• Journal of the Korean Institute of Navigation
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• v.24 no.5
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• pp.385-395
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• 2000

In the transportation literature, many useful decision making models for ship routing and ship scheduling have been studied. But the majority of these studies are on industrial carriers, bulk carriers, or tankers. It is quite recent that a few optimization models have been developed for liner fleet routing and scheduling problems. However there have been few academic studies on decision making models for the routing or scheduling problems of passenger ships in spite of their economic importance in the entire shipping industry. The purpose of this study is to develop analytic decision making models for ship routing and scheduling for the passenger ship fleet. This study gives two optimization models, one is a linear programming model and the other a goal programming model. These two models are solved easy by commercial linear programming softwares and suggest optimal ship routing plans and many other useful implications for passenger ship fleet managers.

• Transactions of Materials Processing
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• v.11 no.5
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• pp.394-404
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• 2002

The drawbead model for a three-dimensional a finite element analysis of sheet metal forming processes is developed. The mathematical models of the basic drawbeads like circular drawbead, stepped drawbead, and squared drawbaed are first derived using the bending theory, belt-pulley equation, and Coulomb friction law. Next, the experiments for finding the drawing characteristics of the drawbead are performed. Based on mathematical models and drawing test results, expert models of basic drawbeads are then developed employing a linear multiple regression method. For the expert models of combined drawbeads such as the double circular drawbead, double stepped drawbead, circular-and-stepped drawbead, etc., those of the basic drawbeads are summed. Finally, in order to verify the expert models developed, the drawing characteristics calculated by the expert models of the double circular drawbead and circular-and-stepped drawbead are compared with those obtained from the experiments. The predictions by expert models agree well with the measurements by experiments.

• International Journal of Highway Engineering
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• v.14 no.5
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• pp.189-199
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• 2012

PURPOSES: The purpose of this study is to propose a new methodology for developing statistical collision models and to show the validation results of the methodology. METHODS: A new modeling method of introducing variables into the model one by one in a multiplicative form is suggested. A method for choosing explanatory variables to be introduced into the model is explained. A method for determining functional forms for each explanatory variable is introduced as well as a parameter estimating procedure. A model selection method is also dealt with. Finally, the validation results is provided to demonstrate the efficacy of the final models developed using the method suggested in this study. RESULTS: According to the results of the validation for the total and injury collisions, the predictive powers of the models developed using the method suggested in this study were better than those of generalized linear models for the same data. CONCLUSIONS: Using the methodology suggested in this study, we could develop better statistical collision models having better predictive powers. This was because the methodology enabled us to find the relationships between dependant variable and each explanatory variable individually and to find the functional forms for the relationships which can be more likely non-linear.

• Journal of the Korea Society of Computer and Information
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• v.26 no.8
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• pp.157-164
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• 2021

Regression analysis is a well-known statistical technique useful to explain the relationship between response variable and predictor variables. In particular, Researchers are interested in comparing the regression coefficients(intercepts and slopes) of the models in two independent populations. The Chow test, proposed by Gregory Chow, is one of the most commonly used methods for comparing regression models and for testing the presence of a structural break in linear models. In this study, we propose the use of permutation method and compare it with Chow test analysis for testing the equality of two independent linear regression models. Then simulation study is conducted to examine the powers of permutation test and Chow test.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1215-1224
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• 2016

Climate information of the high resolution grid units is an important factor to explain the phenomenon in a variety of research field. Statistical linear interpolation models are computationally inexpensive and applicable to any climate data compared to the dynamic simulation method at regional scales. In this paper, we considered four different linear-based statistical interpolation models: general linear model, generalized additive model, spatial linear regression model, and Bayesian spatial linear regression model. The climate variable of interest was the daily mean temperature, where the spatial variability was explained using geographic terrain information: latitude, longitude, elevation. The data were collected by weather stations in January from 2003 and 2012. In the sense of RMSE and correlation coefficient, Bayesian spatial linear regression model showed better performance in reflecting the spatial pattern compared to the other models.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.209-216
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• 2003

Contingent valuation method (CVM) is a main evaluation method of nonmarket goods for which markets either do not exist at all or do exist only incompletely; an example is environmental good. A dichotomous choice approach, the most popular type of CVM in environmental economics, employs binary discrete choice models as statistical estimation models. In this paper, we propose a semiparametric dichotomous choice CVM method using local linear model of Fan and Gijbels (1996) in which probability distribution of error term is specified parametrically but latent structural function is specified nonparametrically. The computation procedures of the proposed method are illustrated with a simple design of simulations.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.75-82
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• 1994

It is known that collinearity among the explanatory variables in generalized linear models inflates the variance of maximum likelihood estimators. A ridge-type estimator is presented using penalized likelihood. A method for choosing a shrinkage parameter is discussed and this method is based on a prediction-oriented criterion, which is Mallow's $C_L$ statistic in a linear regression setting.

• Journal of applied mathematics & informatics
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• v.14 no.1_2
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• pp.377-386
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• 2004

A technique that provides prediction intervals based on a model called an empirical linear model is discussed. The technique, high-dimensional empirical linear prediction (HELP), involves principal component analysis, factor analysis and model selection. HELP can be viewed as a technique that provides prediction (and confidence) intervals based on a factor analysis models do not typically have justifiable theory due to nonidentifiability, we show that the intervals are justifiable asymptotically.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.465-474
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• 2015

In this paper, we study the local influence analysis of LIU type estimator in the linear mixed models. Using the method proposed by Shi (1997), the local influence of LIU type estimator in three disturbance models are investigated respectively. Furthermore, we give the generalized Cook's distance to assess the influence, and illustrate the efficiency of the proposed method by example.

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.13-18
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• 2005

We explore the structure and usefulness of the 3-D residual plot as a basic tool for dealing with interaction in generalized linear models. If predictors have an interaction effect, the shape obtained by rotating the 3-D residual plot will show its presence. To illustrate the use of this plot as an aid to exploring the interaction, we present an example of a binomial regression model using simulated data.