• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.313-323
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• 2003

For the growth curve model with arbitrary covariance structure, known as unstructured covariance matrix, the problems of detecting outliers are discussed in this paper. In order to detect outliers in the growth curve model, the test statistics using U-distribution is established. After detecting outliers in growth curve model, we test homo and/or hetero-geneous covariance matrices using PSR Quasi-Bayes Criterion. For illustration, one numerical example is discussed, which compares between before and after outlier deleting.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.54.1-54
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• 2001

In this paper, an alternate method for state-covariance assignment for SISO(single input singe output) linear systems is proposed. This method is based on the inverse solution of the Lyapunov matrix equation and the resulting formulas are similar in structure to the formulas for pole placement. Further, the set of all assignable covariance matrices to a SISO linear system is also characterized.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.485-501
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• 2012

This research is to find a design D that minimizes forecast variance in d dimensions over the candidate space ${\chi}$. We want a robust design since we may not know the specific covariance structure. We seek a design that minimizes a coverage criterion and hope that this design will provide a small forecast variance even if the covariance structure is unobservable. The details of an exchange or swapping algorithm and several properties of the parameters of coverage criterion with the unknown correlation structures are discussed.

• Health Policy and Management
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• v.11 no.2
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• pp.123-140
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• 2001

Hypotheses In health services research are becoming increasingly more complex and specific. As a result, health services research studies often include multiple independent, intervening, and dependent variables in a single hypothesis. Nevertheless, the statistical models adopted by health services researchers have failed to keep pace with the increasing complexity and specificity of hypotheses and research designs. This article introduces a statistical model well suited for complex and specific hypotheses tests in health services research studies. The covariance structure modeling(CSM) methodology is especially applied to regional relevance indices(RIs) to assess the impact of health resources and healthcare utilization. Data on secondary statistics and health insurance claims were collected by each catchment area. The model for RI was justified by direct and indirect effects of three latent variables measured by seven observed variables, using ten structural equations. The resulting structural model revealed significant direct effects of the structure of health resources but indirect effects of the quantity on RIs, and explained 82% of correlation matrix of measurement variables. Two variables, the number of beds and the portion of specialists among medical doctors, became to have significant effects on RIs by being analyzed using the CSM methodology, while they were insignificant in the regression model. Recommendations for the CSM methodology on health service research data are provided.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.443-445
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• 1998

In this paper, an alternate method for state-covariance assignment for SISO(single input single output) linear systems is proposed. This method is based on the inverse solution of the Lyapunov matrix equation and the resulting formulas are similar in structure to the formulas for pole placement. Further, the set of all assignable covariance matrices to a SISO linear system is also characterized.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.7
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• pp.1534-1542
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• 1997

In this paper, we present a performance improvement method for the direction-of-arrival (DOA) estimation algorithm of the narrowband signals incident on a uniform circular array. It is very important to estimate the covariance matrix effectively because the performance of DOA algorithm mainly depends on the exactness of the sampel coveriance matrix which is computed from the received samples of signals. In case of uniform circular array with the even number sensors, the structure of the arrray has a useful geometrical property. Therefore we present the method which can estimate covariance matrix more effectively using this property. The simulation results are shown to demonstrate the superior perfodrmance obtained by the proposed covariance matrix estimation method relative to that of the conventional estimation method.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.575-585
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• 2016

Longitudinal studies repeatedly measure outcomes over time. Therefore, repeated measurements are serially correlated from same subject (within-subject variation) and there is also variation between subjects (between-subject variation). The serial correlation and the between-subject variation must be taken into account to make proper inference on covariate effects (Diggle et al., 2002). However, estimation of the covariance matrix is challenging because of many parameters and positive definiteness of the matrix. To overcome these limitations, we propose autoregressive moving average Cholesky decomposition (ARMACD) for the linear mixed models. The ARMACD allows a class of flexible, nonstationary, and heteroscedastic models that exploits the structure allowed by combining the AR and MA modeling of the random effects covariance matrix. We analyze a real dataset to illustrate our proposed methods.

• Smart Structures and Systems
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• v.2 no.2
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• pp.127-140
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• 2006

In this study, a wavelet-based covariance-driven system identification technique is proposed for damage assessment of structures under ambient excitation. Assuming the ambient excitation to be a white-noise process, the covariance computation is shown to be able to separate the effect of random excitation from the response measurement. Wavelet transform (WT) is then used to convert the covariance response in the time domain to the WT magnitude plot in the time-scale plane. The wavelet coefficients along the curves where energy concentrated are extracted and used to estimate the modal properties of the structure. These modal property estimations lead to the calculation of the stiffness matrix when either the spectral density of the random loading or the mass matrix is given. The predicted stiffness matrix hence provides a direct assessment on the possible location and severity of damage which results in stiffness alteration. To demonstrate the proposed wavelet-based damage assessment technique, a numerical example on a 3 degree-of-freedom (DOF) system and an experimental study on a three-story building model, which are all under a broad-band excitation, are presented. Both numerical and experimental results illustrate that the proposed technique can provide an accurate assessment on the damage location. It is however noted that the assessment of damage severity is not as accurate, which might be due to the errors associated with the mode shape estimations as well as the assumption of proportional damping adopted in the formulation.

• Korean Management Science Review
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• v.11 no.2
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• pp.29-41
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• 1994

This paper analyses the stochastic nature of the Permanent Income Hypothesis (PIH) by specifying the variance-covariance structure of PIH based on Hall and Mishkin[3]. Maximum likelihood is employed to estimate the model by explicitely incorporating the heteroscedastic nature of the data into the likelihood. The data used are individual Korean household consumption and income data. The results indicate that the data are generally consistent with the Permanent Income Hypothesis, and about 11 percent of the total variation in consumption may be attributable to the excess sensitivity of consumption to income.

• 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.