• Journal of Korean Society of Forest Science
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• v.95 no.5
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• pp.601-604
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• 2006

This study compared the accuracy of partial multivariate and vector autoregressive models for lumber demand prediction in Korea. The partial multivariate model has three explanatory variables; own price, construction permit area and dummy. The dummy variable reflected the boom of lumber demand in 1988, and the abrupt decrease in 1998. The VAR model consists of two endogenous variables, lumber demand and construction permit area with one lag. On the other hand, the prediction accuracy was estimated by Root Mean Squared Error. The results showed that the estimation by partial multivariate and vector autoregressive model showed similar explanatory power, and the prediction accuracy was similar in the case of using partial multivariate and vector autoregressive model.

• Health Policy and Management
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• v.12 no.3
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• pp.54-76
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• 2002

This study investigated how much EVA which evaluate firm's value can explain hospital bankruptcy prediction as a explanatory variable including financial indicators in Korea. In this study, artificial neural network and logit regression which are traditional statistical were used as the model for bankruptcy prediction. Data used in this study were financial and economic value added indicators of 34 bankrupt and -:4 non-bankrupt hospitals from the Database of Korean Health Industry Development Institute. The main results of this study were as follows: First, there was a significant difference between the financial variable model including EVA and the financial variable model excluding EVA in pre-bankruptcy analysis. Second, EVA could forecast bankruptcy hospitals up to 83% by the logistic analysis. Third, the EVA model outperformed the financial model in terms of the predictive power of hospital bankruptcy. Fourth, The predictive power of neural network model of hospital bankruptcy was more powerful than the legit model. After all the result of this study will be useful to future study on EVA to evaluate bankruptcy hospitals forecast.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1341-1364
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• 2023

This article introduces a method for estimating the conditional hazard function of a real-valued response variable based on a functional variable. The method uses local linear estimation of the conditional density and cumulative distribution function and is applied to a functional stationary ergodic process where the explanatory variable is in a semi-metric space and the response is a scalar value. We also examine the uniform almost complete convergence of this estimation technique.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.1-17
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• 2008

The ordinary least squares regression method of Haseman and Elston(1972) is most widely used in genetic linkage studies for continuous traits of sib pairs. Kruglyak and Lander(1995) suggested a statistic which appears to be a nonparametric counterpart to the Haseman and Elston(1972)'s regression method, but in fact these two methods are quite different. In this paper the relationships between these two methods are described and will be compared by simulation studies. One of the characteristics of the sib-pair linkage study is that the explanatory variable has only three different values and thus dependent variable is heavily replicated in each value of the explanatory variable. We propose a weighted least squares regression method which is more appropriate to this situation and the efficiency of the weighted regression in genetic linkage study was explored with normal and non-normal simulated continuous traits data. Simulation studies demonstrated that the weighted regression is more powerful than other tests.

• The Journal of Industrial Distribution & Business
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• v.10 no.1
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• pp.69-76
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• 2019

Purpose - The purpose of this paper is to empirically investigate the effectiveness of Offcial Development Assistance (ODA) in recipient countries' economy. ODA is designed to mitigate poverty and stimulate economic growth in the developing countries. We classify total ODA into bilateral ODA and multilateral ODA depending on the number of donor countries. If the ODA flows from one donor country to one recipient country, it is classified as bilateral ODA. If the multiple countries simultaneously become donor countries through the international organizations such as United Nations and World Bank, it is classified as multilateral ODA. This paper compares the effect of bilateral ODA and multilateral ODA in determining recipient countries' economic development, and tries to provide policy implications to Korean ODA. Research design, data, and methodology - Our primary explanatory variables are bilateral and multilateral ODA. Private credit in recipient countries is adopted as additional explanatory variables to capture the level of financial development in recipient countries. We measure the ODA effectiveness using economic growth and quality of life of the recipient countries as the dependent variable. We collect 142 recipient countries' data from OECD statistics, during the period from 1970-2014. Panel least squares estimation with country fixed effect is employed as the empirical model. Results - Our results support that ODA variable has a negatively significant impact on recipient countries' economic growth, while it is positively correlated with human development index. Recipient countries' private credit is positively correlated with economic growth and human development index. The interaction variable of ODA and financial development turns out to be significant in general. We find that the positive effect of ODA depends on recipient countries' financial market development and this effect is stronger in multilateral aid than bilateral one. Conclusions - From the analysis, we have confirmed that the recipient countries financial development is the necessity condition to achieve positive effect of ODA. Based on these results, we suggest that Korean government should increase the share of multilateral funding and pay attention to recipient countries' financial market development to maximize the effectiveness of ODA.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.87-100
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• 2003

Most of statistical data are generated by a set of dynamic, stochastic, and simultaneous relations. An important question is how to specify statistical models so that they are consistent with the dynamic feature of those data. A general hypothesis is that the lagged effect of a change in an explanatory variable is not felt all at once at a single point in time, but The impact is distributed over a number of future points in time. In other words, current control variables are determined by a function that can be reduced to a distributed lag function of past observations. It is possible to explain the relationship between variables in different points of time and to estimate the long-run impacts of a change in a variable on another if time lag series of explanatory variables are incorporated in the model specification. In this study, distributed lag structure is applied to the domestic stock market model to capture the dynamic response of the market by exogenous shocks. The Domestic market is found more responsive to the changes in foreign market factors both in the short and the long run.

