• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.97-100
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• 2005

A new model-based clustering algorithm is proposed. The idea starts from the assumption that observations are realizations of Gaussian processes and so are correlated. With a special covariance structure, the posterior probability that an observation belongs to each cluster is computed using the ECM algorithm. A preliminary result of small-scale simulation study is given to compare with the k-means clustering algorithms.

• Nuclear Engineering and Technology
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• v.41 no.4
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• pp.381-390
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• 2009

This article provides selects of outstanding problems in computational neutron transport, with some suggested approaches thereto, as follows: i) ray effect in discrete ordinates method, ii) diffusion synthetic acceleration in strongly heterogeneous problems, iii) method of characteristics extension to three-dimensional geometry, iv) fission source and $k_{eff}$ convergence in Monte Carlo, v) depletion in Monte Carlo, vi) nuclear data evaluation, and vii) uncertainty estimation, including covariance data.

• Journal of Fashion Business
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• v.14 no.2
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• pp.166-178
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• 2010

The purposes of this study were to investigate the impacts of interactivity, site stickiness, and switching barrier on intention to use. For this purpose, the study tested covariance structural model which set relationships among independent variable(interactivity), meditated variables(site stickiness and switching barrier), and dependent variable(intention to use). The data were collected from a sample of 239 internet shopper of college female students. The covariance structural model and research hypothesis analyzed by using SPSS 16.0 and AMOS 5.0 program. The results are as follows: First, the structural model is accepted significantly. Second, interactivity had a positive influence on site stickiness, switching barrier, and intention to use. And it was found to have a indirect effect on intention to use through site stickiness and switching barrier. Third, site stickiness had a positive influence on switching barrier. Forth, site stickiness and switching barrier had a positive impact on intention to use.

• Wind and Structures
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• v.9 no.2
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• pp.159-178
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• 2006

For the slender and flexible cable supported bridges, identification of all the flutter derivatives for the vertical, lateral and torsional motions is essential for its stability investigation. In all, eighteen flutter derivatives may have to be considered, the identification of which using a three degree-of-freedom elastic suspension system has been a challenging task. In this paper, a system identification technique, known as covariance-driven stochastic subspace identification (COV-SSI) technique, has been utilized to extract the flutter derivatives for a typical bridge deck. This method identifies the stochastic state-space model from the covariances of the output-only (stochastic) data. All the eighteen flutter derivatives have been simultaneously extracted from the output response data obtained from wind tunnel test on a 3-DOF elastically suspended bridge deck section-model. Simplicity in model suspension and measurements of only output responses are additional motivating factors for adopting COV-SSI technique. The identified discrete values of flutter derivatives have been approximated by rational functions.

• Journal of Environmental Science International
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• v.22 no.3
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• pp.303-308
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• 2013

This paper proposed a new method for estimating missing values in time series rainfall data. The proposed method integrated the two most widely used estimation methods, general linear model(GLM) and ordinary kriging(OK), by taking a weighted average of covariance matrices derived from each of the two methods. The proposed method was cross-validated using daily rainfall data at thirteen rain gauges in the Hyeong-san River basin. The goodness-of-fit of the proposed method was higher than those of GLM and OK, which can be attributed to the weighting algorithm that was designed to minimize errors caused by violations of assumptions of the two existing methods. This result suggests that the proposed method is more accurate in missing values in time series rainfall data, especially in a region where the assumptions of existing methods are not met, i.e., rainfall varies by season and topography is heterogeneous.

• The Research Journal of the Costume Culture
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• v.16 no.6
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• pp.1126-1141
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• 2008

The purposes of this study identify how attributes of the website impact on consumer attitude toward the website. For this purpose, the study tested covariance structural model which set relationships among independent variables (interactivity and shopping value), meditated variables(relationship commitment), and dependent variables(attitude toward website). The data were collected from a sample of 239 internet shopper of college female students. The covariance structural model and research hypothesis analyzed by using SPSS 16.0 and AMOS 5.0 program. The results are as follows: First, the structural model is accepted significantly. Second, interactivity and shopping value of website have a positive influence on relationship commitment. Third, interactivity, shopping value of website, and relationship commitment have a positive impact on attitude toward the website. Forth, even if shopping value has not a positive influence on attitude toward the website, it was found to have a significant effect on attitude toward the website through the relationship commitment.

• Fashion & Textile Research Journal
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• v.12 no.1
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• pp.60-67
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• 2010

The purposes of this study were to investigate the impacts of shopping value, customer satisfaction, and switching barrier on consumer loyalty to website. For this purpose, the study tested covariance structural model which set relationships among independent variable(shopping value), meditated variables(customer satisfaction and switching barrier), and dependent variable(website loyalty). The data were collected from a sample of 239 internet shopper of college female students. The covariance structural model and research hypothesis analyzed by using SPSS 16.0 and AMOS 5.0 program. The results are as follows: First, the structural model is accepted significantly. Second, shopping value had a positive influence on customer satisfaction and switching barrier. Third, shopping value, shopping satisfaction of customer, and switching barrier had a positive impact on website loyalty. Forth, shopping value had a positive impact on website loyalty directly, it was found to have a indirect effect on website loyalty through customer satisfaction and switching barrier.

• Journal of Intelligence and Information Systems
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• v.1 no.1
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• pp.91-109
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• 1995

This paper compared four knowledge acquisition methods (namely, neural network, case-based reasoning, discriminant analysis, and covariance structure modeling) for allergic rhinitis. The data were collected from 444 patients with suspected allergic rhinitis who visited the Otorlaryngology Deduring 1991-1993. Among four knowledge acquisition methods, the discriminant model had the best overall diagnostic capability (78%) and the neural network had slightly lower rate(76%). This may be explained by the fact that neural network is essentially non-linear discriminant model. The discriminant model was also most accurate in predicting allergic rhinitis (88%). On the other hand, the CSM had the lowest overall accuracy rate (44%) perhaps due to smaller input data set. However, it was most accuate in predicting non-allergic rhinitis (82%).

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.9
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• pp.1472-1482
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• 2009

This study identifies how website attributes impact consumer loyalty to websites. For this purpose, the study tested covariance of a structural model which set relationships among independent variables (trust and site stickiness), meditated variables (relationship commitment), and dependent variables (website loyalty). The data were collected from a sample of 239 female college internet student shoppers. The covariance structural model and research hypothesis are analyzed using SPSS 16.0 and AMOS 5.0 program. The results are as follows: First, the structural model is significantly accepted. Second, trust and stickiness have a positive influence on the relationship commitment. Third, trust, stickiness, and relationship commitment have a positive impact on website loyalty. Forth, even if stickiness has no positive influence on website loyalty, it has a significant effect on website loyalty through the relationship commitment.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.55-64
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• 2024

In this study, we consider the sample size determination problem for clustered count data with many zeros. In general, zero-inflated Poisson and binomial models are commonly used for zero-inflated data; however, in real data the assumptions that should be satisfied when using each model might be violated. We calculate the required sample size based on a discrete Weibull regression model that can handle both underdispersed and overdispersed data types. We use the Monte Carlo simulation to compute the required sample size. With our proposed method, a unified model with a low failure risk can be used to cope with the dispersed data type and handle data with many zeros, which appear in groups or clusters sharing a common variation source. A simulation study shows that our proposed method provides accurate results, revealing that the sample size is affected by the distribution skewness, covariance structure of covariates, and amount of zeros. We apply our method to the pancreas disorder length of the stay data collected from Western Australia.