• Journal of the Korean Society of Child Welfare
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• no.32
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• pp.7-32
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• 2010

The purpose of this research is to identify the longitudinal reciprocal relationship between family social capital and self-esteem of adolescents. Five waves of panel data from the Korea Youth Panel Survey were employed for this research. Korean Adolescents Policy Research Institute collected the first wave data in 2004 from elementary school children in 4th grade. Autoregressive cross-lagged modeling was performed to analysis the longitudinal reciprocal relationship between family social capital and self-esteem. The major findings were as follows. First, stability coefficient of family social capital and self-esteem showed that both variables were significantly stable over time. Secondly family social capital(t) had statistically significant effect on self-esteem(t+1), whereas self-esteem did not predict family social capital at a statistically significant level. These findings suggest that family social capital and self-esteem is stable overtime, and that obtaining family social capital and establishing positive self-esteem is important during this period in childhood. In addition, the results show that family social capital affects self-esteem, which highlights the importance of family social capital accumulation on the development of adolescent self-esteem.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.421-430
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• 2006

In sample survey sample designs are performed by geographically-based domain such as countries, states and metropolitan areas. However mostly statistics of interests are smaller domain than sample designed domain. Then sample sizes are typically small or even zero within the domain of interest. Shin and Lee(2003) mentioned Spatial Autoregressive(SAR) model in small area estimation model-based method and show the effectiveness by MSE. In this study, Bayesian Auto-Poisson Model is applied in model-based small area estimation method and compare the results with SAR model using MSE ME and bias check diagnosis using regression line. In this paper Survey of Disability, Aging and Cares(SDAC) data are used for simulation studies.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.99-109
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• 2019

We compared the confidence intervals of estimators using various bootstrap methods for a Random Coefficient Autoregressive(RCA) model. We consider a Quasi score estimator and M-Quasi score estimator using Huber, Tukey, Andrew and Hempel functions as bounded functions, that do not have required assumption of distribution. A standard bootstrap method, percentile bootstrap method, studentized bootstrap method and hybrid bootstrap method were proposed for the estimations, respectively. In a simulation study, we compared the asymptotic confidence intervals of the Quasi score and M-Quasi score estimator with the bootstrap confidence intervals using the four bootstrap methods when the underlying distribution of the error term of the RCA model follows the normal distribution, the contaminated normal distribution and the double exponential distribution, respectively.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.303-313
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• 2018

This study considers time series models to forecast drug expenditures in national health insurance. We adopt autoregressive error model (ARE) and transfer function model (TFM) with segmented level and trends (before and after 2012) in order to reflect drug price reduction in 2012. The ARE has only a segmented deterministic term to increase the forecasting performance, while the TFM explains a causality mechanism of drug expenditure with closely related exogenous variables. The mechanism is developed by cross-correlations of drug expenditures and exogenous variables. In both models, the level change appears significant and the number of drug users and ratio of elderly patients variables are significant in the TFM. The ARE tends to produce relatively low forecasts that have been influenced by a drug price reduction; however, the TFM does relatively high forecasts that have appropriately reflected the effects of exogenous variables. The ARIMA model without the exogenous variables produce the highest forecasts.

• Journal of Korean Society of Transportation
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• v.25 no.1 s.94
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• pp.121-132
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• 2007

Up to now Permanent traffic volumes have been counted by Automatic Vehicle Classification (AVC) on National Highways. When counted data have missing items or errors, the data must be revised to stay statistically reliable This study was carried out to estimate correct data based on outoregression and seasonal AutoRegressive Integrated Moving Average (ARIMA). As a result of verification through seasonal ARIMA, the longer the missed period is, the greater the error. Autoregression results in better verification results than seasonal ARIMA. Traffic data is affected by the present state mote than past patterns. However. autoregression can be applied only to the cases where data include similar neighborhood patterns and even in this case. the data cannot be corrected when data are missing due to low qualify or errors Therefore, these data shoo)d be corrected using past patterns and seasonal ARIMA when the missing data occurs in short periods.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.29 no.4
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• pp.180-188
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• 2017

Recently, as the strength of winds and waves increases due to the climate change, abnormal waves such as swells have been also increased, which results in the increase of downtime events of loading/unloading in a harbour. To reduce the downtime events, breakwaters were constructed in a harbour to improve the tranquility. However, it is also important and useful for efficient port operation by predicting accurately and also quickly the downtime events when the harbour operation is in a limiting condition. In this study, numerical simulations were carried out to calculate the wave conditions based on the forecasted wind data in offshore area/outside harbour and also the long-term observation was carried out to obtain the wave data in a harbour. A forecasting method was designed using an auto-regressive (AR) and artificial neural networks (ANN) models in order to establish the relationship between the wave conditions calculated by wave model (SWAN) in offshore area and observed ones in a harbour. To evaluate the applicability of the proposed method, this method was applied to predict wave heights in a harbour and to forecast the downtime events in Pohang New Harbour with highly complex topography were compared. From the verification study, it was observed that the ANN model was more accurate than the AR model.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.807-817
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• 2014

This paper considers multivariate time series modelling of PM10 data in Korea collected from 2008 to 2011. We consider both temporal and spatial dependencies of PM10 by applying the sparse vector autoregressive (sVAR) modelling proposed by Davis et al. (2013). It utilizes the partial spectral coherence to measure cross correlation between different regions, in turn provides the sparsity in the model while balancing the parsimony of model and the goodness of fit. It is also shown that sVAR performs better than usual vector autoregressive model (VAR) in forecasting.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1117-1124
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• 2010

The PM10 (Promethium 10) data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model has been considered for analyzing the monthly PM10 data at the southern part of the Gyeonggi-Do, Suwon monitoring site in Korea. In the ARE model, six meteorological variables and four pollution variables are used as the explanatory variables for the PM10 data set. The six meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, radiation, and amount of cloud. The four air pollution explanatory variables are sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), carbon monoxide (CO), and ozone ($O_3$). The result showed that the monthly ARE models explained about 13-49% for describing the PM10 concentration.

• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.535-542
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• 2012

The consumer price index (CPI) data is one of the important economic measurement of the country. In this article, the Autoregressive Error (ARE) model has been considered for analyzing the monthly CPI data at Seoul, Pusan, Daegu, and Gwangju Cities in Korea, In the ARE model, nine economic variables are used as the explanatory variables for the CPI data set. The nine explanatory variables are CCI (coincident composite index), won-dollar rate, producer price index, oil import price, oil import volume, international current account, import price index, unemployment rate, and amount of currency. The result showed that the monthly ARE models explained about 46-52% for describing the CPI.

• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.475-486
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• 2020

This study aims to discover the mediating effect of Self-Encouragement and Self-Discouragement on the Relationships between Fear of Negative Evaluation and Social Interaction Anxiety. A questionnaire was conducted on 319 university students and the data was analyzed with Pearson's correlation and regression. As a result, there was a positive correlation between Fear of Negative Evaluation and Social Interaction Anxiety. Also, Fear of Negative Evaluation and Social Interaction Anxiety were positively correlated with Self-Discouragement and negatively correlated with Self-Encouragement. Next, multiple regression showed that Self-Discouragement has a perfect mediating effect and Self-Encouragement has a partial mediating effect on the Relationship between Fear of Negative Evaluation and Social Interaction Anxiety. These results imply that counselee with Social Interaction Anxiety can be treated effectively by promoting self-encouragement.