• Title/Summary/Keyword: 임의효과

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The Development of Biomass Model for Pinus densiflora in Chungnam Region Using Random Effect (임의효과를 이용한 충남지역 소나무림의 바이오매스 모형 개발)

  • Pyo, Jungkee;Son, Yeong Mo
    • Journal of Korean Society of Forest Science
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    • v.106 no.2
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    • pp.213-218
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    • 2017
  • The purpose of this study was to develop age-biomass model in Chungnam region containing random effect. To develop the biomass model by species and tree component, data for Pinus densiflora in central region is collected to 30 plots (150 trees). The mixed model were used to fixed effect in the age-biomass relation for Pinus densiflora, with random effect representing correlation of survey area were obtained. To verify the evaluation of the model for random effect, the akaike information criterion (abbreviated as, AIC) was used to calculate the variance-covariance matrix, and residual of repeated data. The estimated variance-covariance matrix, and residual were -1.0022, 0.6240, respectively. The model with random effect (AIC=377.2) has low AIC value, comparison with other study relating to random effects. It is for this reason that random effect associated with categorical data were used in the data fitting process, the model can be calibrated to fit the Chungnam region by obtaining measurements. Therefore, the results of this study could be useful method for developing biomass model using random effects by region.

Survey of Models for Random Effects Covariance Matrix in Generalized Linear Mixed Model (일반화 선형혼합모형의 임의효과 공분산행렬을 위한 모형들의 조사 및 고찰)

  • Kim, Jiyeong;Lee, Keunbaik
    • The Korean Journal of Applied Statistics
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    • v.28 no.2
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    • pp.211-219
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    • 2015
  • Generalized linear mixed models are used to analyze longitudinal categorical data. Random effects specify the serial dependence of repeated outcomes in these models; however, the estimation of a random effects covariance matrix is challenging because of many parameters in the matrix and the estimated covariance matrix should satisfy positive definiteness. Several approaches to model the random effects covariance matrix are proposed to overcome these restrictions: modified Cholesky decomposition, moving average Cholesky decomposition, and partial autocorrelation approaches. We review several approaches and present potential future work.

메타분석에서 그룹화 임의효과 모형의 베이지안 해석

  • 정윤식;정호진
    • The Korean Journal of Applied Statistics
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    • v.13 no.1
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    • pp.81-96
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    • 2000
  • 본 논문은 의학분야에서 주로 사용되는 메타분석 중 그룹화 임의효과 모형(grouped random effects model)을 프라빗 연결함수(probit link function)를 이용하여 베이즈적 관점에서 연구하였다. 이때 프라빗 함수를 강요하기 위해 잠재변수를 정의하였고, 사전 분포를 달리한 세가지 모형을 고려하였다. 주어진 세가지 모형들에게서 적합한 모형 선택을 위하여 베이즈 인자(Bayes factor, BF)와 유사베이즈 인자(pseudo-Bayes factor, PsBF)를 이용하였다. 깁스샘플러와 메트로폴리스 알고리즘을 이용하여 베이지안 계산상의 어려움을 해결하였다. 예로써, 새로운 간질약에 대한 효과를 조사하기 위하여 앞에서 제시된 방법으로 해석하였다.

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Likelihood-Based Inference of Random Effects and Application in Logistic Regression (우도에 기반한 임의효과에 대한 추론과 로지스틱 회귀모형에서의 응용)

  • Kim, Gwangsu
    • The Korean Journal of Applied Statistics
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    • v.28 no.2
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    • pp.269-279
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    • 2015
  • This paper considers inferences of random effects. We show that the proposed confidence distribution (CD) performs well in logistic regression for random intercepts with small samples. Real data analyses are also done to identify the subject effects clearly.

A Study on Developing Crash Prediction Model for Urban Intersections Considering Random Effects (임의효과를 고려한 도심지 교차로 교통사고모형 개발에 관한 연구)

  • Lee, Sang Hyuk;Park, Min Ho;Woo, Yong Han
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.14 no.1
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    • pp.85-93
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    • 2015
  • Previous studies have estimated crash prediction models with the fixed effect model which assumes the fixed value of coefficients without considering characteristics of each intersections. However the fixed effect model would estimate under estimation of the standard error resulted in over estimation of t-value. In order to overcome these shortcomings, the random effect model can be used with considering heterogeneity of AADT, geometric information and unobserved factors. In this study, data collections from 89 intersections in Daejeon and estimates of crash prediction models were conducted using the random and fixed effect negative binomial regression model for comparison and analysis of two models. As a result of model estimates, AADT, speed limits, number of lanes, exclusive right turn pockets and front traffic signal were found to be significant. For comparing statistical significance of two models, the random effect model could be better statistical significance with -1537.802 of log-likelihood at convergence comparing with -1691.327 for the fixed effect model. Also likelihood ration value was computed as 0.279 for the random effect model and 0.207 for the fixed effect model. This mean that the random effect model can be improved for statistical significance of models comparing with the fixed effect model.

