• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.787-796
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• 2009

A random coefficient model is applied when times of the repeated measurements are not fixed in experiments with respect to the subjects. The procedures of the inference of a random coefficient model are same as those of a mixed model. Diet experimental data was used for applying the random coefficient model. Various random coefficient models are investigated for the experimental data, and are compared each other. Finally, optimal random coefficient model would be selected. It resulted from the analysis that for the fixed effect factor, the baseline, treatment, height, and time effect were very significant. The treatment effect of the diet foods and exercises were more effective in losing weight than the effect of the diet foods only. The fixed cubic time effect was very significant. The variance components corresponding to the subject effect, linear time effect, quadratic time effect, and cubic time effect of the random coefficients are all positive. When quartic time effect was added as random coefficients the model did not converge. Thus random coefficients up to the cubic terms was considered as the optimal model.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.241-250
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• 2015

An important scientific objective of longitudinal studies involves tracking the probability of a subject having certain health condition over the course of the study. Proper definitions and estimates of disease risk tracking have important implications in the design and analysis of long-term biomedical studies and in developing guidelines for disease prevention and intervention. We study in this paper a class of rank-tracking probabilities to describe a subject's conditional probabilities of having certain health outcomes at two different time points. Linear mixed effects models are considered to estimate the tracking probabilities and their ratios of interest. We apply our methods to an epidemiological study of childhood cardiovascular risk factors.

• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.12
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• pp.1691-1698
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• 2000

An in situ digestion trial (Experiment 1) and a lactation trial (Experiment 2) were conducted to determine the effects of replacing corn and wheat bran with soyhulls (SH) in lactating dairy cow diets on the extent and kinetics of digestion of DM and NDF, and lactation performance. In experiment 1, five mixed feeds consisting of mixed concentrate and roughages (50:50 on a DM basis) were formulated on isonitrogenous and isoenergetic bases to produce five levels (0, 25, 50, 75 and 100%) of SH replacement for corn and wheat bran. SH had high in situ digestion (92 and 89% for potentially digestible DM and NDF) and fairly fast digestion rate (7.2 and 6.3 %/h for DM and NDF). Increasing level of SH replacement resulted in increased NDF digestibility (linear, p=0.001-0.04) and similar DM digestibility (beyond 12 h incubation, p=0.10-0.41). As level of SH replacement increased, percentage of slowly digestible fraction (b) of DM increased (linear, p=0.03), percentage of rapidly digestible fraction (a) of DM tended to decrease (linear, p=0.14), and DM digestion lag time tended to be longer (linear, p=0.13). Percentage of potentially digestible fraction (a+b) and digestion rate (c) of slowly digestible fraction of dietary DM remained unaltered (p=0.36-0.90) with increasing SH in the diet. Increasing level of SH for replacing corn and wheat bran in the diet resulted in increases in percentages of b (quadratic, p<0.001), a (linear, p=0.08), a+b (quadratic, p=0.001) and a tendency to increase in c for NDF (linear, p<0.19). It was also observed that there was a satisfactory fit of a non-linear regression model to NDF digestion data ($R^2=0.986-0.998$), but a relatively poor fit of the model to DM digestion data ($R^2=0.915-0.968$). In experiment 2, 42 lactating Holstein cows were used in a randomized complete block design. SH replaced corn and wheat bran in mixed concentrates at 0, 25, and 50%, respectively. These mixed concentrates were mixed with roughages and fed ad libitum as complete diets. Replacing corn and wheat bran with SH at 0, 25 and 50% levels did not influence (p=0.56-0.95) DM intakes (18.4, 18.6, and 18.5 kg/d), milk yields (27.7, 28.4 and 27.6 kg/d), 4% fat-corrected-milk (FCM) yields (26.2, 27.6, and 27.3 kg/d) and percentages of milk protein (3.12, 3.17 and 3.18%), milk lactose (4.69, 4.76 and 4.68%) and SNF (8.50, 8.64, and 8.54%). On the other hand, milk fat percentges linearly increased (3.63, 3.85 and 3.90% for SH replacement rates of 0, 25 and 50% in the diet, p=0.08), while feed costs per kg FCM production were reduced.

