• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.413-420
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• 2008

This paper suggests a marginal logit mixed-effects for analyzing repeated binary response data. Since binary repeated measures are obtained over time from each subject, observations will have a certain covariance structure among them. As a plausible covariance structure, 1st order auto-regressive correlation structure is assumed for analyzing data. Generalized estimating equations(GEE) method is used for estimating fixed effects in the model.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.257-260
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• 2002

The impressions for odors are subjective and have individual differences. In this study, the Impressions of odors were investigated by covariance structure analysis. 46 subjects (men in their twenty) recorded their reactions to ten odorants by grading them on a seven-point scale in terms of twelve adjective pairs. Their reactions were quantified by using factor analysis and covariance structure analysis. The factors were extracted as "preference", "arousal" and "persistency". The subjects were classified into three groups according to the most suitable causal models (structural equation models). Each group had different causal relationship and different impression structure for odors. It was suggested that there is a possibility to evaluate the subjective impression of odor using covariance structure analysis.

• Communications for Statistical Applications and Methods
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• v.20 no.3
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• pp.235-240
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• 2013

Generalized linear mixed models(GLMMs) are frequently used for the analysis of longitudinal categorical data when the subject-specific effects is of interest. In GLMMs, the structure of the random effects covariance matrix is important for the estimation of fixed effects and to explain subject and time variations. The estimation of the matrix is not simple because of the high dimension and the positive definiteness; subsequently, we practically use the simple structure of the covariance matrix such as AR(1). However, this strong assumption can result in biased estimates of the fixed effects. In this paper, we introduce Bayesian modeling approaches for the random effects covariance matrix using a modified Cholesky decomposition. The modified Cholesky decomposition approach has been used to explain a heterogenous random effects covariance matrix and the subsequent estimated covariance matrix will be positive definite. We analyze metabolic syndrome data from a Korean Genomic Epidemiology Study using these methods.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1265-1275
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• 2009

In this paper, we investigated the composite covariance structure models for repeated measures data with multiple repeat factors. When the number of repeat factors is more than three, it is infeasible to fit the composite covariance models using the existing statistical packages. In order to fit the composite covariance structure models to real data, we proposed two approaches: the dimension reduction approach for repeat factors and the random effect model approximation approach. Our proposed approaches were illustrated by using the blood pressure data with three repeat factors obtained from 883 subjects.

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.1-9
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• 2010

This paper suggests a mixed-effects model for analyzing split-plot data when there is a repeated measures factor that affects on the response variable. Covariance structures are discussed among the observations because of the assumption of a repeated measures factor as one of explanatory variables. As a plausible covariance structure, compound symmetric covariance structure is assumed for analyzing data. The restricted maximum likelihood (REML)method is used for estimating fixed effects in the model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.117-120
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• 1996

The objective of this study is to suggest modified Covariance Structure Analysis that combine with existing Multivariate Statistical Method which is used Casual Analysis Method in Management Information. For this purpose, we'll consider special feature and limitation about Correlation Analysis, Regression Analysis, Path Analysis and connect Covariance Structure Analysis with Statistical Factor Analysis so that theoretical casual model compare with variables structure in collecting data. A example is also presented to show the practical applicability of this approach.

• Korean Journal of Applied Biomechanics
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• v.28 no.2
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• pp.127-134
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• 2018

Objective: In a statistical linear model estimating the center of rotation of a human hip joint, which is the parameter related to the mean of response vectors, assumptions of homoscedasticity and independence of position vectors measured repeatedly over time in the model result in an inefficient parameter. We, therefore, should take into account the variance-covariance structure of longitudinal responses. The purpose of this study was to estimate the efficient center of rotation vector of the hip joint by using covariance pattern models. Method: The covariance pattern models are used to model various kinds of covariance matrices of error vectors to take into account longitudinal data. The data acquired from functional motions to estimate hip joint center were applied to the models. Results: The results showed that the data were better fitted using various covariance pattern models than the general linear model assuming homoscedasticity and independence. Conclusion: The estimated joint centers of the covariance pattern models showed slight differences from those of the general linear model. The estimated standard errors of the joint center for covariance pattern models showed a large difference with those of the general linear model.

• Proceedings of the Korean Association for Survey Research Conference
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• 2007.11a
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• pp.55-65
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• 2007

In this research, it will be examined on mathematical model of AMOS software program that ues for Covariance Structure Analysis. if we have not understood to mathematical model of Covariance Structure, we fail to understand Structural equation modeling. Similarly If We were not understand to mathematical model of AMOS Software, we do not use Software adequately. Therefore we examine two sorts of Software that be designed for Structural equation modeling or Covariance Structure Analysis. In this research, We will focus on 8 assumption of Structural equation modeling and compare AMOS(Analysis of MOment Structure) program with LISREL(Linear Structure RELation) program. We found that A Program of AMOS Software have materialized with RAM(Reticular Action Model).

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.81-96
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• 2017

Cumulative logit random effects models are typically used to analyze longitudinal ordinal data. The random effects covariance matrix is used in the models to demonstrate both subject-specific and time variations. The covariance matrix may also be homogeneous; however, the structure of the covariance matrix is assumed to be homoscedastic and restricted because the matrix is high-dimensional and should be positive definite. To satisfy these restrictions two Cholesky decomposition methods were proposed in linear (mixed) models for the random effects precision matrix and the random effects covariance matrix, respectively: modified Cholesky and moving average Cholesky decompositions. In this paper, we use these two methods to model the random effects precision matrix and the random effects covariance matrix in cumulative logit random effects models for longitudinal ordinal data. The methods are illustrated by a lung cancer data set.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.181-188
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• 2009

This paper discusses the covariance structures of data collected from an experiment with a nested design structure, where a smaller experimental unit is nested within a larger one. Due to the nonrandomization of repeated measures factors to the nested experimental units, compound symmetry covariance structure is assumed for the analysis of data. Treatments are given as the combinations of the levels of random factors and fixed factors. So, a mixed-effects model is suggested under compound symmetry structure. An example is presented to illustrate the nesting in the experimental units and to show how to get the parameter estimates in the fitted model.