• Journal of the Korean Data and Information Science Society
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• 제13권2호
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• pp.227-234
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• 2002

In this paper, we consider hierarchical Bayes generalized linear models for the analysis of longitudinal count data. Specifically we introduce the hierarchical Bayes random effects models. We discuss implementation of the Bayes procedures via Markov chain Monte Carlo (MCMC) integration techniques. The hierarchical Baye method is illustrated with a real dataset and is compared with other statistical methods.

• Journal of the Korean Data and Information Science Society
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• 제21권2호
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• pp.309-316
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• 2010

Support vector quantile regression (SVQR) is capable of providing more complete description of the linear and nonlinear relationships among response and input variables. In this paper we propose a weighted SVQR for the longitudinal data. Furthermore, we introduce the generalized approximate cross validation function to select the hyperparameters which affect the performance of SVQR. Experimental results are the presented, which illustrate the performance of the proposed SVQR.

• Journal of the Korean Data and Information Science Society
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• 제25권1호
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• pp.211-218
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• 2014

Qu et. al. (2000) proposed the quadratic inference functions (QIF) method to marginal model analysis of longitudinal data to improve the generalized estimating equations (GEE). It yields a substantial improvement in efficiency for the estimators of regression parameters when the working correlation is misspecified. But for the longitudinal data with time-dependent covariates, when the implicit full covariates conditional mean (FCCM) assumption is violated, the QIF can not provide more consistent and efficient estimator than GEE (Cho and Dashnyam, 2013). Lai and Small (2007) divided time-dependent covariates into three types and proposed generalized method of moment (GMM) for longitudinal data with time-dependent covariates. They showed that their GMM type II and GMM moment selection methods can be more ecient than GEE with independence working correlation (GEE-ind) in the case of type II time-dependent covariates. We develop upgraded QIF method for type II time-dependent covariates. We show that this upgraded QIF method can provide substantial gains in efficiency over QIF and GEE-ind in the case of type II time-dependent covariates.

• Communications for Statistical Applications and Methods
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• 제25권1호
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• pp.61-70
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• 2018

Modeling of the random effects covariance matrix in generalized linear mixed models (GLMMs) is an issue in analysis of longitudinal categorical data because the covariance matrix can be high-dimensional and its estimate must satisfy positive-definiteness. To satisfy these constraints, we consider the autoregressive and moving average Cholesky decomposition (ARMACD) to model the covariance matrix. The ARMACD creates a more flexible decomposition of the covariance matrix that provides generalized autoregressive parameters, generalized moving average parameters, and innovation variances. In this paper, we analyze longitudinal count data with overdispersion using GLMMs. We propose negative binomial loglinear mixed models to analyze longitudinal count data and we also present modeling of the random effects covariance matrix using the ARMACD. Epilepsy data are analyzed using our proposed model.

• 응용통계연구
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• 제29권1호
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• pp.113-122
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• 2016

경시적 자료는 반복적으로 측정된 다변량 자료의 한 형태로 임상학, 보건학, 경제학에서 자주 발생된다. 시계열자료와 구분되는 가장 큰 특징은 표본수와 공변량 효과의 추정에 있다. 경시적 자료는 일반적으로 시계열 자료보다 더 큰 표본 개체로 이루어져 있으며 연구의 주 관심은 특정 공변량의 효과를 추정하는 데 있다. 또한 시계열 자료보다 반복 측정 횟수가 짧으며 개체마다 다른 관측 횟수와 다른 관측 중단 시점을 가질 수 있다. 본 연구에서는 관측 시점과 관측 종료 원인이 경시자료와 서로 연관된 경우에 결합 모형을 적용함으로써 이들간의 연관성을 분석하고자한다. 따라서 이들 변량간의 연관성을 모형화하기 위해 이변량 랜덤효과가 적용된다. 실제 자료 분석에서는 간경변증 환자의 핼액 응고 수치 시간을 관심 있는 경시적 자료로 환자가 병원 방문시점과 관측 중단원인들간의 상호 연관관계를 규명하고자 하였다. 특히, 중도 절단원인은 사망이나 간이식을 받는 사건일 때 발생하는데 본 연구에서는 사망 사건과의 연관성이 고려되었다. 결과를 통해 혈액 응고 시간이 길고 병원 방문 시점이 빈번할수록 사망할 가능성이 높음을 알수 있었다. 또한 혈액응고 시간이 길수록 병원 방문 횟수가 빈번하였다.

