• The Korean Journal of Applied Statistics
• /
• v.10 no.1
• /
• pp.85-104
• /
• 1997

Proportional hazards model has been widely used for analyzing survival data. This article reviews some well-known goodness-of-fit tests for proportional hazards model. Simulation studies also provide some insights into the properties of these test statistics across several types of survival distributions and degerees of censorship.

• Journal of Fisheries and Marine Sciences Education
• /
• v.27 no.3
• /
• pp.656-671
• /
• 2015

The purpose of the present study is to examine relationships between disconnection and rejection schema, defense mechanism, and mental health in college students using structural equation modeling. The present study suggested a proposed model in which defense mechanism exerted a full mediating effect on the relation between disconnection and rejection schema and mental health. Goodness of fit tests were used to compare the proposed model against competing models. The subjects consisted of 304 college students. They completed the Young Schema Questionnaire(YSQ-SF), the Defense Style Questionnaire(DSQ), and the Mental Health Scale. The results showed that the second model had a better goodness of fit. Based on these findings, it is suggested that psychological interventions for mental health in college students should consider strategies to use more flexible and more adaptive defense mechanism style.

• The Korean Journal of Applied Statistics
• /
• v.9 no.1
• /
• pp.75-89
• /
• 1996

This paper proposes a goodness-of-fit test for checking the adequacy of the additive risk model with a binary covariate. The test statistic is based on martingale residuals, which is the extended form of Wei(1984)'s test. The proposed test is shown to be consistent and asymptotically normally distributed under the regularity conditions. Furthermore, the test procedure is illustrated with two set of real data and the results are discussed.

• Journal of Korean Society for Quality Management
• /
• v.31 no.4
• /
• pp.194-202
• /
• 2003

In this paper, we propose goodness of fit test statistics for exponentiality in accelerated life tests data based on Kullback­Leibler information functions. This acceleration model is assumed to be a tampered random variable model. The procedure is applicable when the exponential parameter using the data from accelerated life tests is or is not specified under null hypothesis. And we compare the power of the proposed test statistics with Kolmogorov­Smirnov, Cramer von Mises and Anderson­Darling statistics in the small sample.

• Journal of the Korean Data and Information Science Society
• /
• v.5 no.2
• /
• pp.75-85
• /
• 1994

In this paper, I introduce the goodness-of-fit test statistics for exponential distribution using accelerated life test data. The ALT lifetime data were obtained by assuming step-stress ALT model, specially TRV model introduced by DeGroot and Goel(1979). The critical values are obtained for proposed test statistics, Kolmogorov-Smirnov, Kuiper, Watson, Cramer-von Mises, Anderson-Darling type, under various sample sizes and significance levels. The powers of the five test statistic are compared through Monte-Cairo simulation technique.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.6
• /
• pp.1497-1501
• /
• 2013

Cumulative residual Kullback-Leibler (CRKL) information is well defined on the empirical distribution function (EDF) and allows us to construct a EDF-based goodness of t test statistic. However, we need to consider a scaled CRKL because CRKL is not scale invariant. In this paper, we consider several criterions for estimating the scale parameter in the scale CRKL and compare the performances of the estimated CRKL in terms of both power and unbiasedness.

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

• Proceedings of the Korea Contents Association Conference
• /
• 2016.05a
• /
• pp.383-384
• /
• 2016

The Special Needs Education Assessment Tool (SNEAT) were verified of reliability and validity. However, the reliability and validity has been verified is only Okinawa Prefecture, the national data has not been analyzed. Therefore, this study aimed to verify the reliability and construct validity of SNEAT in Miyagi Prefecture as part of the national survey. SNEAT using 55 children collected from the classes on independent activities of daily living for children with disabilities in Miyagi Prefecture between November and December 2015. Survey data were collected in a longitudinal prospective cohort study. The reliability of SNEAT was verified via the internal consistency method; the coefficient of Cronbach's ${\alpha}$ were over 0.7. The validity of SNEAT was also verified via the latent growth curve model. SNEAT is valid based on its goodness-of-fit values obtained using the latent growth curve model, where the values of comparative fit index (0.997), tucker-lewis index (0.996) and root mean square error of approximation (0.025) were within the goodness-of-fit range. These results indicate that SNEAT has high reliability and construct validity.

• Journal of Forest and Environmental Science
• /
• v.39 no.4
• /
• pp.195-202
• /
• 2023

In this study, we sought to compare and evaluate the accuracy and predictive performance of machine learning algorithms for estimating the growth of individual Larix kaempferi trees in Gangwon Province, Korea. We employed linear regression, random forest, XGBoost, and LightGBM algorithms to predict tree growth using monitoring data organized based on different thinning intensities. Furthermore, we compared and evaluated the goodness-of-fit of these models using metrics such as the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The results revealed that XGBoost provided the highest goodness-of-fit, with an R² value of 0.62 across all thinning intensities, while also yielding the lowest values for MAE and RMSE, thereby indicating the best model fit. When predicting the growth volume of individual trees after 3 years using the XGBoost model, the agreement was exceptionally high, reaching approximately 97% for all stand sites in accordance with the different thinning intensities. Notably, in non-thinned plots, the predicted volumes were approximately 2.1 m³ lower than the actual volumes; however, the agreement remained highly accurate at approximately 99.5%. These findings will contribute to the development of growth prediction models for individual trees using machine learning algorithms.

• Journal of the Korean Data Analysis Society
• /
• v.20 no.6
• /
• pp.2721-2731
• /
• 2018

This study purposes to derive the predictive model for the cold tolerance of Brassica napus, using the data collected in the Tree Breeding Lab of Gyeongsang National University during July and August of 2016. Three Brassica napus samples were treated at each of low temperatures from $4^{\circ}C$ to $-12^{\circ}C$ by decrement of $4^{\circ}C$, step by step, and electrolyte leakage levels were measured at each stage. Electrolyte leakages were observed tangibly from $-4^{\circ}C$. We tried to fit the six nonlinear regression models to the electrolyte leakage data of Brassica napus: 3-parameter logistic model, baseline logistic model, 4-parameter logistic model, (4-1)-parameter logistic model, 3-parameter Gompertz model, and (3-1)-parameter Gompertz model. The baseline levels of the electrolyte leakage estimated by these models were 4.81%, 4.07%, 4.19%, 4.07%, 4.55%, and 0%, respectively. The estimated median lethal temperature, LT50, were $-5.87^{\circ}C$, $-6.31^{\circ}C$, $-6.05^{\circ}C$, $-6.35^{\circ}C$, $-4.98^{\circ}C$, and $-5.15^{\circ}C$, respectively. We compared and discussed the measures of goodness of fit to select the appropriate nonlinear regression model.