• Journal of the Korean Data and Information Science Society
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• 제27권3호
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• pp.815-825
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• 2016

Marginalized random effects models (MREMs) are often used to analyze longitudinal categorical data. The models permit direct estimation of marginal mean parameters and specify the serial correlation of longitudinal categorical data via the random effects. However, it is not easy to estimate the random effects covariance matrix in the MREMs because the matrix is high-dimensional and must be positive-definite. To solve these restrictions, we introduce two modeling approaches of the random effects covariance matrix: partial autocorrelation and the modified Cholesky decomposition. These proposed methods are illustrated with the real data from Korean genomic epidemiology study.

• 제어로봇시스템학회논문지
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• 제12권2호
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• pp.93-100
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• 2006

In this paper, a new-type Extended Kalman Filter (EKF) is proposed as a robust nonlinear filter for a stochastic nonlinear system. The original EKF is widely used for various nonlinear system applications. But it is fragile to its estimation errors because they give rise to linearization errors that affect the system mode1 as the modeling errors. The linearization errors are nonlinear functions of the estimation errors therefore it is very difficult to obtain the accurate error covariance of the EKF using the linear form. The inaccurately estimated error covariance hinders the EKF from being a sub-optimal estimator. The proposed filter tries to obtain the upper bound of the error covariance tolerating the uncertainty of the error covariance instead of trying to obtain the accurate one. It treats the linearization errors as uncertain modeling errors that can be handled by the robust linear filtering. In order to be more robust to the estimation errors than the original EKF, the proposed filter minimizes the upper bound like the robust linear filter that is applied to the linear model with uncertainty. The in-flight alignment problem of the inertial navigation system with GPS position measurements is a good example that the proposed robust filter is applicable to. The simulation results show the efficiency of the proposed filter in the robustness to initial estimation errors of the filter.

• 한국지리정보학회지
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• 제12권1호
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• pp.36-43
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• 2009

본 연구에서는 GPS 자료를 이용한 다중기선 처리 시 기선사이에 발생하는 공분산이 위치결정에 미치는 영향을 분석하였다. GPS를 이용한 위치결정방법 중 일반적으로 이용되고 있는 측위방법인 정지측위에 대해 공분산의 영향을 분석하였으며 이를 위해 공분산이 적절히 고려된 다중기선 처리결과를 얻은 후 단일기선 처리 결과, 즉 기선사이에 공분산이 고려되지 않은 결과와의 위치정확도 차이를 비교 분석하였다. 정지측위의 경우 정확도 차이는 거의 없었으나 다중기선 처리가 기선별 공분산을 고려했을 때 보다 안정적인 위치정확도를 확보할 수 있는 것으로 나타났다. 따라서 일반적인 측량의 경우는 기선간의 자료처리를 통해 위치결정을 하여도 충분한 요구정확도를 확보할 수 있는 것으로 판단되나 기선간의 공분산을 고려하면 좀 더 안정적이고 정확한 결과를 얻을 수 있는 것으로 사료된다.

• 대한전기학회논문지
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• 제38권3호
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• pp.237-246
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• 1989

A sliding memory covariance circular lattice (SMC-CL) filter and an efficient ARMA modeling method using the SMC-CL filter are presented. At first, SMC-CL filter is derived based on the geometric approach. Then ARMA process is converted into 2 channel AR process, and SMC-CL filter is applied to it. The structure of SMC-CL filter becomes simpler in case of ARMA modeling due to the whiteness of a driving input process. The parameters of ARMR process can be obtained by the Levinson recursions from the PARCOR coefficients of the second channel of the filter. Computer simulations are performed to show the effctiveness of the proposed algorithm.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2002년도 춘계학술대회 논문집
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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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• 제23권6호
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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.

• 보건행정학회지
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• 제11권2호
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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.

• 응용통계연구
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• 제30권1호
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• pp.103-117
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• 2017

경시적 자료분석에서 공변량 효과를 추정할 때 반복 측정된 결과들의 상관성은 고려되어야 한다. 따라서 공분산 행렬을 모형화하는 것은 매우 중요하다. 그러나 공분산 행렬의 추정은 모수들의 수가 많고 추정된 공분산행렬이 양정치성을 만족해야 하므로 쉽지 않은 문제이다. 이러한 제한을 극복하기 위해, 공분산행렬의 모형화를 위한 여러가지 방법을 제안하였다: 자기회귀/이동평균/자기회귀-이동평균 구조를 각각 적용한 수정 콜레스키분해 (Pourahmadi, 1999), 이동평균 콜레스키분해 (Zhang과 Leng, 2012)와 자기회귀-이동평균 콜레스키 분해 (Lee 등, 2017) 이들 구조를 가지는 공분산 행렬의 특징을 비교연구하고자 한다. 이 세 가지 모형의 성능을 비교하기 위한 모의실험을 실시한다.

• 지능정보연구
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• 제1권1호
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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%).

• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.19-30
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• 2003

Spatiotemporal statistical models are used for analyzing space-time data in many fields, such as environmental sciences, meteorology, geology, epidemiology, forestry, hydrology, fishery, and so on. It is well known that classical spatiotemporal process modeling requires the estimation of space-time variogram or covariance functions. In practice, the estimation of such variogram or covariance functions are computationally difficult and highly sensitive to data structures. We investigate a Bayesian hierarchical model which allows the specification of a more realistic series of conditional distributions instead of computationally difficult and less realistic joint covariance functions. The spatiotemporal model investigated in this study allows both spatial component and autoregressive temporal component. These two features overcome the inability of pure time series models to adequately predict changes in trends in individual sites.