• Communications for Statistical Applications and Methods
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• 제28권4호
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• pp.369-391
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• 2021

The vlda is an R (R Development Core team et al., 2011) package which provides functions for visualization of multidimensional longitudinal data. In particular, the R package vlda was developed to assist in producing a plot that more effectively expresses changes over time for two different types (long format and wide format) and uses a consistent calling scheme for longitudinal data. The main features of this package allow us to identify the relationship between categories and objects using an indicator matrix with object information, as well as to cluster objects. The R package vlda can be used to understand trends in observations over time in addition to identifying relative relationships at a simple visualization level. It also offers a new interactive implementation to perform additional interpretation, therefore it is useful for longitudinal data visual analysis. Due to the synergistic relationship between the existing VLDA plot and interactive features, the user is empowered by a refined observe the visual aspects of the VLDA plot layout. Furthermore, it allows the projection of supplementary information (supplementary objects and variables) that often occurs in longitudinal data of graphs. In this study, practical examples are provided to highlight the implemented methods of real applications.

• 응용통계연구
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• 제29권7호
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• pp.1459-1473
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• 2016

시간의 흐름에 따라 관측되는 경시적(longitudinal) 자료의 경우, 경시적 자료와 생존(survival) 자료가 종종 동시에 수집된다. 이 때 경시적 자료에서 발생하는 결측이 생존자료와의 연관성으로 인해 발생한 무시할 수 없는 결측(non-ignorable missing)이라면, 경시적 자료분석 방법만으로는 두 자료 간의 연관성을 고려하지 않아 독립변수에 대한 효과는 편향된 결과를 얻게 된다. 이러한 문제를 해결하기 위해서 결측의 원인이 생존시간과 연관되어 있으므로 생존모형을 고려하여 불편추정량을 얻기 위해 경시적 자료와 생존자료의 결합모형에 대한 연구가 이루어져 왔다. 본 논문은 경시적 자료의 형태가 영이 많이 존재하는 영과잉 가산자료(zero-inflated count data)와 생존자료의 결합모형을 연구하였다. 경시적 영과잉 가산자료와 생존자료는 각각 허들모형(hurdle model)과 비례위험모형(proportional hazards model)의 부 모형을 적용하였고, 두 부 모형들의 변량효과가 다변량 정규분포를 따른다는 가정을 통하여 결합하였다. 모수의 최우추정법으로 EM 알고리즘을 활용하였고, 추정된 표준오차를 계산하기 위해 프로파일 우도(profile likelihood)를 이용하였다. 최종적으로 모의실험을 통해 두 부 모형의 변량효과 간 상관관계가 존재하는 경우 결합모형이 개별적 모형보다 편의와 포함확률(coverage probability)의 측면에서 더 우수함을 보였다.

• 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.

• 인터넷정보학회논문지
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• 제21권4호
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• pp.77-85
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• 2020

본 연구는 학생들의 성장에 따른 미디어 교육 활용 특성 종단적 변화를 알아보고자 하였다. 이를 위해 미디어의 교육적 활용 특성을 학습이용, 정보이용, 그리고 게임이용으로 구분하였다. 잠재성장모형을 적용하여 학습이용, 정보이용, 게임이용의 종단적 변화를 탐색하였다. 이후 3가지 미디어 교육적 활용 특성의 종단적 변화에서 성별 차이를 검증하였다. 한국청소년패널조사(KYPS)의 중등2패널을 활용하여 4년간 반복 추적 조사한 3,499명의 데이터를 분석하였다. 연구결과 (a) 학년이 증감함으로써 미디어의 학습이용과 정보이용의 변화율은 증가하는 경향을 나타내었다. (b) 여학생의 미디어 학습이용과 정보이용의 초기치와 변화율이 높은 것으로 나타났다. (c) 학년이 증가함으로써 미디어의 게임이용은 변화율이 감소하는 것으로 나타났다. (d) 미디어 게임이용에서는 초기치에는 남학생이 여학생보다 높은 것으로 나타났으나, 변화율에는 유의한 차이가 없는 것으로 나타났다.

