• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.39-46
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• 1996

Most of existing nonparametric test statistics are based on the residuals which are obtained by regressing the data to a parametric model. In this paper we compare power of goodness-of-fit test statistics for testing the (null)parametric model versus the (alternative) nonparametric model.

• Journal of Korea Water Resources Association
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• v.46 no.6
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• pp.643-653
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• 2013

The most important procedure in frequency analysis is to determine the appropriate probability distribution and to estimate quantiles for a given return period. To perform the frequency analysis, the goodness-of-fit tests should be carried out for judging fitness between obtained data from empirical probability distribution and assumed probability distribution. The previous goodness-of-fit could not consider enough extreme events from the recent climate change. In this study, the critical values of the modified Anderson-Darling test statistics were derived for 3-parameter Weibull distribution and power test was performed to evaluate the performance of the suggested test. Finally, this method was applied to 50 sites in South Korea. The result shows that the power of modified Anderson-Darling test has better than other existing goodness-of-fit tests. Thus, modified Anderson-Darling test will be able to act as a reference of goodness-of-fit test for 3-parameter Weibull model.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.259-268
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• 2006

For Type-II censored sample, we propose three modified entropy estimators based on the Vasieck's estimator, van Es' estimator, and Correa's estimator. We also propose the goodness-of-fit tests of the Weibull distribution based on the modified entropy estimators. We simulate the mean squared errors (MSE) of the proposed entropy estimators and the powers of the proposed tests. We also compare the proposed tests with the modified Kolmogorov-Smirnov and Cramer-von-Mises tests which were proposed by Kang et al. (2003).

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.935-944
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• 1998

This paper investigates the problem of goodness of fit tests for no effect model. The proposed test statistic $Z_{mn}$ is obtained by multiplying constant on the model free curve estimation techniques. The small and large sample properties of$Z_{mn}$ are investigated and the good results of power studies for the proposed test are illustrated.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.645-660
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• 2001

In this paper we propose a goodness-of-fit test statistic for testing the (null) parametric model versus the (alternative) nonparametric model. Most of existing nonparametric test statistics are based on the residuals which are obtained by regressing the data to a parametric model. Our test is based on the bootstrap estimator of the probability that the smoothing parameter estimator is infinite when fitting residuals to cubic smoothing spline. Power performance of this test is investigated and is compared with many other tests. Illustrative examples based on real data sets are given.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.1-10
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• 1999

Table of the empirical quantiles for the well known Shapiro-Francia W' goodness of fit statistic is produced which is more accurate than the existing ones. Prediction equation for the quantiles of W' statistic for sample sizes 30 or more we developed. The process of computing the expected values for the standard normal variate is discussed. This work is intended to make the Shapiro-Francia W' statistic more accessible to the practitioner.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.133-137
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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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• v.16 no.2
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• pp.349-361
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• 2009

In this paper, we derive the approximate maximum likelihood estimators of the shape parameter and the scale parameter in a Weibull distribution under multiply Type-II censoring by the approximate maximum likelihood estimation method. We develop three modified empirical distribution function type tests for the Weibull distribution based on multiply Type-II censored samples. We also propose modified normalized sample Lorenz curve plot and new test statistic.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1057-1065
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• 2010

In this paper we discuss some cautionary notes in using the Pearson chi-squared test statistic for the goodness-of-fit of the ordinal response model. If a model includes continuous type explanatory variables, the resulting table from the t of a model is not a regular one in the sense that the cell boundaries are not fixed but randomly determined by some other criteria. The chi-squared statistic from this kind of table does not have a limiting chi-square distribution in general and we need to be very cautious of the use of a chi-squared type goodness-of-t test. We also study the limiting distribution of the chi-squared type statistic for testing the goodness-of-t of cumulative logit models with ordinal responses. The regularity conditions necessary to the limiting distribution will be reformulated in the framework of the cumulative logit model by modifying those of Moore and Spruill (1975). Due to the complex limiting distribution, a parametric bootstrap testing procedure is a good alternative and we explained the suggested method through a practical example of an ordinal response dataset.

• International Journal of Reliability and Applications
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• v.9 no.2
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• pp.125-140
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• 2008

The new better than used failure rate (NBUFR), Abouammoh and Ahmed (1988), and new better than average failure rate (NBAFR) Loh (1984) classes of life distributions, have been considered in the literature as natural weakenings of NBU (NWU) property. The paper considers testing exponentiality against strictly NBUFR (NBAFR) alternatives, or their duals, based on goodness of fit approach that is possible in life testing problems and that it results in simpler procedures that are asymptotically equivalent or better than standard ones. They may also have superior finite sample behavior. The asymptotic normality are proved. Powers, Pitman asymptotic efficiency and critical points are computed. Dealing with censored data case also studied. Practical applications of our tests in the medical sciences are present.