• Earthquakes and Structures
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• v.4 no.2
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• pp.203-217
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• 2013

In recent decades, it has been realized that increasing the lateral stiffness of structure subjected to lateral loads is not the only parameter enhancing safety or reducing damage. Factors such as ductility and damping govern the structural response due to lateral loads. Despite the significant contribution of damping in resisting lateral loads, especially at resonance, there is no accurate mathematical representation for it. The main objective of this study is to develop a damping identification procedure for linear systems based on a mixed numerical-experimental approach, assuming viscous damping. The proposed procedure has been applied to a laboratory experiment associated with a numerical model, where a hollow rectangular steel cantilever column, having three lumped masses, has been fixed on a shaking table subjected to different exciting waves. The modal damping ratio has been identified; in addition, the effect of adding filling material to the hollow specimen has been studied in relation to damping enhancement. The results have revealed that the numerically computed response based on the identified damping is in a good fitting with the measured response. Moreover, the filling material has a significant effect in increasing the modal damping.

• 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.21 no.1
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• pp.45-60
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• 2014

We introduce a MCMC sampling for a generalized linear normal random effects model with the ordered probit link function based on latent variables from suitable truncated normal distribution. Such models have proven useful in practice and we have observed numerically reasonable results in the estimation of fixed effects when the random effect term is provided. Applications that utilize Korean Welfare Panel Study data can be difficult to model; subsequently, we find that an ordered probit model with the random effects leads to an improved analyses with more accurate and precise inferences.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.11-24
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• 1999

In this paper we propose a method to test $\textit{H}$:$\rho_i$=$\gamma_i$ for 1$\leq$$\textit{i}$$\leq$$\ell$ against $\textit{K}$:$\rho_i$$\neq$$\gamma_i$ for some ｉin k-variance component random or mixed linear model where $\rho$i denotes the ratio of the i-th variance component to the error variance and $\ell$$\leq$K. The test which we call Rao-Wald test is exact and does not depend upon nuisance parameters. From a numerical study of the power performance of the test of the interaction effect for the case of a two-way random model Rao-Wald test was seen to be quite comparable to the locally best invariant (LBI) test when the nuisance parameters of the LBI test are assumed known. When the nuisance parameters of the LBI test are replaced by maximum likelihood estimators Rao-Wald test outperformed the LBI test.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.697-710
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• 2021

Relative error prediction is preferred over ordinary prediction methods when relative/percentile errors are regarded as important, especially in econometrics, software engineering and government official statistics. The relative error prediction techniques have been developed in linear/nonlinear regression, nonparametric regression using kernel regression smoother, and stationary time series models. However, random effect models have not been used in relative error prediction. The purpose of this article is to extend relative error prediction to some of generalized linear mixed model (GLMM) with panel data, which is the random effect models based on gamma, lognormal, or inverse gaussian distribution. For better understanding, the real auto insurance data is used to predict the claim size, and the best predictor and the best relative error predictor are comparatively illustrated.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1439-1447
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• 2014

Most of literatures on Korean child aggression are based on using the cross-sectional data sets. Although there is a related study with a longitudinal data set, it is assumed that the data sets measured repeatedly in the longitudinal data are mutually independent. A longitudinal data analysis for Korean child aggression is then necessary. This study is to analyze the effect of child development outcomes including academic achievement, self-esteem, depression anxiety, delinquency, victimization by peers, abuse by parents and internet using time on child aggression with Korea Welfare Panel Study data observed three times between 2006 and 2012. Since Korea Welfare Panel Study data have missing values, the missing at random is assumed. The linear mixed effect model and the restricted maximum likelihood estimation are considered.