel 분포를 따르는지 확인하는 것은 매우 중요하다. 이를 확인하기 위해 본 논문에서는 새로운 두 가지의 Gumbel 분포의 적합도 검정통계량을 일반화된 로렌츠 곡선을 기반으로 하여 제안하였고, Anderson - Darling 검정, Cramer - vonMises 검정, 수정된 Anderson - Darling 검정과 비교하였다. 그 결과 새롭게 제안한 검정통계량은 기존의 검정방법에 비하여 우수한 것을 확인할 수 있었다. 또한 새롭게 제안한 변형된 표본 일반화된 로렌츠 곡선을 이용하여 두 가지의 새로운 적합도 검정 그래프 방법을 제안하였고, 새롭게 제안된 그래프를 통하여 손쉽게 데이터가 Gumbel 분포를 따르는지를 파악 할 수 있었다. 또한 호주 시드니의 연간 일 최대 강수량 자료를 사용하여 새롭게 제안한 검정 통계량과 그래프 방법을 이용하여 적용해 보았다.

• 아동학회지
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• 제15권1호
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• pp.145-157
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• 1994

This research focused on the examination of a "Goodness of Fit" model with reference to the interaction effects of temperament and context. Two hundred forty 5th graders from urban and rural areas were administered the EAS (Emotionality, Activity, Sociability) and the Perceived Competence scales. The degree of satisfaction of mothers with their children's temperament was assessed for the context measure. Results of hierarchical multiple regression analyses showed that the interaction of children's emotion and mothers' satisfaction with children's emotion explained perceived social competence and general self-worth. This result supported the goodness of fit model. However, interaction effects were not found in children's perception of cognitive and physical competence. Also, children's activity and sociability showed strong main effects on perceived competence. It implies that activity and sociability should be applied to the personological model. The implication of the findings for following studies of goodness of fit model were discussed.

• Communications for Statistical Applications and Methods
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• 제26권2호
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• pp.191-203
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• 2019

The problem of examining how well an assumed distribution fits the data of a sample is of significant and must be examined prior to any inferential process. The observed failure time data of items are often not wholly available in reliability and life-testing studies. Lowering the expense and period associated with tests is important in statistical tests with censored data. Goodness-of-fit tests for perfect data can no longer be used when the observed failure time data are progressive Type II censored (PC) data. Therefore, we propose goodness-of-fit test statistics and a graphical method based on generalized Lorenz curve for PC data from a location-scale distribution. The power of the proposed tests is then assessed through Monte Carlo simulations. Finally, we analyzed two real data set for illustrative purposes.

• Communications for Statistical Applications and Methods
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• 제21권4호
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• pp.309-316
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• 2014

Testing normality is very important because the most common assumption is normality in statistical analysis. We propose a new plot and test statistic to goodness-of-fit test for normality based on the generalized Lorenz curve. We compare the new plot with the Q-Q plot. We also compare the new test statistic with the Kolmogorov-Smirnov (KS), Cramer-von Mises (CVM), Anderson-Darling (AD), Shapiro-Francia (SF), and Shapiro-Wilks (W) test statistic in terms of the power of the test through by Monte Carlo method. As a result, new plot is clearly classified normality and non-normality than Q-Q plot; in addition, the new test statistic is more powerful than the other test statistics for asymmetrical distribution. We check the proposed test statistic and plot using Hodgkin's disease data.