• Title/Summary/Keyword: Longitudinal data

Search Result 1,655, Processing Time 0.027 seconds

Risk Evaluation of Longitudinal Cracking in Concrete Deck of Box Girder Bridge (콘크리트 박스거더 교량 바닥판의 종방향 균열 위험성 정가)

  • Kim, Eui-Sung
    • Journal of the Korean Society of Safety
    • /
    • v.23 no.5
    • /
    • pp.84-90
    • /
    • 2008
  • The occurrence of longitudinal cracking in concrete deck of box girder bridge is affected by many factors, but the most important factors are the shrinkage and thermal gradient of deck slabs. In this study, therefore, the tensile stresses at the bottom of deck were calculated from the experimental data(autogeneous shrinkage, drying shrinkage, and thermal gradient of deck slab). Also, the possibility of longitudinal cracks at bottom of deck was estimated. For this purpose, full-scale box girder segments have been fabricated and tested. The thermal gradients and shrinkage strains of deck slabs were measured after placement of concrete. Also, analytic program was conducted for the evaluation of longitudinal cracking in bridge deck considering differential shrinkage induced from non-uniform moisture distributions in concrete.

Bayesian Conway-Maxwell-Poisson (CMP) regression for longitudinal count data

  • Morshed Alam ;Yeongjin Gwon ;Jane Meza
    • Communications for Statistical Applications and Methods
    • /
    • v.30 no.3
    • /
    • pp.291-309
    • /
    • 2023
  • Longitudinal count data has been widely collected in biomedical research, public health, and clinical trials. These repeated measurements over time on the same subjects need to account for an appropriate dependency. The Poisson regression model is the first choice to model the expected count of interest, however, this may not be an appropriate when data exhibit over-dispersion or under-dispersion. Recently, Conway-Maxwell-Poisson (CMP) distribution is popularly used as the distribution offers a flexibility to capture a wide range of dispersion in the data. In this article, we propose a Bayesian CMP regression model to accommodate over and under-dispersion in modeling longitudinal count data. Specifically, we develop a regression model with random intercept and slope to capture subject heterogeneity and estimate covariate effects to be different across subjects. We implement a Bayesian computation via Hamiltonian MCMC (HMCMC) algorithm for posterior sampling. We then compute Bayesian model assessment measures for model comparison. Simulation studies are conducted to assess the accuracy and effectiveness of our methodology. The usefulness of the proposed methodology is demonstrated by a well-known example of epilepsy data.

Development of Pavement Distress Prediction Models Using DataPave Program (DataPave 프로그램을 이용한 포장파손예측모델개발)

  • Jin, Myung-Sub;Yoon, Seok-Joon
    • International Journal of Highway Engineering
    • /
    • v.4 no.2 s.12
    • /
    • pp.9-18
    • /
    • 2002
  • The main distresses that influence pavement performance are rutting, fatigue cracking, and longitudinal roughness. Thus, it is important to analyze the factors that affect these three distresses, and to develop prediction models. In this paper, three distress prediction models were developed using DataPave program which stores data from a wide variety of pavement sections In the United States. Also, sensitivity studies were conducted to evaluate how the input variables impact on the distresses. The result of sensitivity study for the prediction model of rutting showed that asphalt content, air void, and optimum moisture content of subgrade were the major factors that affect rutting. The output of sensitivity study for the prediction model of fatigue cracking revealed that asphalt consistency, asphalt content, and air void were the most influential variables. The prediction model of longitudinal roughness indicated asphalt consistency, #200 passing percent of subgrade aggregate, and asphalt content were the factors that affect longitudinal roughness.

  • PDF

A Study on the Real-Time Parameter Estimation of DURUMI-II for Control Surface Fault Using Flight Test Data (Longitudinal Motion)

  • Park, Wook-Je;Kim, Eung-Tai;Song, Yong-Kyu;Ko, Bong-Jin
    • International Journal of Control, Automation, and Systems
    • /
    • v.5 no.4
    • /
    • pp.410-418
    • /
    • 2007
  • For the purpose of fault detection of the primary control surface, real-time estimation of the longitudinal stability and control derivatives of the DURUMI-II using the flight data is considered in this paper. The DURUM-II, a research UAV developed by KARI, is designed to have split control surfaces for the redundancy and to guarantee safety during the fault mode flight test. For fault mode analysis, the right elevator was deliberately fixed to the specified deflection condition. This study also mentions how to implement the multi-step control input efficiently, and how to switch between the normal mode and the fault mode during the flight test. As a realtime parameter estimation technique, Fourier transform regression method was used and the estimated data was compared with the results of the analytical method and the other available method. The aerodynamic derivatives estimated from the normal mode flight data and the fault mode data are compared and the possibility to detect the elevator fault by monitoring the control derivative estimated in real time by the computer onboard was discussed.

Quadratic inference functions in marginal models for longitudinal data with time-varying stochastic covariates

  • Cho, Gyo-Young;Dashnyam, Oyunchimeg
    • Journal of the Korean Data and Information Science Society
    • /
    • v.24 no.3
    • /
    • pp.651-658
    • /
    • 2013
  • For the marginal model and generalized estimating equations (GEE) method there is important full covariates conditional mean (FCCM) assumption which is pointed out by Pepe and Anderson (1994). With longitudinal data with time-varying stochastic covariates, this assumption may not necessarily hold. If this assumption is violated, the biased estimates of regression coefficients may result. But if a diagonal working correlation matrix is used, irrespective of whether the assumption is violated, the resulting estimates are (nearly) unbiased (Pan et al., 2000).The quadratic inference functions (QIF) method proposed by Qu et al. (2000) is the method based on generalized method of moment (GMM) using GEE. The QIF yields a substantial improvement in efficiency for the estimator of ${\beta}$ when the working correlation is misspecified, and equal efficiency to the GEE when the working correlation is correct (Qu et al., 2000).In this paper, we interest in whether the QIF can improve the results of the GEE method in the case of FCCM is violated. We show that the QIF with exchangeable and AR(1) working correlation matrix cannot be consistent and asymptotically normal in this case. Also it may not be efficient than GEE with independence working correlation. Our simulation studies verify the result.

