• 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.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.889-902
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• 2013

Several goodness-of-fit test statistics have been proposed for a multinomial logit regression model; however, the properties of the proposed tests were not adequately studied. This paper evaluates three different goodness-of-fit tests using grouping strategies, proposed by Fagerland et al. (2008), Bull (1994), and Pigeon and Heyse (1999). In addition, Pearson (1900)'s method is also examined as a reference. Simulation studies were conducted to evaluate the four methods in terms of null distribution and power. A real data example is presented to illustrate the methods.

• Journal of the Korean Society of Safety
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• v.33 no.1
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• pp.47-53
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• 2018

This study compares the interrelation of fit factor(FF) and visual acuity test by the order of wearing preference between Particulate filtering face piece respirators(PFFR) and glasses for 54 participants. Glasses fitting factors is Optical Center Height(OH), Vertex Distance(VD) and Pantoscopic Angle(PA) or Visual acuity. We measured those factors and expressed by the ratio of standard point and change point. Quantitative fit factor was measured by Portacount Pro+ 8038 and compared the result of preference of wearing order between respirators and glasses. Also, we selected to 6 exercises among 8 exercises OSHA QNFT (Quantitative Fit testing) protocol to measure the fit factors. The pass/ fail criterion of FF was set at 100. Visual acuity test chart is developed by Chunsuk Han was used, Descriptive statistics was performed. Descriptive statistics(SAS ver 9.2), it is used geometric means, Wilcoxon analysis, peason correlation(P=0.05) Fit factor was increased when the respirator was worn before wearing the glasses(p=0.000) and decreased for visual acuity(p=0.000) The negative correlation was showed between OH and Overall fit factor(r=-0.409, p=0.002). Among 54 participants, 11 participants(20.3%) were worn respirator before wearing glasses and 1 participant(1.9%) was worn glasses before wearing respirator. The overall fit test showed the higher level was investigated for the group of participants wearing respirator before wearing glasses in 6 exercises. Also, overall fit factor were increased when participants wore glasses prior to respirator(16.6) to respirator prior to glasses(36.6). Visual acuity were increased when participants wore respirator prior to glasses(93.8) to glasses prior to respirator(106.0). Finally, comparison result of overall fit factor and visual acuity were glasses first choice from mask first choice. The results showed that higher overall fit factor was investigate when the participants wore the respirator prior to glassess at all. The results implied that it is important to maintain the overall fit factor and visual acuity according to the consideration of OH for glasses fitting when worker wore respirator and glasses at the same time.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.139-155
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• 2000

An exclusive simulation study is conducted in computing means for order statistics in standard normal variate. Monte Carlo moments are used in Shapiro-Francia W' statistic computation. Finally, quantiles for Shapiro-Francia W' are generated. The study shows that in computing means for order statistics in standard normal variate, complicated distributions and intensive numerical integrations can be avoided by using Monte Carlo simulation. Lack of accuracy is minimal and computation simplicity is noteworthy.

• Journal of Korean Academy of Nursing Administration
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• v.21 no.2
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• pp.164-173
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• 2015

Purpose: This study was done to investigate the effects of individual-organization personality agreement using a five-factor model on job satisfaction and organizational commitment of hospital nurses. Methods: Participants were 222 nurses who had worked for more than 1 year in a university hospital. Data were collected from January 14 to 20, 2012, using self-recorded questionnaires. Collected data were analyzed using descriptive statistics and multiple regression methods. Results: Extraversion personality fit and the Agreeableness personality fit had a significant effect on job satisfaction. Extraversion personality fit, agreeableness personality fit, and openness personality fit had a significant effect on organizational commitment. Conclusion: Results of this study show that individual-organizational personality agreement affects hospital nurses' job satisfaction and organizational commitment. The extraversion personality fit and agreeableness personality fit of the 5 factors are identified as important variables to increase organizational performance. Based on these results, it is necessary to develop an integrated organizational personality measure model for increasing nurses' work environment satisfaction related to individual-organization personality fit.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1465-1475
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• 2013

