• Title/Summary/Keyword: random effect

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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.

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.

An Approach for the Uncertainty Evaluation of the Overall Result from Replications of Measurement: Separately Combining Individual Uncertainty Components According to their 'systematic' and 'random' Effects

  • Kim, In Jung;Kim, Byungjoo;Hwang, Euijin
    • Bulletin of the Korean Chemical Society
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    • v.35 no.4
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    • pp.1057-1060
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    • 2014
  • In our previous articles, an approach has been proposed for the evaluation of the uncertainty of overall result from multiple measurements. In the approach, uncertainty sources were classified into two groups: the first including those giving same 'systematic' effect on each individual measurement and the second including the others giving 'random' effect on each individual measurement and causing a variation among individual measurement results. The arithmetic mean of the replicated measurements is usually assigned as the value for the overall result. Uncertainty of the overall result is determined by separately evaluating and combining an overall uncertainty from sources of the 'systematic' effect and another overall uncertainty from sources of the 'random' effect. This conceptual approach has been widely adopted in chemical metrology society. In this study, further logical proof with more detailed mathematical expressions is provided on the approach.

Joint Modeling of Death Times and Counts Using a Random Effects Model

  • Park, Hee-Chang;Klein, John P.
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.4
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    • pp.1017-1026
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    • 2005
  • We consider the problem of modeling count data where the observation period is determined by the survival time of the individual under study. We assume random effects or frailty model to allow for a possible association between the death times and the counts. We assume that, given a random effect, the death times follow a Weibull distribution with a rate that depends on some covariates. For the counts, given the random effect, a Poisson process is assumed with the intensity depending on time and the covariates. A gamma model is assumed for the random effect. Maximum likelihood estimators of the model parameters are obtained. The model is applied to data set of patients with breast cancer who received a bone marrow transplant. A model for the time to death and the number of supportive transfusions a patient received is constructed and consequences of the model are examined.

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EFFICIENT ESTIMATION IN SEMIPARAMETRIC RANDOM EFFECT PANEL DATA MODELS WITH AR(p) ERRORS

  • Lee, Young-Kyung
    • Journal of the Korean Statistical Society
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    • v.36 no.4
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    • pp.523-542
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    • 2007
  • In this paper we consider semiparametric random effect panel models that contain AR(p) disturbances. We derive the efficient score function and the information bound for estimating the slope parameters. We make minimal assumptions on the distribution of the random errors, effects, and the regressors, and provide semiparametric efficient estimates of the slope parameters. The present paper extends the previous work of Park et al.(2003) where AR(1) errors were considered.

Bayesian Test for the Intraclass Correlation Coefficient in the One-Way Random Effect Model

  • Kang, Sang-Gil;Lee, Hee-Choon
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.3
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    • pp.645-654
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    • 2004
  • In this paper, we develop the Bayesian test procedure for the intraclass correlation coefficient in the unbalanced one-way random effect model based on the reference priors. That is, the objective is to compare two nested model such as the independent and intraclass models using the factional Bayes factor. Thus the model comparison problem in this case amounts to testing the hypotheses $H_1:\rho=0$ versus $H_2:{\rho}{\neq}0$. Some real data examples are provided.

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On Second Order Probability Matching Criterion in the One-Way Random Effect Model

  • Kim, Dal Ho;Kang, Sang Gil;Lee, Woo Dong
    • Communications for Statistical Applications and Methods
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    • v.8 no.1
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    • pp.29-37
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    • 2001
  • In this paper, we consider the second order probability matching criterion for the ratio of the variance components under the one-way random effect model. It turns out that among all of the reference priors given in Ye(1994), the only one reference prior satisfies the second order matching criterion. Similar results are also obtained for the intraclass correlation as well.

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Bootstrap Confidence Bounds for P(X>Y) in 1-Way Random Effect Model with Equal Variances

  • Kim, Dal Ho;Cho, Jang Sik
    • Journal of Korean Society for Quality Management
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    • v.24 no.1
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    • pp.87-95
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    • 1996
  • We construct bootstrap confidence bounds for reliability, R=P(X>Y), where X and Y are independent normal random variables. 1-way random effect models with equal variances are assumed for the populations of X and Y. We compare the accuracy of the proposed bootstrap confidence bounds and classical confidence bound for small samples via Monte Carlo simulation.

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