• Communications for Statistical Applications and Methods
• /
• v.31 no.3
• /
• pp.263-277
• /
• 2024

This paper proposes new parameterizations of the tilted beta binomial distribution, obtained from the combination of the binomial distribution and the tilted beta distribution, where the beta component of the mixture is parameterized as a function of their mean and variance. These new parameterized distributions include as particular cases the beta rectangular binomial and the beta binomial distributions. After that, we propose new linear regression models to deal with overdispersed binomial datasets. These new models are defined from the proposed new parameterization of the tilted beta binomial distribution, and assume regression structures for the mean and variance parameters. These new linear regression models are fitted by applying Bayesian methods and using the OpenBUGS software. The proposed regression models are fitted to a school absenteeism dataset and to the seeds germination rate according to the type seed and root.

• Journal of the Korea Institute of Building Construction
• /
• v.14 no.6
• /
• pp.545-550
• /
• 2014

The re-locatable military facilities are significant in combat zones with limited infrastructures. Military tents are particularly one of the most essential components in the battlefields, During the offensive operation. This study assesses educational effectiveness of the 4D Building Information Modeling (BIM), which is used to deliver educational information with considering re-locatable military facility construction. Furthermore, the real time for constructing temporary concentration camp was measured, By using analysis of variance associated with the educational effects of the 4D BIM. Statistically, understandability of facility construction using 4D BIM was found to be higher than the conventional educational materials with field manual.

• Journal of Korean Academy of Nursing
• /
• v.45 no.1
• /
• pp.64-75
• /
• 2015

Purpose: This study was designed to construct and test structural equation modeling on healthy menopausal transition in middle-aged women in order to identify variables affecting healthy menopausal transition. Methods: Participants, 276 women, 45 to 60 years of age, with menopausal symptom score higher than 5 on the Korean version of Menopause Rating Scale, were recruited in three cities and one county of Gyeongnam Province. Research data were collected via questionnaires and analysed using SPSS version 18.0 and AMOS version 20.0. Results: After confirmatory factor analysis, one of the observed variables was excluded due to relatively low factor loading. The model fit indices for the hypothetical model were suitable for the recommended level: GFI=.93, CFI=.92, RMSEA=.05. Self-efficacy, self-differentiation, and menopausal symptoms explained 67.7% of variance in menopausal transition, and self-differentiation was the most influential factor for menopausal transition. Self efficacy and menopausal symptoms explained 9.6% of variance in menopausal management, although "menopausal symptoms" was not significant. Conclusion: These results suggest that nursing interventions to improve self-differentiation, self efficacy, menopausal management and decrease menopausal symptoms are critical for healthy menopausal transition in middle-aged women. Continued development of a variety of community-based nursing interventions to facilitate healthy menopausal transition is suggested.

• International Commerce and Information Review
• /
• v.12 no.1
• /
• pp.99-121
• /
• 2010

This paper reviews the categories and properties of risk measures, analyzes the classes and structural equations of volatility forecasting models, and presents the practical methodologies and their expansion methods of estimating and forecasting the volatilities of exchange rates using Excel spreadsheet modeling. We apply the GARCH(1,1) model to the Korean won(KRW) denominated daily and monthly exchange rates of USD, JPY, EUR, GBP, CAD and CNY during the periods from January 4, 1998 to December 31, 2009, make the estimates of long-run variances in the returns of exchange rate calculated as the step-by-step change rate, and test the adequacy of estimated GARCH(1,1) model using the Box-Pierce-Ljung statistics Q and chi-square test-statistics. We demonstrate the adequacy of GARCH(1,1) model in estimating and forecasting the volatility of exchange rates in the monthly series except the semi-variance GARCH(1,1) applied to KRW/JPY100 rate. But we reject the adequacy of GARCH(1,1) model in estimating and forecasting the volatility of exchange rates in the daily series because of the very high Box-Pierce-Ljung statistics in the respective time lags resulting to the self-autocorrelation. In conclusion, the GARCH(1,1) model provides for the easy and helpful tools to forecast the exchange rate volatilities and may become the powerful methodology to overcome the application difficulties with the spreadsheet modeling.

