• Journal of the Korean Statistical Society
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• v.26 no.3
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• pp.397-405
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• 1997

Recently maximum likelihood estimators using unconditional likelihood function are used for testing unit roots. When one wants to use this method the determinant term of initial values in the multivariate unconditional likelihood function produces a complicated function of the elements in the coefficient matrix and variance matrix. In this paper an approximation of the determinant term is calculated and based on this aproximation an approximated unconditional likelihood function is calculated. The approximated unconditional maximum likelihood estimators can be used to test for unit roots. When multivariate process has one unit root the limiting distribution obtained by this method and the limiting distribution using exact unconditional likelihood function are the same.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.167-178
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• 1994

To estimate coefficient matrix in autoregressive model, usually ordinary least squares estimator or unconditional maximum likelihood estimator is used. It is unknown that for univariate AR(p) model, unconditional maximum likelihood estimator gives better power property that ordinary least squares estimator in testing for unit root with mean estimated. When autoregressive model contains multiple unit roots and unconditional likelihood function is used to estimate coefficient matrix, the seperation of nonstationary part and stationary part of the eigen-values in the estimated coefficient matrix in the limit is developed. This asymptotic property may give an idea to test for multiple unit roots.

• Journal of the Korean Society of School Health
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• v.35 no.1
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• pp.31-39
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• 2022

Purpose: The purpose of the study was to examine the meditating effect of unconditional self acceptance on the relationship between life stress and depression in nursing students. Methods: Data was collected from a survey of 140 nursing students using self-reported questionnaires. The data was analyzed using IBM SPSS Statistic 25.0. The mediating effect of unconditional self-acceptance on the relationship between the subject's life stress and depression was analyzed using Baron and Kenny's method. In addition, the Sobel test was conducted to determine the significance of the mediating effect. Results: The regression model explained 43% of the variance in nursing students' depression. Significant factors were task-related life stress, unconditional self acceptance, and academic achievement. Unconditional self acceptance had a partial mediating effect on the relationship between nursing students' task-related life stress and depression. Conclusion: To prevent depression in nursing students, it is necessary to build effective strategies to manage task-related stress and improve unconditional self-acceptance.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1409-1419
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• 2013

We analyzed the data from KYPS using the latent growth model which has been widely studied as an analysis method of longitudinal data. In this study, we applied repeated measures ANOVA to unconditional model in order for faster decision of the unconditional model of the latent growth model. Also, we compared the six-type models, the quadratic model and the model of which repeated measures ANOVA is applied.

• Journal of Soil and Groundwater Environment
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• v.20 no.3
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• pp.51-64
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• 2015

In the present study, we develop two history matching techniques based on Markov chain Monte Carlo method where radial basis function and Gaussian distribution generated by unconditional geostatistical simulation are employed as the random walk transition kernels. The Bayesian inverse methods for aquifer characterization as the developed models can be effectively applied to the condition even when the targeted information such as hydraulic conductivity is absent and there are transient hydraulic head records due to imposed stress at observation wells. The model which uses unconditional simulation as random walk transition kernel has advantage in that spatial statistics can be directly associated with the predictions. The model using radial basis function network shares the same advantages as the model with unconditional simulation, yet the radial basis function network based the model does not require external geostatistical techniques. Also, by employing radial basis function as transition kernel, multi-scale nested structures can be rigorously addressed. In the validations of the developed models, the overall predictabilities of both models are sound by showing high correlation coefficient between the reference and the predicted. In terms of the model performance, the model with radial basis function network has higher error reduction rate and computational efficiency than with unconditional geostatistical simulation.

• Journal of Digital Convergence
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• v.16 no.6
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• pp.85-93
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• 2018

The Latent Growth Modeling(LGM) is known as the arising analysis method of longitudinal data and it could be classified into unconditional model and conditional model. Unconditional model requires estimated value of intercept and slope to complete a model of fitness. However, the existing LGM is in absence of a structured methodology to estimate slope when longitudinal data is neither simple linear function nor the pre-defined function. This study used Sequential Pattern of Association Rule Mining to calculate slope of unconditional model. The applied dataset is 'the Youth Panel 2001-2006' from Korea Employment Information Service. The proposed methodology was able to identify increasing fitness of the model comparing to the existing simple linear function and visualizing process of slope estimation.

