• Journal of Korean Society of Forest Science
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• v.98 no.2
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• pp.178-182
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• 2009

A new mixed-effects model was developed that predicts individual-tree total height for Pinus densiflora trees in Gangwon province as a function of individual-tree diameter (cm). The mixed-effects model contains two random-effects parameters. Maximum likelihood estimation was used to fit the model to 560 height-diameter observations of individual trees measured throughout Gwangwon province in 2007 as part of the National Forest Inventory Program in Korea. The new model is an improvement over fixed-effects models because it can be calibrated to a local area, such as an inventory plot or individual stand. The new model also appears to be an improvement over the Forest Resources Evaluation and Prediction Program for the ten calibration trees used in this study. An example is provided that describes how to estimate the random-effects parameters using ten calibration trees.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1169-1178
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• 2010

This article consider a performance model selection based on AIC under unbalanced deign in linear mixed effect models. Vaida and Balanchard (2005) proposed conditional AIC for model selection in linear mixed effect models when the prediction of random effects is of primary interest. Theoretical properties of cAIC and related criteria have been investigated by Liang et al. (2008) and Greven and Kneib (2010). However, all of the simulation studies were performed under a balanced design. Even though functional form of AIC remain same even under the unbalanced deign, it is worthwhile to investigate performance of AIC based model selection criteria under the unbalanced design. The simulation study in this article shows how unbalancedness affects model selection in linear mixed effect models.

• Journal of the Korean Operations Research and Management Science Society
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• v.11 no.2
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• pp.51-68
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• 1986

The effects of goal setting in the context of Multiple Criteria Making (MCDM) are classified into two types : internal and external. In the internal models, the impact of the changed goal is limited only to the other goals in MCDM model. By contrast, in the external models, the impact is limited to the factors not included in the MCDM model. In fact, most real world examples of goal setting have the nature of mixed models. To assist in the goal setting process, the framework named Goal Setting Support (GSS) is developed. The GSS helps decision-makers for mixed models to 1) make internal trade-offs in a way that guarantees non-dominancy after the trade-ofs, and 2) evaluate achieved goals systematically. The GSS can be used in creating Decision Support Systems that will allow interactive goal setting.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.1037-1047
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• 2012

The generalized linear mixed model(GLMM) is widely used in fitting categorical responses of clustered data. In the numerical approximation of likelihood function the normality is assumed for the random effects distribution; subsequently, the commercial statistical packages also routinely fit GLMM under this normality assumption. We may also encounter departures from the distributional assumption on the response variable. It would be interesting to investigate the impact on the estimates of parameters under misspecification of distributions; however, there has been limited researche on these topics. We study the sensitivity or robustness of the maximum likelihood estimators(MLEs) of GLMM for counts data when the true underlying distribution is normal, gamma, exponential, and a mixture of two normal distributions. We also consider the effects on the MLEs when we fit Poisson-normal GLMM whereas the outcomes are generated from the negative binomial distribution with overdispersion. Through a small scale Monte Carlo study we check the empirical coverage probabilities of parameters and biases of MLEs of GLMM.

• Genomics & Informatics
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• v.20 no.1
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• pp.8.1-8.14
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• 2022

Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/ environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings.

• Geomechanics and Engineering
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• v.29 no.5
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• pp.567-582
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• 2022

In the present study, a series of physical experiments and numerical simulations were conducted to investigate the effects of mode I and mixed-mode I/II cracks on the fracture modes and stability of roadway tunnel models. The experiments and simulations incorporated different inclination angle flaws under both static and dynamic loads. The quasi-static and dynamic testing were conducted by using an electro-hydraulic servo control device and drop weight impact system (DWIS), and the failure process was simulated by using rock failure process analysis (RFPA) and AUTODYN software. The stress intensity factor was also calculated to evaluate the stability of the flawed roadway tunnel models by using ABAQUS software. According to comparisons between the test and numerical results, it is observed that for flawed roadways with a single radical crack and inclination angle of 45°, the static and dynamic stability are the lowest relative to other angles of fractured rock masses. For mixed-mode I/II cracks in flawed roadway tunnel models under dynamic loading, a wing crack is produced and the pre-existing cracks increase the stress concentration factor in the right part of the specimen, but this factor will not be larger than the maximum principal stress region in the roadway tunnel models. Additionally, damage to the sidewalls will be involved in the flawed roadway tunnel models under static loads.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.759-771
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• 2005

Twin studies are one of the most widely used methods for quantifying the influence of genetic and environmental factors on some traits such as a life span or a disease. In this paper we propose a genetic mixed linear model for twin survival time data, which allows us to separate the genetic component from the environmental component. Inferences are based upon the hierarchical likelihood (h-likelihood), which provides a statistically efficient and simple unified framework for various random-effect models. We also propose a simple and fast computation method for analyzing a large data set on twin survival study. The new method is illustrated to the survival data in Swedish Twin Registry. A simulation study is carried out to evaluate the performance.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.11-18
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• 2003

Quite often normality assumptions are not satisfied in practical applications. In this paper, an estimated generalized least squares(EGLS) analysis are considered in two way mixed linear models with arbitrary types of distributions for random effects. We investigate the power performance of EGLS analysis based on Henderson's method III, ML, REML and MINQUE(1). The power performances depend on the imbalance of design, on the actual values of ratio of variance components, and on the skewness and kurtosis parameters of the underlying distributions slightly. Results of our limited simulation study suggest that the EGLS F-statistics using four estimators and arbitrary distributions produce similar type I error rates and power performance.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.201-210
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• 2015

Repeated measurements on each variables of interest often arise in bioscience or medical research. We need to account for correlations among repeated measurements to assess the correlation between two variables in the presence of replication. This paper reviews methods to estimate a correlation coefficient between two variables in repeated measurements using the variance-covariance matrix of linear mixed effect models. We analyze acoustic radiation force impulse imaging (ARFI) data to assess correlation between three shear wave velocity (SWV) measurements in liver or spleen and spleen length by ultrasonography. We present how to obtain parameter estimates for the variance-covariance matrix and correlations in mixed effects models using PROC MIXED in SAS.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.199-205
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• 1999

This paper discusses the generalized mixed-effects model for the analysis of overdispersed binomial data. Sometimes certain types of sampling designs or genetic characters of experimental units can be regarded as factors of extra binomial variation. For such cases, this paper suggests models with one or two random effects to explain overdispersion caused by those affecting factors and shows how to test for a model adequacy based on deviance.