• Journal of dental hygiene science
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• v.23 no.4
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• pp.312-319
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• 2023

Background: Parental attention is crucial for preventing childhood oral diseases. Mothers play a significant role in maintaining their families' oral health, and their educational level influences their children's oral health behaviors. This study investigates the impact of mothers' educational levels on adult oral health behaviors using data from a national survey. Methods: This study employed a cross-sectional analysis of secondary data. The data used were obtained from the 8th Korea National Health and Nutrition Examination Survey. Descriptive statistics were calculated to identify participant characteristics. Next, t-tests and one-way analysis of variance were conducted to examine the effects of the explanatory variables on the distribution of the dependent variable. Finally, logistic regression analysis was used to investigate the influence of the explanatory variable on the dependent variable, using "no education" as the reference value, and calculate the odds ratios. Results: Children of mothers with a college education or higher had a 1.13 times higher likelihood of receiving oral examinations than those whose mothers had no education. Children whose mothers graduated from college or higher had a 2.23 times higher probability of receiving preventative dental treatment than those whose mothers had no education. Children whose mothers graduated from college or higher had a 1.92 times higher probability of receiving scaling than those whose mothers had no education. Children whose mothers graduated from high school had a 1.35 times higher probability of receiving scaling than those whose mothers had no education. Conclusion: Developing oral health programs is important for low-educated and low-income parents to change theirs and their children's oral health behaviors/attitudes. This will help reduce oral health disparities among adults raised by parents of higher and lower socioeconomic statuses. Therefore, a comprehensive approach is essential for adults to maintain good oral health, regardless of variations in their parental educational levels during childhood.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.301-312
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• 2008

Mutual information is a measure of association of explanatory variable for predicting target variable. It is used for variable ranking and variable subset selection. This study is about the Sample-spacing approach which can be used for the estimation of mutual information from data consisting of continuous explanation variables and categorical target variable without estimating a joint probability density function. The results of Monte-Carlo simulation and experiments with real-world data show that m = 1 is preferable in using Sample-spacing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.6 no.2
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• pp.45-55
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• 1986

This study proposes the explanatory indices of urban traffic for the purpose of solving the ambiguity of selection of the explanatory variables, which always raises problems in case of the travel-demand forecasting in the urban transportation planning, and develops optimal urban traffic generation models. The multiple regression models for objective traffic generation are developed by using the proposed explanatory inidces. Objective variables that can be explained by one explanatory variable are modified into simple regression type (Y=bX) in order to ensure the nonnegativity of traffic generation. Similarities are noted in the generaton characteristics of generated traffic from homogeneous land-use activity. Objective variables that can not be explained by multiple variable, such as trip attraction of school and trip generation of social-recreation, are classified by the characteristics of each zone. And traffic generation forecasting models are built as homogeneous zone group, the validity of each model being tested by a statistical method. It is desired that the forecasting precision is in improved by easy and simple method. Accordingly, trip generation rates are calculated from each land-use activity, and trip generation rates for practical application are proposed by considering their stability.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.57 no.3
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• pp.236-245
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• 2021

In this study, a drifting test using a experimental vessel (2,966 tons) in the northern waters of Jeju was carried out for the first time in order to obtain the fundamental data for drift. During the test, it was shown that the average leeway speed and direction by GPS position were 0.362 m/s and 155.54° respectively and the leeway rate for wind speed was 8.80%. The analysis of linear regression modes about leeway speed and direction of the experimental vessel indicated that wind or current (i.e. explanatory variable) had a greater influence upon response variable (e.g. leeway speed or direction) with the speed of the wind and current rather than their directions. On the other hand, the result of multiple regression model analysis was able to predict that the direction was negative, and it was demonstrated that predicted values of leeway speed and direction using an experimental vessel is to be more influential by current than wind while the leeway speed through variance and covariance was positive. In terms of the leeway direction of the experimental vessel, the same result of the leeway speed appeared except for a possibility of the existence of multi-collinearity. Then, it can be interpreted that the explanatory variables were less descriptive in the predicted values of the leeway direction. As a result, the prediction of leeway speed and direction can be demonstrated as following equations. Ŷ₁= 0.4031-0.0032X₁+0.0631X₂-0.0010X₃+0.4110X₄ Ŷ₂= 0.4031-0.6662X₁+27.1955X₂-0.6787X₃-420.4833X₄ However, many drift tests using actual vessels and various drifting objects will provide reasonable estimations, so that they can help search and rescue fishing gears as well.