• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.9-20
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• 2019

The primary objective of this study was to analyze the delivery ratio using Hydrological Simulation Program - Fortran (HSPF) in Okdong-cheon watershed. Model parameters related to hydrology and water quality were calibrated and validated by comparing model predictions with the 8-day interval filed data collected for ten years from the Korea Ministry of Environment. The results indicated that hydrology and water quality parameters appeared to be reasonably comparable to the field data. The pollutant delivery loads of the watershed in 2015 were simulated using the HSPF model. The delivery ratios of each subwatershed were also estimated by the simple ratio calculation of pollutant discharge load and pollutant delivery load. Coefficients of the regression equation between the delivery ratio and specific discharge were also computed using the delivery ratio. Based on the results, multiple regression analysis was performed using the discharge and the physical characteristics of the subwatershed such as the area. The equation of delivery ratio derived in this study is only for the Okdong-cheon watershed, so the larger studies are needed to apply the findings to other watersheds.

• Journal of Preventive Veterinary Medicine
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• v.41 no.3
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• pp.129-132
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• 2017

The current study identified risk factors associated with porcine circovirus type 2 (PCV2) infection on pig farms in the Republic of Korea using a multinomial logistic regression model to evaluate the PCV2 infection status of pigs at different growth stages. Compulsory disinfection of visitors (odds ratio [OR]: 0.019, 95% confidence interval [CI]: <0.001-0.378, p=0.0095), compulsory registration of visitors (OR: 0.002, 95% CI: <0.001-0.184, p=0.0070), regular blood testing (OR: 0.012, 95% CI: <0.001-0.157, p=0.0007), and running on-farm biosecurity learning programs for workers (OR: 0.156, 95% CI: 0.040-0.604, p=0.0072 and OR: 0.201, 95% CI: 0.055-0.737, p=0.0155, respectively) were identified as factors which could reduce the risk of PCV2 infection. However, visitation by a regular veterinarian (OR: 32.733, 95% CI: 3.768-284.327, p=0.0016) was associated with PCV2 infection.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.539-553
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• 2017

We propose a multi-state model to analyze semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the three states of the illness-death model: healthy, disease, and dead. The 'diseased' state can be considered as the intermediate event. Two more states are added into the illness-death model to incorporate the missing events, which are caused by a loss of follow-up before the end of a study. One of them is a state of the lost-to-follow-up (LTF), and the other is an unobservable state that represents an intermediate event experienced after the occurrence of LTF. Given covariates, we employ the Lin and Ying additive hazards model with log-normal frailty and construct a conditional likelihood to estimate transition intensities between states in the multi-state model. A marginalization of the full likelihood is completed using adaptive importance sampling, and the optimal solution of the regression parameters is achieved through an iterative quasi-Newton algorithm. Simulation studies are performed to investigate the finite-sample performance of the proposed estimation method in terms of empirical coverage probability of true regression parameters. Our proposed method is also illustrated with a dataset adapted from Helmer et al. (2001).

• Journal of the Korean Solar Energy Society
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• v.37 no.1
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• pp.81-90
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• 2017

We have analyzed the performance of 58 kWp photovoltaic (PV) power systems installed in Jeddah, Saudi Arabia. Performance ratio (PR) of 3 PV systems with 3 desert-type PV modules using monitoring data for 1 year showed 85.5% on average. Annual degradation rate of 5 individual modules achieved 0.26%, the regression model using monitoring data for the specified interval of one year showed 0.22%. Root mean square error (RMSE) of 6 big data analysis models for power output prediction in May 2016 was analyzed 2.94% using a support vector regression model.

• The Korean Journal of Applied Statistics
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• v.37 no.3
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• pp.339-353
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• 2024

In cases where the response variable in proportional data is confined to a limited interval, a regression model based on the assumption of normality can yield inaccurate results due to issues such as asymmetry and heteroscedasticity. In such cases, the beta regression model can be considered as an alternative. This model reparametrizes the beta distribution in terms of mean and precision parameters, assuming that the response variable follows a beta distribution. This allows for easy consideration of heteroscedasticity in the data. In this paper, we therefore aim to analyze proportional data using the beta regression model in two empirical analyses. Specifically, we investigate the relationship between smoking rates and coffee consumption using data from the 6th National Health Survey, and examine the association between regional characteristics in the U.S. and cumulative mortality rates based on COVID-19 data. In each analysis, we apply the ordinary least squares regression model, the beta regression model, and the extended beta regression model to analyze the data and interpret the results with the selected optimal model. The results demonstrate the appropriateness of applying the beta regression model and its extended version in proportional data.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.243-248
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• 2004

The timings of two successive events of interest may not be measurable, instead it may be right censored or interval censored; this data structure is called doubly censored data. In the study of HIV, two such events are the infection with HIV and the onset of AIDS. These data have been analyzed by authors under the assumption that infection time and induction time are independent. This paper investigates the regression problem when two events arc modeled to allow the presence of a possible relation between two events as well as a subject-specific effect. We derive the estimation procedure based on Goetghebeur and Ryan's (2000) piecewise exponential model and Gauss-Hermite integration is applied in the EM algorithm. Simulation studies are performed to investigate the small-sample properties and the method is applied to a set of doubly censored data from an AIDS cohort study.

• Steel and Composite Structures
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• v.18 no.3
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• pp.757-773
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• 2015

This work attempts to implement multiple regression analysis (MRA) for modeling and predicting the shear buckling strength of a steel beam with corrugated web. It was recognized from theoretical and experimental results that the shear buckling strength of a steel beam with corrugated web is complicated and affected by several parameters. A model that predicts the shear strength of a steel beam with corrugated web with reasonable accuracy was sought. To that end, a total of 93 experimental data points were collected from different sources. Then mathematical models for the key response parameter (shear buckling strength of a steel beam with corrugated web) were established via MRA in terms of different input geometric, loading and materials parameters. Results indicate that, with a minimal processing of data, MRA could accurately predict the shear buckling strength of a steel beam with corrugated web within a 95% confidence interval, having an $R^2$ value of 0.93 and passing the F- and t-tests.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.269-279
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• 2015

This paper considers inferences of random effects. We show that the proposed confidence distribution (CD) performs well in logistic regression for random intercepts with small samples. Real data analyses are also done to identify the subject effects clearly.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.141-152
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• 2021

A new statistical model needs additional variables in order to re-evaluate the undecided inference. Then the MNAR assumption is required, since the probabilities for the positivity of the indeterminant and the determinant is calculated differently. In this study, since two statistical models have a hierarchical relationship, we determine the undecided inference under the MNAR assumption using the confidence interval of the difference between two AUCs. Among many methods of estimating the confidence interval of the AUC difference, it is found that four kinds of methods show excellent performance through simulations. And based on these methods, we propose a variable selection method that are useful for the undecided inference using logistic regression models.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.444-447
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• 2008

This study is intended to develop a improved version of Cross Impact Analysis Model based on Scaling Theory. In developing the model, we applied the scale transformation technique and regression technique to existing CIA model. Improved CIA model is composed of two sub-models: 'model for impact value measurement,' and 'model for impact value conversion'. We applied a technique which measures data by ordinal scale and then transforms them into interval scale and ratio scale data to CIA model. The accuracy of forecasting and the usability of CIA application have been improved.