• Korea-Australia Rheology Journal
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• v.21 no.2
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• pp.143-146
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• 2009

Breakthrough of high Weisenberg number problem is related with keeping the positive definiteness of the conformation tensor in numerical procedures. In this paper, we suggest a simple method to preserve the positive definiteness by use of vector decomposition of the conformation tensor which does not require eigenvalue problem. We also derive the constitutive equation of tensor-logarithmic transform in simpler way than that of Fattal and Kupferman and discuss the comparison between the vector decomposition and tensor-logarithmic transformation.

• Journal of Korean Academy of Nursing Administration
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• v.13 no.1
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• pp.65-73
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• 2007

Purpose: This study was carried out to find out the factors affecting on organizational commitment of hospital nurses. Method: The questionnaire-based research was done with 989 hospital nurses between January and March, 2004. Results: Variables such as job satisfaction, met expectations, compensation and welfare status of hospital, work involvement, positive affectivity had significant positive direct effect on organizational commitment in order of size, however, vertical conflict and job opportunity had negative direct effect in order of size. It was found that the following variables, listed in order of size, had significant total effects on organizational commitment: job satisfaction, met expectations, vertical conflict, compensation and welfare status of hospital, positive affectivity, work involvement, job opportunity, job autonomy, work definiteness, division justice, stability of employment, expectations before entering a hospital. Conclusion: It is recommended that programs for job satisfaction promotion, met expectations promotion, adequate compensation and welfare of hospital, work definiteness and work autonomy, solving conflict, positive affectivity promotion should be implemented to increase organizational commitment of hospital nurses.

• Health Policy and Management
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• v.11 no.4
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• pp.109-128
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• 2001

This study was conducted to investigate the factors affecting on organizational commitment of military hospital nursing officers. For the purpose of this study, the self-administerd questionnaire survey was done with 440 military hospital nursing officers during february, 2001. The major results of the study were summarized as follows: By path analysis using LISREL 7.0, variables such as met expectations, work involvement, pay, work definiteness, positive affectivity, family support, peer support, promotion opportunity, expectations before entering a military hospital had significant positive effect on Job satisfaction in order of size, however, vertical conflict and horizontal conflict had significant negative effect in order of size. Variables such as job satisfaction, met expectations, promotion opportunity, positive affectivity, pay had significant positive direct effect on organizational commitment in order of size, however, job routinization, job opportunity had significant negative direct effect in order of size. It was found that the following variables, listed m order of size, had significant total effects on organizational commitment: job satisfaction, met expectations, positive affectivity, promotion opportunity, pay, vertical conflict, job routinization, family support, work involvement, work definiteness, job opportunity. In considering above findings, the program or plan for job satisfaction promotion, met expectations, fair promotion opportunity, adequate pay, work definiteness, solving conflict, positive affectivity promotion would be implemented to increase organizational commitment of military hospital nursing officers.

• 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.

• Communications for Statistical Applications and Methods
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• v.27 no.2
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• pp.201-210
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• 2020

Baseline-category logit random effects models have been used to analyze longitudinal nominal data. The models account for subject-specific variations using random effects. However, the random effects covariance matrix in the models needs to explain subject-specific variations as well as serial correlations for nominal outcomes. In order to satisfy them, the covariance matrix must be heterogeneous and high-dimensional. However, it is difficult to estimate the random effects covariance matrix due to its high dimensionality and positive-definiteness. In this paper, we exploit the modified Cholesky decomposition to estimate the high-dimensional heterogeneous random effects covariance matrix. Bayesian methodology is proposed to estimate parameters of interest. The proposed methods are illustrated with real data from the McKinney Homeless Research Project.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1266-1269
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• 2008

We propose an integral sliding mode control for robot manipulators guaranteeing that sliding motion exists starting from an initial time. Also, we prove the asymptotic stability for robot manipulators using three important properties in the robot dynamics: skew-symmetry, positive-definiteness, and boundedness of robot parameter matrices. From illustrative examples, we show that the proposed method effectively controls for robot manipulators.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.168-172
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• 2009

We compare an integral sliding mode control with a typical sliding mode control for robot manipulators through two primitive tasks: set-point regulation and trajectory tracking control. To prove the asymptotic stability of two methods for robot manipulators, we introduce three important properties in the robot dynamics: skew-symmetry, positive-definiteness, and boundedness of robot parameter matrices and we present one unified control structure using a parametric velocity vector. From illustrative examples, we show that two methods effectively control for robot manipulators.

• Communications for Statistical Applications and Methods
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• v.21 no.2
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• pp.169-181
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• 2014

Marginalized random effects models (MREM) are commonly used to analyze longitudinal categorical data when the population-averaged effects is of interest. In these models, random effects are used to explain both subject and time variations. The estimation of the random effects covariance matrix is not simple in MREM because of the high dimension and the positive definiteness. A relatively simple structure for the correlation is assumed such as a homogeneous AR(1) structure; however, it is too strong of an assumption. In consequence, the estimates of the fixed effects can be biased. To avoid this problem, we introduce one approach to explain a heterogenous random effects covariance matrix using a modified Cholesky decomposition. The approach results in parameters that can be easily modeled without concern that the resulting estimator will not be positive definite. The interpretation of the parameters is sensible. We analyze metabolic syndrome data from a Korean Genomic Epidemiology Study using this method.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.927-936
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• 2017

To analyze longitudinal count data, Poisson linear mixed models are commonly used. In the models the random effects covariance matrix explains both within-subject variation and serial correlation of repeated count outcomes. When the random effects covariance matrix is assumed to be misspecified, the estimates of covariates effects can be biased. Therefore, we propose reasonable and flexible structures of the covariance matrix using autoregressive and moving average Cholesky decomposition (ARMACD). The ARMACD factors the covariance matrix into generalized autoregressive parameters (GARPs), generalized moving average parameters (GMAPs) and innovation variances (IVs). Positive IVs guarantee the positive-definiteness of the covariance matrix. In this paper, we use the ARMACD to model the random effects covariance matrix in Poisson loglinear mixed models. We analyze epileptic seizure data using our proposed 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.