The chi-square type test statistic is the most commonly used test in terms of measuring testing goodness-of-fit for multinomial logistic regression model, which has its grouped data (binomial data) and ungrouped (binary) data classified by a covariate pattern. Chi-square type statistic is not a satisfactory gauge, however, because the ungrouped Pearson chi-square statistic does not adhere well to the chi-square statistic and the ungrouped Pearson chi-square statistic is also not a satisfactory form of measurement in itself. Currently, goodness-of-fit in the ordinal setting is often assessed using the Pearson chi-square statistic and deviance tests. These tests involve creating a contingency table in which rows consist of all possible cross-classifications of the model covariates, and columns consist of the levels of the ordinal response. I examined goodness-of-fit tests for a proportional odds logistic regression model-the most commonly used regression model for an ordinal response variable. Using a simulation study, I investigated the distribution and power properties of this test and compared these with those of three other goodness-of-fit tests. The new test had lower power than the existing tests; however, it was able to detect a greater number of the different types of lack of fit considered in this study. I illustrated the ability of the tests to detect lack of fit using a study of aftercare decisions for psychiatrically hospitalized adolescents.

• 한국데이터정보과학회:학술대회논문집
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• 2004.10a
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• pp.61-61
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• 2004

• Wind and Structures
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• v.19 no.4
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• pp.371-387
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• 2014

Probability plotting positions are popular and used as the basis for distribution fitting and for inspecting the quality of the fit because of its simplicity. The plotting positions that lead to excellent approximation to the mean of the order statistics should be used if the objective of the fitting is to estimate quantiles. Since the mean depends on the sample size and is not amenable for simple to use closed form solution, many plotting positions have been presented in the literature, including a new plotting position that is derived based on the weighted least-squares method. In this study, the accuracy of using the new plotting position to fit the Gumbel distribution for estimating quantiles is assessed. Also, plotting positions derived by fitting the mean of the order statistics for all ranks is proposed, and an approximation to the covariance of the order statistics for the Gumbel (and Weibull) variate is given. Relative bias and root-mean-square-error of the estimated quantiles by using the proposed plotting position are shown. The use of the proposed plotting position to estimate the quantiles of annual maximum wind speed is illustrated.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.431-443
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• 2019

Goodness-of-fit techniques are an important topic in statistical analysis. Censored data occur frequently in survival experiments; therefore, many studies are conducted when data are censored. In this paper we mainly consider test statistics based on the empirical distribution function (EDF) to test normal distributions with unknown location and scale parameters when data are randomly censored. The most famous EDF test statistic is the Kolmogorov-Smirnov; in addition, the quadratic statistics such as the $Cram{\acute{e}}r-von$ Mises and the Anderson-Darling statistic are well known. The $Cram{\acute{e}}r-von$ Mises statistic is generalized to randomly censored cases by Koziol and Green (Biometrika, 63, 465-474, 1976). In this paper, we generalize the Anderson-Darling statistic to randomly censored data using the Kaplan-Meier estimator as it was done by Koziol and Green. A simulation study is conducted under a particular censorship model proposed by Koziol and Green. Through a simulation study, the generalized Anderson-Darling statistic shows the best power against almost all alternatives considered among the three EDF statistics we take into account.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.987-994
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• 2011

From the point view of credit evaluation whose population is divided into the default and non-default state, two methods are considered to estimate conditional distribution functions: one is to estimate under the assumption that the data is followed the mixture normal distribution and the other is to use the kernel density estimation. The parameters of normal mixture are estimated using the EM algorithm. For the kernel density estimation, five kinds of well known kernel functions and four kinds of the bandwidths are explored. In addition, the corresponding ROC functions are obtained based on the estimated distribution functions. The goodness-of-fit of the estimated distribution functions are discussed and the performance of the ROC functions are compared. In this work, it is found that the kernel distribution functions shows better fit, and the ROC function obtained under the assumption of normal mixture shows better performance.