• Journal of Korean Society of Transportation
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• v.29 no.4
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• pp.95-101
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• 2011

This study proposes the accident estimation model developed based on the time-series cross-sectional data at 50 intersections in Cheongju. The data were collected repeatedly and accumulated from 2004 to 2007. This study focused on deriving the optimal among the various models including TSCSREG(Time Series Cross Section Regression). Four different models utilizing various elements affecting accidents were developed. Through a statistical test, it was found that the t values of independent variables of the fixed effect models were less than those of the random effect models. Two variables were then found to be positive to the accidents: the number of crosswalks at an intersection and the number of intersections.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1163-1170
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• 2014

The concepts and structure of intermittent panel time series data are introduced. We suggest a Wald test statistic for the test of homogeneity for intermittent panel first order autoregressive model and its limit distribution is derived. We consider the fitting the model with pooling data using sample mean at the time point if homogeneity for intermittent panel AR(1) is satisfied. We performed simulations to examine the limit distribution of the homogeneity test statistic for intermittent panel AR(1). In application, we fit the intermittent panel AR(1) for panel Mumps data and investigate the test of homogeneity.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.123-132
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• 2016

We can achieve the principle of parsimony and efficiency if homogeneity for panel time series model is satisfied. We suggest a Rao test statistic and a Wald test statistic for the test of homogeneity for panel AR(1) and derived the limit distribution. We performed a simulation to examine statistics with the same chisquare distribution when number of the individual is small and in common with large. We also simulated to compare the empirical power of the statistics in a small panel. In application, we fit panel AR(1) model using regional monthly economical active population data and test homogeneity for panel AR(1). It is satisfied homogeneity, so it could be fitted AR(1) using the sample mean at the time point. We also compare the power of prediction between each individual and pooled model.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.01a
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• pp.263-264
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• 2012

본 논문은 여러 개의 독립적인 시계열로 구성된 시계열 패널 자료를 이용하여 비선형 모형인 GRCA모형과 신경망을 이용하여 예측값을 구하여 서로 비교 분석하고자 한다. 먼저 GRCA모형에 대하여 연구하고 신경망의 구조와 예측값을 구하기 위한 여러 가지 변환함수를 유도한다. 단기 예측에서는 신경망 방법의 예측값이 더 좋았고, 장기예측에서는 비선형모형을 이용한 예측값이 더 좋은 것으로 나타났다.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.521-534
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• 2006

This paper considers a panel regression model with ill-posed data and proposes the generalized maximum entropy(GME) estimator of the unknown parameters. These are natural extensions from the biometries, statistics and econometrics literature. The performance of this estimator is investigated by using of Monte Carlo experiments. The results indicate that the GME method performs the best in estimating the unknown parameters.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1409-1419
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• 2013

We analyzed the data from KYPS using the latent growth model which has been widely studied as an analysis method of longitudinal data. In this study, we applied repeated measures ANOVA to unconditional model in order for faster decision of the unconditional model of the latent growth model. Also, we compared the six-type models, the quadratic model and the model of which repeated measures ANOVA is applied.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.521-529
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• 2013

The acceptance of the test of the homogeneity for panel time series models allows for the pooling of the series to achieve parsimony. In this paper, we introduce a panel bilinear time series model as well as derive the stationary condition and the limiting distribution of the test statistic of the homogeneity test for the model. For the applications study, we use Korea Mumps data from January 2001 to December 2008. Finally, we perform test of homogeneity for the panel data with 8 independent bilinear time series.

• Environmental and Resource Economics Review
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• v.16 no.4
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• pp.803-832
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• 2007

Electricity and heat load profiles by use types on an hourly basis at the least are essential for assessing economic viability of new cogeneration and CES projects and for optimally operating existing cogeneration and CES facilities. We adopt a multilevel model to specify heat load profiles so as to utilize in a flexible manner the panel nature of our data on weather and heating/cooling use. Converting the multilevel model to the linear mixed-effects model, we estimate the model by panel FGLS. The estimated load profile model for each distinct use type accounts for the effects of temperature, humidity, each hour over the year, each day of the week, each type of legal holidays, and heating/cooling area on energy use. To save space, we feature in detail the heating profile of the household.

• Korea journal of population studies
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• v.29 no.3
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• pp.159-188
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• 2006

This study attempts to create a longitudinal dataset by linking tdata on the identical individuals across the monthly sample household management lists of the Economically Active Population Survey(EAPS) and the Household Income and Expenditure Survey(HIES). Using the data constructed through such process, the study also tryies to analyze the duration of longitudinal responses and the characteristics of nonrespondents. Between 1998 and 2002, longitudinal response rates had declined to 46% of total EAPS and 34% of total HIES. The fact that nonresponse was not a random phenomenon leads to concerns about the representativeness of the remaining sample. Using Cox's proportional hazard model the study revealed that the duration of longitudinal responses is affected by the ownership of house and the age of the respondent.

• Journal of Digital Convergence
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• v.12 no.7
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• pp.261-266
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• 2014

When the number of parameters in the time series model are diverse, it is hard to forecast because of the increasing error by a parameter estimation. If the homogeneity hypothesis which was obtained from the same model about severeal data for the time series is selected, it is easy to get the predictive value better. Nonlinear time-series panel data for each parameter for each time series, since there are so many parameters that are present, and the large number of parameters according to the parameter estimation error increases the accuracy of the forecast deteriorated. Panel present in the time series of multiple independent homogeneity is satisfied by a comprehensive time series to estimate and to test of the parameters. For studying about the homogeneity test for the m independent non-linear of the time series panel data, it needs to set the model and to make the normal conditions for the model, and to derive the homogeneity test statistic. Finally, it shows to obtain the limit distribution according to ${\chi}^2$ distribution. In actual analysis,, we can examine the result for the homogeneity test about nonlinear time series panel data which are 2 groups of stock price data.