• Management & Information Systems Review
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• v.24
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• pp.97-124
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• 2008

This study aimed to explore how the involvement of environment influenced eight subjects group. Thus, experiment was performed to clarify the role that the attitude of university student consumer plays in the communication process depending on the level of engagement of consumer in the environment and method to raise donation for preservation of environment. Analyzing as per the type of appeal, the mark in altruistic appeal type was higher in all variables than egoistic appeal type. Finally, checking the average mark of each variable as per the condition of donation, the value in unconditional donation was higher than in all variables than conditional donation. It was found 3 groups composed of 2 groups with high level of environmental engagement and 1 group with low level of environmental engagement were suitable to double mediation model among the 8 experimental groups. The group where double mediation model best corresponds than any other group was high level related to environment and the group that contacts altruistic appeal and the message in the form of conditional donation. It was also found that the group that has low level of environmental engagement and contacts egoistic appeal type and conditional donation shows the group that corresponds to double mediation model in the second place among the 8 groups. Finally, it was found that the group that has high level of environmental engagement and is stimulated by altruistic appeal and unconditional donation corresponds to double mediation model. Depending on the condition of message stimulation, unconditional donation is found to better correspond to double mediation model than conditional donation. However, opposite phenomena is observed when the level of environmental engagement is high and appeal type is egoistic. Namely, it was found that conditional donation better corresponds to double mediation model than unconditional donation.

• Journal of Convergence for Information Technology
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• v.8 no.3
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• pp.183-191
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• 2018

This study is to examine the effects of Socially Prescribed Perfectionism on depression by Intolerance of Uncertainty and Unconditional Self Acceptance, and to well being to improve the positive life of college students. This study is conducted on 238 college students who are influenced by Socially Prescribed Perfectionism, Intolerance of Uncertainty, Unconditional Self Acceptance, and Depression. This study analyzed a questionnaire consisted of a sub-component of the Multidimensional Perfectionism Scale (MPS), a Intolerance of Uncertainty Scale(IUS), an Unconditional Self Acceptance Questionnaire-R(USAQ-R), and a depression scale (CES-D) and verified correlation analysis and structural equation model. The results of this study showed that socially prescribed perfectionism had significant negative correlations with intolerance of uncertainty, and had significant positive correlation with unconditional self acceptance. The results of the structural equation model showed full mediating effect of the intolerance of uncertainty and unconditional self acceptance between Socially prescribed perfectionism and depression, Finally, implications and suggestions are suggested in this study.

• Journal of the Korean Mathematical Society
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• v.60 no.2
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• pp.273-302
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• 2023

In this paper, a fully discrete numerical scheme for the viscoelastic Oldroyd flow is considered with an introduced auxiliary variable. Our scheme is based on the finite element approximation for the spatial discretization and the backward Euler scheme for the time discretization. The integral term is discretized by the right trapezoidal rule. Firstly, we present the corresponding equivalent form of the considered model, and show the relationship between the origin problem and its equivalent system in finite element discretization. Secondly, unconditional stability and optimal error estimates of fully discrete numerical solutions in various norms are established. Finally, some numerical results are provided to confirm the established theoretical analysis and show the performances of the considered numerical scheme.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.217-225
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• 2019

The Latent Growth Modeling(LGM) is known as the typical analysis method of longitudinal data and it could be classified into unconditional model and conditional model. It is common to assume that the growth trajectory of unconditional model of LGM is linear. In the case of quasi-linear, the methodology for improving the model fitness using Sequential Pattern of Association Rule Mining is suggested. To do this, we divide longitudinal data into quintiles and extract periodic changes of the longitudinal data in each quintiles and make sequential pattern based on this periodic changes. To evaluate the effectiveness, the LGM module in SPSS AMOS was used and the dataset of the Youth Panel from 2001 to 2006 of Korea Employment Information Service. Our methodology was able to increase the fitness of the model compared to the simple linear growth trajectory.