Generalized methods of moments in marginal models for longitudinal data with time-dependent covariates

  • Cho, Gyo-Young;Dashnyam, Oyunchimeg
    • Journal of the Korean Data and Information Science Society
    • /
    • v.24 no.4
    • /
    • pp.877-883
    • /
    • 2013
  • The quadratic inference functions (QIF) method proposed by Qu et al. (2000) and the generalized method of moments (GMM) for marginal regression analysis of longitudinal data with time-dependent covariates proposed by Lai and Small (2007) both are the methods based on generalized method of moment (GMM) introduced by Hansen (1982) and both use generalized estimating equations (GEE). Lai and Small (2007) divided time-dependent covariates into three types such as: Type I, Type II and Type III. In this paper, we compared these methods in the case of Type II and Type III in which full covariates conditional mean assumption (FCCM) is violated and interested in whether they can improve the results of GEE with independence working correlation. We show that in the marginal regression model with Type II time-dependent covariates, GMM Type II of Lai and Small (2007) provides more ecient result than QIF and for the Type III time-dependent covariates, QIF with independence working correlation and GMM Type III methods provide the same results. Our simulation study showed the same results.

Longitudinal Data Analysis for School-aged Adolescents' Obesity Rates across the States (미국 청소년의 비만에 관한 종단적 분석)

  • Kim, TaeEung;Kim, Jongho;Hwang, Sunhwan
    • The Journal of the Korea Contents Association
    • /
    • v.16 no.8
    • /
    • pp.743-755
    • /
    • 2016
  • The objectives of this research is to examine: 1) how the rates of adolescents' BMI change over time in terms of the state level; and 2) development difference in the state level of BMI in terms of children's obesogenic behaviors from 1999 to 2011. Data were drawn from the 1999-2011 Youth Risk Behavior Survey in the United States (N=260, 293, grades 9-12, and 27 states). Ordinary least squares regression and hierarchical linear modeling were utilized to capture a longitudinal time effect of school-aged adolescents' obesity rates across the states, controlling for demographics and nutrition- and physical activity-related behaviors. The state's level of children's BMI percentile was significantly associated with longitudinal time. Longitudinal time effect across the states appears to play an important factor associated with children's decrease of BMI percentile. Therefore the states' implementation of physical activity and nutritional policies seems to be effective for preventing and reducing childhood obesity during last decade. More attention should focus on enforcing the policy and overcoming current barriers in order to minimize children's obesogenic factor.

Longitudinal Study of Child-Teacher Relationship and Peer Interactions Based on Latent Profile Analysis (유아-교사 관계의 잠재프로파일 집단이 유아의 또래 상호작용에 미치는 영향에 관한 종단 연구)

  • Yi, Ye Jin;Shin, Yoolim
    • Human Ecology Research
    • /
    • v.54 no.3
    • /
    • pp.321-332
    • /
    • 2016
  • This study clarified the maintenance of relationship between children and teachers based on longitudinal data and explored the latent classes. It clarified the latent classes connection with the children's peer play interaction. The subjects of this study were 194 children (aged 3) who attended 11 different kindergartens and daycare centers. We collected data three times (once every 6 months) until they reached age 4. The results of this study were: first, closeness, conflict, and dependence of child-teacher relationship that showed a continuous short-term connection. Second, we classified the child-teacher relationship into three groups according to longitudinal data. Those groups were, 'low level maintenance group' which had the lowest conflict and dependence compared to the highest closeness with teacher, 'middle level maintenance group' which had the teacher relationship in the middle level of the sub element area, and 'high level maintenance group' which showed high conflict and dependence compared to low closeness with the teacher. Third, the group which maintains a longitudinal high conflict.dependence showed more interruption and disruption behavior than the group which maintained a low conflict and dependence. In conclusion, the child-teacher relationship seemed to be the steady characteristic because it showed the early formation of a stable relationship. It was possible to predict the child's peer interaction through an early child-teacher relationship. Teachers need to be educated by the kindergarten and daily care center because the early formation of a child-teacher relationship can be the foundation of child's later peer and teacher relationships.

Reliability in longitudinal study (종단적 연구의 신뢰도)

  • Jinuk Kim
    • The Korean Journal of Applied Statistics
    • /
    • v.37 no.1
    • /
    • pp.61-72
    • /
    • 2024
  • The purpose of this study is to investigate retest reliabilities in longitudinal study, the same test is administered repeatedly over time. Linear mixed models were used to establish various situations of tests occurred in longitudinal study. Combination of two types of true value and three types of systematic error was considered. In order to apply the models to real longitudinal data, height data from the Berkeley growth study and vocabulary score data from the University of Chicago experimental school were used. Using the mixed model, there is an advantage that the reliability can be determined by selecting the covariance structure of the true value and the error separately. However, in order to properly analyze the reliability, researchers need to consider variations that can occur in measurement, such as characteristics of subject, the test, and the the treatment applied in the study. And the proper model should be selected and the quality of the measurement should be evaluated for each trial.

INFLUENCE ANALYSIS FOR GENERALIZED ESTIMATING EQUATIONS

  • Jung Kang-Mo
    • Journal of the Korean Statistical Society
    • /
    • v.35 no.2
    • /
    • pp.213-224
    • /
    • 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.