• 아동복지연구
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• 제16권2호
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• pp.109-135
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• 2018

The purpose of this study was to analyze the longitudinal causal relationship between caregiver relations and peer relations of children in out-of-home care. We analyzed the three years(2011-2013) of longitudinal data from the Panel Study on Korean Children in Out-of-Home Care. The autoregressive cross-lagged model (ARCL) was used to measure the longitudinal causal relationship between caregiver relations and peer relations. As a result, first, caregiver relations and peer relations showed stability over time. In other words, the results of the measurement at three time points showed that the caregiver relations and peer relations at the previous time had a significant effect on the caregiver relations and peer relations at the later time point. Second, the previous caregiver relations had a significant effect on the subsequent peer relations over time. Third, the previous peer relations had a significant effect on the subsequent caregiver relations over time. This study confirmed the interrelationships of caregiver relations and peer relations of children in care by examining the longitudinal data using the longitudinal analysis method.

• Communications for Statistical Applications and Methods
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• 제9권3호
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• pp.703-713
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• 2002

In longitudinal studies, outcomes are repeatedly measured over time for each subject. It is common to have missing values or dropouts for longitudinal data. In this study time trend in longitudinal data with dropouts is of concern. The confounding of time trend with dropout process is investigated through simulation studies. Some simulation results are reported for binary responses as well as continuous responses with patterns of dropouts varying. It has been found that time trend is not confounded with random dropout process for binary responses when it is estimated using GEE.

• 대한가정학회지
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• 제47권3호
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• pp.103-118
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• 2009

This study was conducted to explore the factors that influence the development of happiness during childhood using longitudinal data obtained from the Korea Youth Panel Survey(KYPS). Specifically, the causal relationships between factors impacting the individual children, the home environment factor, and the happiness of children were examined over a 3-years-period. The subjects evaluated in this study included 2,844 children (1,524 boys and 1,320 girls) and 2,844 parents who were administered the KYPS. The data were analyzed using the SAS program. The results revealed that happiness that develops during childhood remains stable and constant, which indicates that prior happiness has a strong effect on future happiness. The individual factors affecting the children, which include schoolwork achievement and extra private education, were found to have a great influence on the development of happiness at all ages. The use of longitudinal data in this study is a new method in the field of Human Development.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1353-1360
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• 2008

An estimating procedure is introduced for the nonlinear mixed-effect Poisson regression, for longitudinal study, where data from different subjects are independent whereas data from same subject are correlated. The proposed procedure provides the estimates of the mean function of the response variables, where the canonical parameter is related to the input vector in a nonlinear form. The generalized cross validation function is introduced to choose optimal hyper-parameters in the procedure. Experimental results are then presented, which indicate the performance of the proposed estimating procedure.

• Journal of the Korean Data and Information Science Society
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• 제23권2호
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• pp.385-392
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• 2012

Logistic regression is a well known binary classification method in the field of statistical learning. Mixed-effect regression models are widely used for the analysis of correlated data such as those found in longitudinal studies. We consider kernel extensions with semiparametric fixed effects and parametric random effects for the logistic regression. The estimation is performed through the penalized likelihood method based on kernel trick, and our focus is on the efficient computation and the effective hyperparameter selection. For the selection of optimal hyperparameters, cross-validation techniques are employed. Numerical results are then presented to indicate the performance of the proposed procedure.