• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.7-14
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• 2006

The finite failure NHPP models proposed in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, we propose the Erlang reliability model, which can capture the increasing nature of the failure occurrence rate per fault. Equations to estimate the parameters of the Erlang finite failure NHPP model based on failure data collected in the form of inter-failure times are developed. For the sake of proposing shape parameter of the Erlang distribution, we used to the goodness-of-fit test of distribution. Data set, where the underlying failure process could not be adequately described by the existing models, which motivated the development of the Erlang model. Analysis of the failure data set which led us to the Erlang model, using arithmetic and Laplace trend tests, goodness-of-fit test, bias tests is presented.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.891-901
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• 1997

The objective of this research is to investigate the problem of goodness of fit testing based on nonparametric density estimation with a data-driven smoothing parameter. The small and large smaple properties of the proposed test statistic $Z_{mn}$ are investigated with the minimizer $\widehat{m}$ of the estimated mean integrated squared error by the Diggle and Hall (1986) method.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.847-856
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• 2013

In this paper, we deal with testing goodness-of-fit for normal distribution based on parametric and nonparametric entropy estimators. The minimum variance unbiased estimator for the entropy of the normal distribution is derived as a parametric entropy estimator to be used for the construction of a test statistic. For a nonparametric entropy estimator of a data-generating distribution under the alternative hypothesis sample entropy and its modifications are used. The critical values of the proposed tests are estimated by Monte Carlo simulations and presented in a tabular form. The performance of the proposed tests under some selected alternatives are investigated by means of simulations. The results report that the proposed tests have better power than the previous entropy-based test by Vasicek (1976). In applications, the new tests are expected to be used as a competitive tool for testing normality.

• 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.

• Journal of Environmental Health Sciences
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• v.48 no.5
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• pp.266-271
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• 2022

Background: The selection of distributions of input parameters is an important component in probabilistic exposure assessment. Goodness-of-fit (GOF) methods are used to determine the distribution of exposure factors. However, there are no clear guidelines for choosing an appropriate GOF method. Objectives: The outcomes of probabilistic consumer exposure assessment were compared by using five different GOF methods for the selection of input distributions: chi-squared test, Kolmogorov-Smirnov test (K-S), Anderson-Darling test (A-D), Akaike information criterion (AIC) and Bayesian information criterion (BIC). Methods: Individual exposures were estimated based on product usage factor combinations from 10,000 respondents. The distribution of individual exposure was considered as the true value of population exposures. Results: Among the five GOF methods, probabilistic exposure distributions using the A-D and K-S methods were similar to individual exposure estimations. Comparing the 95th percentiles of the probabilistic distributions and the individual estimations for 10 CPs, there were 0.73 to 1.92 times differences for the A-D method, and 0.73 to 1.60 times differences (excluding tire-shine spray) for the K-S method. Conclusions: There were significant differences in exposure assessment results among the selection of the GOF methods. Therefore, the GOF methods for probabilistic consumer exposure assessment should be carefully selected.

• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.41-61
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• 2001

The minimum negative exponential disparity estimator(MNEDE), introduced by Lindsay(1994), is an excellenet competitor to the minimum Hellinger distance estimator(Beran 1977) as a robust and yet efficient alternative to the maximum likelihood estimator in parametric models. In this paper we define the negative exponential deviance test(NEDT) as an analog of the likelihood ratio test(LRT), and show that the NEDT is asymptotically equivalent to he LRT at the model and under a sequence of contiguous alternatives. We establish that the asymptotic strong breakdown point for a class of minimum disparity estimators, containing the MNEDE, is at least 1/2 in continuous models. This result leads us to anticipate robustness of the NEDT under data contamination, and we demonstrate it empirically. In fact, in the simulation settings considered here the empirical level of the NEDT show more stability than the Hellinger deviance test(Simpson 1989). The NEDT is illustrated through an example data set. We also define a goodness-of-fit statistic to assess adequacy of a specified parametric model, and establish its asymptotic normality under the null hypothesis.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.119-129
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• 2022

In this paper, we predict the earthquake magnitudes which were recently occurred in Gyeongju and Pohang, using statistical methods based on historical data. For this purpose, we use the five-year block maximum data of 1392~1771 period, which has a relatively high annual density, among the historical earthquake magnitude data of the Chosun Dynasty. Then, we present the prediction and analysis of earthquake magnitudes for the return level over return period in the Chosun Dynasty using the extreme value theory based on the distribution of generalized extreme values (GEV). We use maximum likelihood estimation (MLE) and L-moments estimation for parameters of GEV distribution. In particular, this study also demonstrates via the goodness-of-fit tests that the GEV distribution can be an appropriate analytical model for these historical earthquake magnitude data.

• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.155-162
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• 2017

In this paper we propose influence measures for two basic goodness-of-fit statistics, the coefficient of determination $R^2$ and test statistic F in the linear regression model using the deletion method. Some useful lemmas are provided. We also express the influence measures in terms of basic building blocks such as residual, leverage, and deviation that showed them as increasing function of residuals and a decreasing function of deviation. Further, the proposed measure reduces computational burden from O(n) to O(1). As illustrative examples, we applied the proposed measures to the stackloss data sets. We verified that deletion of one or few influential observations may result in big change in $R^2$ and F-statistic.

• International Journal of Reliability and Applications
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• v.7 no.1
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• pp.1-11
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• 2006

The better mean residual life at $t_0\;(BMRL-t_0)$ class of life distribution is introduced by Kulasekara and Park (1987). They proved that the $BMRL-t_0$ class contains the DMRL class, but it is a proper subclass of the NBUE class. In this paper we develop a new family of tests for testing exponentiality against the $BMRL-t_0\;(WMRL-t_0)$ alternatives based on the goodness of fit approach. It is shown that the suggested test is better than the one introduced by Kulasekara and Park (1987) in the sense of Pitman asymptotic efficiency values.

• Communications for Statistical Applications and Methods
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• v.13 no.2
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• pp.297-307
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• 2006

Most methods for describing the relationship among random variables require specific probability distributions and some assumptions of random variables. The mutual information based on the entropy to measure the dependency among random variables does not need any specific assumptions. And the redundancy which is a analogous version of the mutual information was also proposed. In this paper, the redundancy and mutual information are explored to multi-dimensional categorical data. It is found that the redundancy for categorical data could be expressed as the function of the generalized likelihood ratio statistic under several kinds of independent log-linear models, so that the redundancy could also be used to analyze contingency tables. Whereas the generalized likelihood ratio statistic to test the goodness-of-fit of the log-linear models is sensitive to the sample size, the redundancy for categorical data does not depend on sample size but its cell probabilities itself.