• Journal of the Korean Statistical Society
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• v.35 no.2
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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 Society for Precision Engineering
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• v.20 no.3
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• pp.58-65
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• 2003

Vehicle acceleration data from an accelerometer and wheel speed data from standard, 50-tooth antilock braking system wheel speed sensors are used to estimate the absolute longitudinal speed of a vehicle. We develop the four velocity estimation algorithms. And we compare experimental results with the Butterworth filtered speed from the fifth wheel and find that it is possible to estimate absolute longitudinal vehicle speed during a hard braking maneuver lasting three seconds.

• Journal of the Korean Society of Industry Convergence
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• v.3 no.1
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• pp.61-67
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• 2000

Effective shear modulus of continuous fiber reinforced polymeric composites is measured using a modified Iosipescu Shear Test(IST) and compared with data obtained by finite element analyses that a concept of unit cell is. It is found that the numerical results of the longitudinal shear modulus give a good agreement with experimental data at lower fiber volume fraction. In this paper, both the distance and stress transfer between the fibers are discussed as the major factors.

• Communications for Statistical Applications and Methods
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• v.27 no.2
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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.

• Korean Journal of Child Studies
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• v.22 no.4
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• pp.17-32
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• 2001

The purpose of this study was to explore the developmental patterns of self-concept of pre-adolescents and adolescents by using the longitudinal data originated by the Korean Institute for Research in the Behavioral Sciences (KIRBS). Specifically, using Structural Equation Modeling (SEM), the effects of parents and peer groups on the stability and change of self-concept were examined across a 9-year-period. The subjects were 62 children(29 boys and 33 girls) from the KIRBS longitudinal data. The results showed that, peer relationships revealed consistent effects on children's self-concept from pre-adolescence to mid-adolescence. In particular, this influence was most evident at 7 years of age and at 16 years of age. However, parental influence was almost non-existent for 7-year-olds, equivalent to peer relationships for 10-year-olds, and decreased for 16-year-olds. On the whole, parental and peer influence on self-concept gradually decrease in adolescence. Nevertheless, parental and peer influence continue to maintain a certain level of influence from childhood to adolescence. This study provides an understanding of developmental change and stability in the self-concept of Korean adolescents.

• Korean Journal of Childcare and Education
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• v.15 no.5
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• pp.23-45
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• 2019

Objective: The purpose of this study is to identify the longitudinal reciprocal relationship between young children's vocabulary ability and physical aggression in young children. Methods: Two waves of panel data(2013/2015) from the Panel Study of Korean Children were analyzed in this study by using an adapted version of Autoregressive cross-lagged modeling. A total of 306 five-year-old and seven-year-old preschoolers, and their mothers participated in the study. Autoregressive cross-lagged modeling for multiple groups was conducted by using AMOS 24.0. Results: First, vocabulary ability and physical aggression showed stability over time. Second, young children's vocabulary ability(t) had a statistically significant effect on physical aggression(t+1). Conclusion/Implications: This study confirmed the interrelationships of young children's vocabulary ability and physical aggression by examining longitudinal data using the longitudinal analysis method. This study highlights the importance of developing interventions to support language development with aggressive children. The results of the present study can be used as a source in developing policies for aggressive children and their parents.

• Human Ecology Research
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• v.59 no.3
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• pp.341-351
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• 2021

The purpose of this study was to examine the longitudinal relationships between Korean adolescents'mobile phone dependency, trajectories of depression, and self-regulated learning abilities. To achieve these goals, structural equation modeling analysis was conducted, using the 3rd, 5th and 7th wave of the data on 4th graders taken from the Korean Children and Youth Panel Survey. The results can be summarized as follows. First, growth-curve longitudinal analysis indicates that depression in 6th through 10th grade has increased. Second, mobile phone dependency among adolescents at 6th grade has a significant effect on both the initial value and the rate of change in depression. Also, the initial value and the rate of change in depression have significant relationships with mobile phone dependency at 10th grade. Moreover, both increased levels of mobile phone dependency and the rate of change in depression significantly influence adolescents'self-regulated learning abilities at 10th grade. Based on a longitudinal data set, these findings demonstrate the causal relationships between Korean adolescents'trajectories of depression and their mobile phone dependency. The findings also provide a comprehensive framework with implications for adolescents'development through an understanding of the relationships between adolescents'depression and mobile phone dependency, which impact their self-regulated learning abilities.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.65-83
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• 2022

Although various statistical methods have been developed to map time-dependent genetic factors, most identified genetic variants can explain only a small portion of the estimated genetic variation in longitudinal traits. Gene-gene and gene-time/environment interactions are known to be important putative sources of the missing heritability. However, mapping epistatic gene-gene interactions is extremely difficult due to the very large parameter spaces for models containing such interactions. In this paper, we develop a Gaussian process (GP) based nonparametric Bayesian variable selection method for longitudinal data. It maps multiple genetic markers without restricting to pairwise interactions. Rather than modeling each main and interaction term explicitly, the GP model measures the importance of each marker, regardless of whether it is mostly due to a main effect or some interaction effect(s), via an unspecified function. To improve the flexibility of the GP model, we propose a novel grid-based method for the within-subject dependence structure. The proposed method can accurately approximate complex covariance structures. The dimension of the covariance matrix depends only on the number of fixed grid points although each subject may have different numbers of measurements at different time points. The deviance information criterion (DIC) and the Bayesian predictive information criterion (BPIC) are proposed for selecting an optimal number of grid points. To efficiently draw posterior samples, we combine a hybrid Monte Carlo method with a partially collapsed Gibbs (PCG) sampler. We apply the proposed GP model to a mouse dataset on age-related body weight.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.224-232
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• 2015

In order to develop an active safety system which avoids or mitigates collisions with preceding vehicles such as autonomous emergency braking (AEB), accurate state estimation of the nearby vehicles is very important. In this paper, an algorithm is proposed using 3 dynamic models to better estimate the state of a vehicle which has various dynamic patterns in both longitudinal and lateral direction. In particular, the proposed algorithm is based on the Interacting Multiple Model (IMM) method which employs three different dynamic models, in cruise mode, lateral maneuver mode and longitudinal maneuver mode. In addition, a Probabilistic Data Association Filter (PDAF) is utilized as a data association algorithm which can improve the reliability of the measurement under a clutter environment. In order to verify the performance of the proposed method, it is simulated in comparison with a Kalman filter method which employs a single dynamic model. Finally, the proposed method is validated using radar data obtained from the field test in the proving ground.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.203-213
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• 2017

We study estimation and inference of joint conditional distributions of bivariate longitudinal outcomes using regression models and copulas. We consider a class of time-varying transformation models and combine the two marginal models using Gaussian copulas to estimate the joint models. Our models and estimation method can be applied in many situations where the conditional mean-based models are inadequate. Gaussian copulas combined with time-varying transformation models may allow convenient and easy-to-interpret modeling for the joint conditional distributions for bivariate longitudinal data. We apply our method to an epidemiological study of repeatedly measured bivariate cholesterol data.