• 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.

• Journal of Korean Society of Transportation
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• v.14 no.1
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• pp.43-50
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• 1996

This paper is concerned with assessing goodness of fit of gravity models. The Chi-square test, or one of its asymptotic equivalents, is usually recommended for the purpose. A difficulty that frequently arises, particularly when working with urban travel data, is that the expected number of trips for most origin-destination(O-D) pairs are small. In order to test goodness of fit of gravity model, a simple approach, which depends on the number of O-D pairs and certain trip totals being large, is proposed in this paper. In addition, derivation of variance of Chi-square ratio is proposed to test the confidence interval of Chi-square ratio and application of its results with simulated data set is made to verify the usefulness of the results.

• Journal of applied mathematics & informatics
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• v.1 no.1
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• pp.43-48
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• 1994

There are some difficulties in applying the Pearson's Chi-Square Test for the continuous distribution. The problems include how to form class intervals for the test of fit how to employ in the test when the estimators of parameters are obtained from the ungrouped sample so on. In order to solve these problems we use the generalized minimum Chi-Square technique which is a test free of the complications associated with the Peason's Chi-Square test. This paper show how to apply the goodness of fit tests based on generalized minimum Chi-Square technique to the extreme values.

• Journal of the Korean Mathematical Society
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• v.47 no.6
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• pp.1137-1146
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• 2010

In this paper, we study the divergence based goodness of fit test for partially observed sample from diffusion processes. In order to derive the limiting distribution of the test, we study the asymptotic behavior of the residual empirical process based on the observed sample. It is shown that the residual empirical process converges weakly to a Brownian bridge and the associated phi-divergence test has a chi-square limiting null distribution.

• Journal of the Korean Statistical Society
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• v.12 no.1
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• pp.24-35
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• 1983

In fitting models to data, selection of the most desirable estimation method and determination of the adequacy of fitted model are the central issues. This paper compares the maximum likelihood estimators and the minimum logit chi-square estimators, both being best asymptotically normal, when logit models are fitted to infant mortality data. Chi-square goodness-of-fit test and likelihood ratio one are also compared. The analysis infant mortality data shows that the outlying observations do not necessarily result in the same impact on goodness-of-fit measures.

• 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.

• Water for future
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• v.7 no.2
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• pp.99-106
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• 1974

The records of wave heights which were observed at Muk ho and Po hang of the East Coast of Korea were analized by several probility functions. The exponential 2 parameter distribution was found as the best fit probability function to the historical distribution of wave heights by the test of goodness of fit. But log-normal 2 parameter and log-extremal type A distributions were also fit to the historical distribution, especially in the Smirnov-Kolmogorov test. Therefore, it can't be always regarded that those two distributions are not fit to the wave heiht's distribution. In the test of goodness of fit, the Chi-Square test gave very sensitive results and Smirnov-Kolmogorov test, which is a distribution free and non-parametric test, gave more inclusive results. At the next stage, the inter-relationship between the mean and the one-third wave heights, the mean and the one-=tenth wave heights, the one-third and the one-tenth wave heights, the one-third and the highest wave heights were obtained and discussed.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.1-25
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• 2022

A modified Bagdonavičius-Nikulin chi-square goodness-of-fit is defined and studied. The lymphoma data is analyzed using the modified goodness-of-fit test statistic. Different non-Bayesian estimation methods under complete samples schemes are considered, discussed and compared such as the maximum likelihood least square estimation method, the Cramer-von Mises estimation method, the weighted least square estimation method, the left tail-Anderson Darling estimation method and the right tail Anderson Darling estimation method. Numerical simulation studies are performed for comparing these estimation methods. The potentiality of the new model is illustrated using three real data sets and compared with many other well-known generalizations.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1245-1255
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• 2015

In this paper, we investigated the major 8 companies of Korean stock market, and carried out the goodness of fit and independence tests. We found out the distributions of absolute returns are closed to compressed exponential distribution. The parameters are dominant that 1 < ${\beta}$ < 2, followed by ${\beta}=1$(exponential distribution) and ${\beta}=2$(normal distribution). Meanwhile, we assured that most of the absolute returns for major 8 companies have relevance to each other by chi-square independence test.

• Journal of Korean Academy of Nursing
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• v.49 no.4
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• pp.437-448
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• 2019

Purpose: This study was conducted to test a path model for the factors related to undergraduate nursing students' clinical practicum stress, based on Lazarus and Folkman's stress-coping model. Methods: This study utilized a path analysis design. A total of 235 undergraduate nursing students participated in this study. The variables in the hypothetical path model consisted of clinical practicum, emotional intelligence, self-efficacy, Nun-chi, and nursing professionalism. We tested the fit of the hypothetical path model using SPSS/WIN 23.0 and AMOS 22.0. Results: The final model fit demonstrated a satisfactory statistical acceptance level: goodness-of-fit-index=.98, adjusted goodness-of-fit-index=.91, comparative fit index=.98, normed fit index=.95, Tucker-Lewis index=.92, and root mean square error of approximation=.06. Self-efficacy (${\beta}=-.22$, p=.003) and Nun-chi behavior (${\beta}=-.17$, p=.024) were reported as significant factors affecting clinical practicum stress, explaining 10.2% of the variance. Nursing professionalism (${\beta}=.20$, p=.006) and self-efficacy (${\beta}=.45$, p<.001) had direct effects on emotional intelligence, explaining 45.9% of the variance. Self-efficacy had indirect effects on Nun-chi understanding (${\beta}=.20$, p<.001) and Nun-chi behavior (${\beta}=.09$, p=.005) through emotional intelligence. Nursing professionalism had indirect effects on Nun-chi understanding (${\beta}=.09$, p=.005) and Nun-chi behavior (${\beta}=.09$, p=.005) through emotional intelligence. The variables for self-efficacy and nursing professionalism explained 29.1% of the Nun-chi understanding and 18.2% of the Nun-chi behavior, respectively. Conclusion: In undergraduate nursing education, it is important to identify and manage factors that affect clinical practicum stress. The findings of this study emphasize the importance of Nun-chi, self-efficacy, emotional intelligence, and nursing professionalism in the development of an educational strategy for undergraduate nursing students.