• Journal of the Korean Statistical Society
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• 제35권2호
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• pp.213-224
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• 2006

We investigate the influence of subjects or observations on regression coefficients of generalized estimating equations using the influence function and the derivative influence measures. The influence function for regression coefficients is derived and its sample versions are used for influence analysis. The derivative influence measures under certain perturbation schemes are derived. It can be seen that the influence function method and the derivative influence measures yield the same influence information. An illustrative example in longitudinal data analysis is given and we compare the results provided by the influence function method and the derivative influence measures.

• 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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• 제27권1호
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• pp.15-35
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• 2020

In research on behavioral studies, significant attention has been paid to the stage-sequential process for multiple latent class variables. We now explore the stage-sequential process of multiple latent class variables using the multivariate latent class profile analysis (MLCPA). A latent profile variable, representing the stage-sequential process in MLCPA, is formed by a set of repeatedly measured categorical response variables. This paper proposes the extended MLCPA in order to explain an association between the latent profile variable and the latent group variable as a form of a two-dimensional contingency table. We applied the extended MLCPA to the National Longitudinal Survey on Youth 1997 (NLSY97) data to investigate the association between of developmental progression of depression and substance use behaviors among adolescents who experienced Authoritarian parental styles in their youth.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2004년도 춘계학술대회 논문집
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• pp.1102-1107
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• 2004

High-speed railway bridges subject to effect of statical loads by temperature change as well as dynamic loads by interaction between vehicle load which run specially fast and behavior of bridges, If suitable longitudinal expansion by temperature change of bridge does not happened, it can cause unhealthy condition for the parts of bridges as well as can generate addition stress to bridges, For these reason, Analysis and Estimation of data about behavior of bridges occupies important factor in that estimate the remaining life of bridges and select the maintenance, repair and retrofit. In this paper, Analysis for the long-term behavior of bridges using Longitudinal displacement and Temperature data that is actuality measured data to the bridges of Seoul-Busan high speed railroad test section has been made.

• Communications for Statistical Applications and Methods
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• 제27권2호
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• pp.241-254
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• 2020

This study proposes generalized models of joint latent class analysis (JLCA) for longitudinal data in two approaches, a JLCA with latent profile (JLCPA) and a JLCA with latent transition (JLTA). Our models reflect cross-sectional as well as longitudinal dependence among multiple latent classes and track multiple class-sequences over time. For the identifiability and meaningful inference, EM algorithm produces maximum-likelihood estimates under local independence assumptions. As an empirical analysis, we apply our models to track the joint patterns of adolescent depression and anxiety among US adolescents and show that both JLCPA and JLTA identify three adolescent emotional well-being subgroups. In addition, JLCPA classifies two representative profiles for these emotional well-being subgroups across time, and these profiles have different tendencies according to the parent-adolescent-relationship subgroups.

• International Journal of Contents
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• 제15권3호
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• pp.7-12
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• 2019

The purpose of this study was to analyze characteristics and patterns of 'better class' by using the longitudinal text mining big data analysis technique from subjective lecture evaluation comments. First, this study classified upper 30% classes to deduce certain characteristics and patterns from every five-year subjective text data for 10 years. A total of 47,177courses (100%) from spring semester 2005 to fall semester 2014 were analyzed from a university at a metropolitan city in the mid area of South Korea. This study extracted meaningful words such as good, course, professor, appreciation, lecture, interesting, useful, know, easy, improvement, progress, teaching material, passion, and concern from the order of frequency 2005-2009. The other set of words were class, appreciation, professor, good, course, interesting, understanding, useful, help, student, effort, thinking, not difficult, explanation, lecture, hard, pleasant, easy, study, examination, like, various, fun, and knowledge 2010-2014. This study suggests that the characteristics and patterns of 'better class' at college, should be analyzed according to different academic code such as liberal arts, fine arts, social science, engineering, math and science, and etc.