• Journal of the Ergonomics Society of Korea
• /
• v.25 no.2
• /
• pp.161-172
• /
• 2006

This study aims to compare and contrast the Kansei modeling methods for building a luxuriousness model that people feel about appearance of mobile phones. For the evaluation based on Kansei engineering approaches, 15 participants were employed to evaluate 18 mobile phones using a questionnaire. The results of evaluation were analyzed to build luxuriousness models through quantification I method, neural network, and decision tree method, respectively. The performance of Kansei modeling methods was compared and contrasted in terms of accuracy and predictability. The result of comparison of modeling methods indicated that model accuracy and predictability was closely related to the number of variables and data size. It was also revealed that quantification I method was the best in terms of model accuracy while decision tree method was the best modeling method with small variance in terms of predictability. However, it was empirically found that quantification I method showed extremely unstable predictability with small number of data. Consequently, it is expected that the research findings of this study might be utilized as a guideline for selecting proper Kansei modeling method.

• The Korean Journal of Applied Statistics
• /
• v.23 no.6
• /
• pp.1125-1145
• /
• 2010

For modeling(skewed) semicircular data, we derive a new exponential family of distributions. We extend it to the l-axial exponential family of distributions by a projection for modeling any arc of arbitrary length. It is straightforward to generate samples from the l-axial exponential family of distributions. Asymptotic result reveals that the linear exponential family of distributions can be used to approximate the l-axial exponential family of distributions. Some trigonometric moments are also derived in closed forms. The maximum likelihood estimation is adopted to estimate model parameters. Some hypotheses tests and confidence intervals are also developed. The Kolmogorov-Smirnov test is adopted for a goodness of t test of the l-axial exponential family of distributions. Samples of orientations are used to demonstrate the proposed model.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2006.11a
• /
• pp.373-376
• /
• 2006

In general, there are three kinds of methods in analyzing dynamic robust design experiment: loss model approach, response function approach, and response model approach. In this talk, we review the three modeling approaches in terms of several criteria in comparison. This talk also generalizes the response model approach based on a generalized linear model. We develop a generalized two-step optimization procedure to substantially reduce the process variance by dampening the effect of both explicit and hidden noise variables. The proposed method provides more reliable results through iterative modeling of the residuals from the fitted response model. The method is compared with three existing approaches in practical examples.

• Journal of Environmental Health Sciences
• /
• v.24 no.1
• /
• pp.80-86
• /
• 1998

Seom River was major branch of Namhan river, consist of primary basin that Wonjoo-city, Hoingsung-gun and primary contamination source was sewage from human lives. This study was evaluated production contamination loading of each branch basin and water quality grade and water quality simulation by QUAL2E to provide efficient contaminations source control. Rusult of survey, production loading of BOD, T-N, T-P were 26,591 kg/day, 4,560 kg/day, 731 kg/day resectively. Water quality analysis in 17 points of main stream were appeared that 1st grade(BOD 1 mg/l under) was 6 point, 2nd grade was 9 point and 3rd grade was 2 point. And result of water quality analysis for branch steram, first grade was evaluated 68.7%. Based of field data, calibration and verification result were in good agreement with mesured value within coefficient of variance were from 2.59% to 18.73%, from 6.39%, to 28.46%, respectively.

• Transactions on Electrical and Electronic Materials
• /
• v.7 no.6
• /
• pp.297-300
• /
• 2006

The response surface modeling of the pretilt angle control using ion-beam (IB) alignment on nitrogen doped diamond-like carbon (NDLC) thin film layer is investigated. This modeling is used to analyze the variation of the pretilt angle under various process conditions. IB exposure angle and IB exposure time are considered as input factors. The analysis of variance technique is settled to analyze the statistical significance, and effect plots are also investigated to examine the relationships between the process parameters and the response. The model can allow us to reliably predict the pretilt angle with respect to the varying process conditions.

• International Journal of Highway Engineering
• /
• v.19 no.4
• /
• pp.1-7
• /
• 2017

PURPOSES : This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors. METHODS: Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling. RESULTS : The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables. CONCLUSIONS : Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.