• 한국ITS학회 논문지
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• 제16권1호
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• pp.26-37
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• 2017

본 연구는 random effects Tobit 회귀모형을 이용하여 도심지 교차로에 대한 교통사고모형을 개발하여 교통사고와 요인간의 상관관계를 파악하는 것이 목적이다. Random effects Tobit 회귀모형의 적용성을 비교 분석하기 위하여 fixed effect Tobit 회귀모형을 산정하였다. 산정결과, 교통량, 제한속도, 차로수, 토지이용, 우회전차로, 전방신호등이 유효한 변수로 나타났으며, 총 교통사고율에 대한 random effects 모형의 모형 적합도(결정계수: 0.418, 로그-우도함수값: -3210.103, 우도비: 0.056)와 모형 설명력(MAD: 19.533, MAPE: 75.725, RMSE: 26.886)은 fixed effects 모형의 모형 적합도 (결정계수: 0.298, 로그-우도함수값: -3276.138, 우도비: 0.037)와 모형 설명력(MAD: 20.725, MAPE: 82.473, RMSE: 27.267)보다 우수한 것으로 나타났으며, 부상교통사고율에 대한 교통사고모형에서도 총 교통사고율의 산정결과와 동일하게 나타나 두 모형에서 random effects Tobit 회귀모형이 다소 우수한 것으로 분석되었다.

• Journal of the Korean Data and Information Science Society
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• 제17권1호
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• pp.123-130
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• 2006

This paper discusses about how to build up a mixed-effects model using cumulative logits when some factors are fixed and others are random. Location effects are considered as random effects by choosing them randomly from a population of locations. Estimation procedure for the unknown parameters in a suggested model is also discussed by an illustrated example.

• Journal of the Korean Data and Information Science Society
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• 제25권6호
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• pp.1263-1272
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• 2014

경시적 범주형자료 (longitudinal categorical data)는 의학, 보건학, 그리고 사회과학에서 많이 발생하는 자료이다. 이러한 자료는 반복측정으로 인한 결과치들의 상관관계를 설명하면서 공변량의 효과를 설명해야 한다. 이 논문에서 모집단에 대한 공변량의 효과를 추정하면서 우도함수에 기초한 모형인 주변화 변량효과모형 (marginalized random effects model)을 소개하고, 그 모형의 어떻게 발전했는지를 고찰한다. 그리고 실제 자료를 이용하여 제시된 모형을 설명한다.

• Journal of the Korean Data and Information Science Society
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• 제18권2호
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• pp.471-479
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• 2007

This paper discusses about how to build up mixed-effects model for analysing ordinal response data by using cumulative logits. Random factors are assumed to be coming from the designed sampling scheme for choosing observational units. Since the observed responses of individuals are ordinal, a proportional odds model with two random effects is suggested. Estimation procedure for the unknown parameters in a suggested model is also discussed by an illustrated example.

• Communications for Statistical Applications and Methods
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• 제25권3호
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• pp.275-282
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• 2018

This paper discusses the use of projections for the sums of squares in the analyses of variance for two-way nested design data. The model for this data is assumed to only have random effects. Two different sizes of experimental units are required for a given experimental situation, since nesting is assumed to occur both in the treatment structure and in the design structure. So, variance components are coming from the sources of random effects of treatment factors and error terms in different sizes of experimental units. The model for this type of experimental situation is a random effects model with more than one error terms and therefore estimation of variance components are concerned. A projection method is used for the calculation of sums of squares due to random components. Squared distances of projections instead of using the usual reductions in sums of squares that show how to use projections to estimate the variance components associated with the random components in the assumed model. Expectations of quadratic forms are obtained by the Hartley's synthesis as a means of calculation.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2006년도 PROCEEDINGS OF JOINT CONFERENCEOF KDISS AND KDAS
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• pp.1-6
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• 2006

We consider the issue of Bayesian prediction of the unobservable random effects, And we characterize priors that ensure approximate frequentist validity of posterior quantiles of unobservable random effects. Finally we show that the probability matching criteria for prediction of unobservable random effects in one-way random ANOVA model.

• 한국유기농업학회지
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• 제23권1호
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• pp.31-42
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• 2015

This thesis has analyzed the determination factor for the crop insurance of rice focused on paddy rice. The analysis on each farmer has been used with integrated probit model & random effects probit model. It has shown in the analysis result of determination factor for buying the crop insurance of paddy rice farmer through integrated probit model & random effects probit model that the higher age, degree of education, cultivated area, and amount of received insurance money and the lower in a number of family member have revealed the higher possibility to buy the crop insurance in the integrated probit model. While the random effects probit model has shown a higher possibility to buy the crop insurance as the higher age, cultivated area, and amount of received insurance money.

• Communications for Statistical Applications and Methods
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• 제23권2호
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• pp.179-187
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• 2016

The fixed effects panel probit model faces "incidental parameters problem" because it has a property that the number of parameters to be estimated will increase with sample size. The maximum likelihood estimation fails to give a consistent estimator of slope parameter. Unlike the panel regression model, it is not feasible to find an orthogonal reparameterization of fixed effects to get a consistent estimator. In this note, a hierarchical Bayesian model is proposed. The model is essentially equivalent to the frequentist's random effects model, but the individual specific effects are estimable with the help of Gibbs sampling. The Bayesian estimator is shown to reduce reduced the small sample bias. The maximum likelihood estimator in the random effects model is also efficient, which contradicts Green (2004)'s conclusion.

• Journal of the Korean Statistical Society
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• 제27권4호
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• pp.407-420
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• 1998

This paper is concerned with selecting covariates to be included in building linear random effects models designed to analyze clustered response normal data. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting premising subsets of covariates. The approach reformulates the linear random effects model in a hierarchical normal and point mass mixture model by introducing a set of latent variables that will be used to identify subset choices. The hierarchical model is flexible to easily accommodate sign constraints in the number of regression coefficients. Utilizing Gibbs sampler, the appropriate posterior probability of each subset of covariates is obtained. Thus, In this procedure, the most promising subset of covariates can be identified as that with highest posterior probability. The procedure is illustrated through a simulation study.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.1017-1026
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• 2005

We consider the problem of modeling count data where the observation period is determined by the survival time of the individual under study. We assume random effects or frailty model to allow for a possible association between the death times and the counts. We assume that, given a random effect, the death times follow a Weibull distribution with a rate that depends on some covariates. For the counts, given the random effect, a Poisson process is assumed with the intensity depending on time and the covariates. A gamma model is assumed for the random effect. Maximum likelihood estimators of the model parameters are obtained. The model is applied to data set of patients with breast cancer who received a bone marrow transplant. A model for the time to death and the number of supportive transfusions a patient received is constructed and consequences of the model are examined.