• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.1
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• pp.85-93
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• 2015

Previous studies have estimated crash prediction models with the fixed effect model which assumes the fixed value of coefficients without considering characteristics of each intersections. However the fixed effect model would estimate under estimation of the standard error resulted in over estimation of t-value. In order to overcome these shortcomings, the random effect model can be used with considering heterogeneity of AADT, geometric information and unobserved factors. In this study, data collections from 89 intersections in Daejeon and estimates of crash prediction models were conducted using the random and fixed effect negative binomial regression model for comparison and analysis of two models. As a result of model estimates, AADT, speed limits, number of lanes, exclusive right turn pockets and front traffic signal were found to be significant. For comparing statistical significance of two models, the random effect model could be better statistical significance with -1537.802 of log-likelihood at convergence comparing with -1691.327 for the fixed effect model. Also likelihood ration value was computed as 0.279 for the random effect model and 0.207 for the fixed effect model. This mean that the random effect model can be improved for statistical significance of models comparing with the fixed effect model.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1367-1375
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• 2015

Consider the linear growth models for longitudinal data analysis. Several kind of linear growth models are selected such as time-effect and random-effect models as well as a dummy variable included model. In this work, simulation data are generated with normality assumption, and both binormal ROC curve and AUC are obtained and compared for various linear growth models. It is found that ROC curves have different shapes and AUC increase slowly, as values of the covariance increase and the time passes for random-effect models. On the other hand, AUC increases very fast as values of covariance decrease. When the covariance has positive value, we explored that the variances of random-effect models increase and the increment of AUC is smaller than that of AUC for time-effect models. And the increment of AUC for time-effect models is larger than the increment for random-effect models.

• Journal of Korean Society of Forest Science
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• v.106 no.2
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• pp.213-218
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• 2017

The purpose of this study was to develop age-biomass model in Chungnam region containing random effect. To develop the biomass model by species and tree component, data for Pinus densiflora in central region is collected to 30 plots (150 trees). The mixed model were used to fixed effect in the age-biomass relation for Pinus densiflora, with random effect representing correlation of survey area were obtained. To verify the evaluation of the model for random effect, the akaike information criterion (abbreviated as, AIC) was used to calculate the variance-covariance matrix, and residual of repeated data. The estimated variance-covariance matrix, and residual were -1.0022, 0.6240, respectively. The model with random effect (AIC=377.2) has low AIC value, comparison with other study relating to random effects. It is for this reason that random effect associated with categorical data were used in the data fitting process, the model can be calibrated to fit the Chungnam region by obtaining measurements. Therefore, the results of this study could be useful method for developing biomass model using random effects by region.

• The Korean Journal of Applied Statistics
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• v.13 no.1
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• pp.81-96
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• 2000

본 논문은 의학분야에서 주로 사용되는 메타분석 중 그룹화 임의효과 모형(grouped random effects model)을 프라빗 연결함수(probit link function)를 이용하여 베이즈적 관점에서 연구하였다. 이때 프라빗 함수를 강요하기 위해 잠재변수를 정의하였고, 사전 분포를 달리한 세가지 모형을 고려하였다. 주어진 세가지 모형들에게서 적합한 모형 선택을 위하여 베이즈 인자(Bayes factor, BF)와 유사베이즈 인자(pseudo-Bayes factor, PsBF)를 이용하였다. 깁스샘플러와 메트로폴리스 알고리즘을 이용하여 베이지안 계산상의 어려움을 해결하였다. 예로써, 새로운 간질약에 대한 효과를 조사하기 위하여 앞에서 제시된 방법으로 해석하였다.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.957-981
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• 2017

A generalized linear mixed model is an extension of a generalized linear model that allows random effect as well as provides flexibility in developing a suitable model when observations are correlated or when there are other underlying phenomena that contribute to resulting variability. We describe maximum likelihood estimation methods for logistic regression models that include random effects - the Laplace approximation, Gauss-Hermite quadrature, adaptive Gauss-Hermite quadrature, and pseudo-likelihood. Applications are provided with social science problems by analyzing the effect of mental health and life satisfaction on volunteer activities from Korean welfare panel data; in addition, we observe that the inclusion of random effects in the model leads to improved analyses with more reasonable inferences.

• 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.28 no.2
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• pp.211-219
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• 2015

Generalized linear mixed models are used to analyze longitudinal categorical data. Random effects specify the serial dependence of repeated outcomes in these models; however, the estimation of a random effects covariance matrix is challenging because of many parameters in the matrix and the estimated covariance matrix should satisfy positive definiteness. Several approaches to model the random effects covariance matrix are proposed to overcome these restrictions: modified Cholesky decomposition, moving average Cholesky decomposition, and partial autocorrelation approaches. We review several approaches and present potential future work.

• Journal of Korean Society of Forest Science
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• v.104 no.1
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• pp.111-116
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• 2015

The purpose of this study was to evaluate mixed model of dbh-height relation containing random effect. Data were obtained from a survey site for Quercus variabilis in Gangwon region and remeasured the same site after three years. The mixed model were used to fixed effect in the dbh-height relation for Quercus variabilis, with random effect representing correlation of survey period were obtained. To verify the evaluation of the model for random effect, the akaike information criterion (abbreviated as, AIC) was used to calculate the variance-covariance matrix, and residual of repeated data. The estimated variance-covariance matrix, and residual were -0.0291, 0.1007, respectively. The model with random effect (AIC = -215.5) has low AIC value, comparison with model with fixed effect (AIC = -154.4). It is for this reason that random effect associated with categorical data is used in the data fitting process, the model can be calibrated to fit repeated site by obtaining measurements. Therefore, the results of this study could be useful method for developing model using repeated measurement.

• Journal of Labour Economics
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• v.27 no.1
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• pp.1-25
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• 2004

In knowledge-based economy where the human capital has an strong compatibilities, the life cycle of technologies and skills get shorter, and the mobility of labor get greater, the role of the signal system of qualifications have greater importance. This article used the KLIPS(Korean Labor Institute Panel Study) data, and analysed the factors of the acquisition of qualifications and the employment and wage effects of the acquisition of qualifications by fixed effects logit model and random effects model. The lower school stratification acquired the more qualifications, and in the case of men the unemployed one acquired the more qualifications. The employment effects of the acquisition of qualifications are significant at first year and second year in women, but the men's of the employment effects of acquisition of qualifications are not significant. The wage effects of the acquisition of qualifications are not significant. The results of the regression suggest that in Korea the signal system of qualifications do not working, and that the qualifications in Korea need to reform.

• International Area Studies Review
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• v.14 no.1
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• pp.203-219
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• 2010

Nowadays an instability of the exchange rate on accounts of global finance crisis brings on a lot of an economic damage such as recession, decreasing of total trade and so on. However some countries which belong to be membership of the eurozone could escape economic slump shortly and easier than others. The reason for this is that they share with the Euro as a their own currency which is the second vehicle currency all of the world. This paper analyzes the correlation of joining the Euro zone and trade with pooled OLS, random effect estimation, and fixed effect estimation. A membership of the Euro zone are able to increase trade 11.3% ~ 25.3% one another on average since some country belongs to the Euro zone. It is very important for some countries which have a plan to affiliate the Euro zone sooner or later to realize economic effect because of a protection of the Euro zone as well as political power.