Maximum likelihood estimation of Logistic random effects model (로지스틱 임의선형 혼합모형의 최대우도 추정법)

  • Kim, Minah;Kyung, Minjung
    • The Korean Journal of Applied Statistics
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    • v.30 no.6
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    • pp.957-981
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    • 2017
  • A generalized linear mixed model is an extension of a generalized linear model that allows random effect as well as provides flexibility in developing a suitable model when observations are correlated or when there are other underlying phenomena that contribute to resulting variability. We describe maximum likelihood estimation methods for logistic regression models that include random effects - the Laplace approximation, Gauss-Hermite quadrature, adaptive Gauss-Hermite quadrature, and pseudo-likelihood. Applications are provided with social science problems by analyzing the effect of mental health and life satisfaction on volunteer activities from Korean welfare panel data; in addition, we observe that the inclusion of random effects in the model leads to improved analyses with more reasonable inferences.

Applicability Evaluation of a Mixed Model for the Analysis of Repeated Inventory Data : A Case Study on Quercus variabilis Stands in Gangwon Region (반복측정자료 분석을 위한 혼합모형의 적용성 검토: 강원지역 굴참나무 임분을 대상으로)

  • Pyo, Jungkee;Lee, Sangtae;Seo, Kyungwon;Lee, Kyungjae
    • Journal of Korean Society of Forest Science
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    • v.104 no.1
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    • pp.111-116
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    • 2015
  • The purpose of this study was to evaluate mixed model of dbh-height relation containing random effect. Data were obtained from a survey site for Quercus variabilis in Gangwon region and remeasured the same site after three years. The mixed model were used to fixed effect in the dbh-height relation for Quercus variabilis, with random effect representing correlation of survey period were obtained. To verify the evaluation of the model for random effect, the akaike information criterion (abbreviated as, AIC) was used to calculate the variance-covariance matrix, and residual of repeated data. The estimated variance-covariance matrix, and residual were -0.0291, 0.1007, respectively. The model with random effect (AIC = -215.5) has low AIC value, comparison with model with fixed effect (AIC = -154.4). It is for this reason that random effect associated with categorical data is used in the data fitting process, the model can be calibrated to fit repeated site by obtaining measurements. Therefore, the results of this study could be useful method for developing model using repeated measurement.

ROC curve and AUC for linear growth models (선형성장모형에 대한 ROC 곡선과 AUC)

  • Hong, Chong Sun;Yang, Dae Soon
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.6
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    • pp.1367-1375
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    • 2015
  • Consider the linear growth models for longitudinal data analysis. Several kind of linear growth models are selected such as time-effect and random-effect models as well as a dummy variable included model. In this work, simulation data are generated with normality assumption, and both binormal ROC curve and AUC are obtained and compared for various linear growth models. It is found that ROC curves have different shapes and AUC increase slowly, as values of the covariance increase and the time passes for random-effect models. On the other hand, AUC increases very fast as values of covariance decrease. When the covariance has positive value, we explored that the variances of random-effect models increase and the increment of AUC is smaller than that of AUC for time-effect models. And the increment of AUC for time-effect models is larger than the increment for random-effect models.

A Study on Vehicle Platoon Formation on Freeways (Platoon Generation Model) (고속도로 교통류의 차량군집현상에 관한 연구 (차량군집화 발생모형))

  • Lee, Jun;Jeong, Jin-Hyeok
    • Journal of Korean Society of Transportation
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    • v.25 no.2 s.95
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    • pp.109-120
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    • 2007
  • This study developed a methodology that can reflect more realistic traffic flow to understand platoons on highways. The understanding of the phenomenon nay secure highway safety and authoritative credibility in highway planning. In this study platoons generated by 'random effects' of cars are modeled, and this model is validated using a real data set acquired from the Jayu Expressway in Kyonggi Province, Korea.

An analysis of depression of the individuals with disabilities using repeated measurement data (반복 측정 자료를 이용한 장애인 우울에 대한 분석)

  • Hong, Haesun;Huh, Jib
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.5
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    • pp.1055-1067
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    • 2017
  • Most previous works to study for the depression of the disabilities in Korea have analyzed the repeated measured data of each individual under the mutually independent assumption. In this study, Korea Welfare Panel data of the disabilities surveyed additionally every three years are analyzed to detect the significant exploratory variables by the linear mixed models. A suitable correlation matrix is considered for the dependency of repeated measurement of each individual. The random effect to reflect the characteristics of the individuals as well as the fixed effect is included in the fitted linear mixed model. By the residual plot of the fixed effect model, the problem that the averages of residuals of each individual do not seem to be around zero is described. Further, the residual plot and the Q-Q plot coming from the selected final model are shown that the problem is modified well.