• 디지털융복합연구
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• 제13권10호
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• pp.121-133
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• 2015

본 튜토리얼은 R을 이용하여 공분산 기반의 구조방정식모델링을 수행하는 방법을 제시하고 있다. 이를 위해 본 튜토리얼에서는 기존 연구들에 대한 리뷰를 통해 공분산 기반의 구조방정식모델링을 위한 기준들을 정의하고, 하나의 예시 연구모형을 제시하여 공분산 기반의 구조방정식모델링을 지원하는 R 패키지인 "lavaan"을 이용하여 이 예시 모형을 분석하는 것을 보여준다. 결과물로 본 튜토리얼에서는 예시모형을 대상으로 한 R을 이용한 공분산 기반의 구조방정식모델링 기법과 실습 스크립트가 제시되었다. 본 튜토리얼은 공분산 기반의 구조방정식모델링을 처음 접하는 연구자들에게는 연구모형을 구조방정식 모델링으로 분석하는데 유용한 가이드가 될 것이며, 이미 공분산 기반의 구조방정식모델링에 익숙한 연구자들에게는 R을 이용한 새로운 공분산 기반의 구조방정식모델링 분석기법 제시를 통하여 R이라는 통합된 통계 소프트웨어 운영환경에서 심도 있는 연구를 위한 기반 지식을 제공할 것이다.

• 센서학회지
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• 제29권6호
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• pp.440-446
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• 2020

A well-known difficulty in attitude estimation based on inertial measurement unit (IMU) signals is the occurrence of external acceleration under dynamic motion conditions, as the acceleration significantly degrades the estimation accuracy. Lee et al. (2012) designed a Kalman filter (KF) that could effectively deal with the acceleration issue. Ahmed and Tahir (2017) modified this method by adjusting the acceleration-related covariance matrix because they considered covariance modeling as a pivotal factor in the estimation accuracy. This study investigates the effects of covariance modeling on estimation accuracy in an IMU-based attitude estimation KF. The method proposed by Ahmed and Tahir can be divided into two: one uses the covariance including only diagonal components and the other uses the covariance including both diagonal and off-diagonal components. This paper compares these three methods with respect to the motion condition and the window size, which is required for the methods by Ahmed and Tahir. Experimental results showed that the method proposed by Lee et al. performed the best among the three methods under relatively slow motion conditions, whereas the modified method using the diagonal covariance with a high window size performed the best under relatively fast motion conditions.

• Communications for Statistical Applications and Methods
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• 제20권3호
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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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• 제25권1호
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• pp.61-70
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• 2018

Modeling of the random effects covariance matrix in generalized linear mixed models (GLMMs) is an issue in analysis of longitudinal categorical data because the covariance matrix can be high-dimensional and its estimate must satisfy positive-definiteness. To satisfy these constraints, we consider the autoregressive and moving average Cholesky decomposition (ARMACD) to model the covariance matrix. The ARMACD creates a more flexible decomposition of the covariance matrix that provides generalized autoregressive parameters, generalized moving average parameters, and innovation variances. In this paper, we analyze longitudinal count data with overdispersion using GLMMs. We propose negative binomial loglinear mixed models to analyze longitudinal count data and we also present modeling of the random effects covariance matrix using the ARMACD. Epilepsy data are analyzed using our proposed model.

• 한국조사연구학회:학술대회논문집
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• 한국조사연구학회 2007년도 추계학술대회 발표논문집
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• pp.55-65
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• 2007

연구에서는 공분산구조분석을 실시하기 위해 주로 사용하고 있는 AMOS 소프트웨어에 대하여 알아보려고 한다. 응용소프트웨어에서 활용된 수학적인 모형을 알지 못하면, 구조방정식에 대한 충분한 이해를 할 수 없으며, 구조방정식에 대한 8가지 가정이 어떻게 구현되는지 알 수 가 없다. 따라서 본 연구에서는 구조방정식모형 연구에서 주로 활용되었던 LISREL 프로그램으로 RAM과 MOSAN을 구현하였고 AMOS 결과와 비교분석하였다. 연구 결과에 따르면 AMOS프로그램은 8가지의 모든 가정을 따르지 않는 것으로 나타났다. AMOS 프로그램이 MOSAN보다는 RAM으로 구현되고 있음을 본 연구를 통해 알 수 있다. AMOS 소프트웨어 프로그램은 잠재변수(F)와 측정오차(e)간의 상관값을 산출할 때 충분히 추정을 하지 못하고 부분적으로만 추정하여 값을 산출하고 있음을 알 수 있었다.

• Communications for Statistical Applications and Methods
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• 제24권1호
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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.

• 응용통계연구
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• 제33권3호
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• pp.281-296
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• 2020

같은 개체로부터 반복 측정한 자료를 경시적 자료(longitudinal data)라고 한다. 이러한 자료를 분석하려면 흔히 사용되는 횡단 자료 분석과는 다른 분석 방법이 필요하다. 즉, 경시적 자료에서 공변량의 효과를 추정할 때에는 반복 측정된 결과 간의 상관성을 고려해야 하며, 따라서 공분산행렬을 모형화 하는 것이 매우 중요하다. 그러나 추정해야 할 모수가 많고, 추정된 공분산행렬이 양정치성을 만족해야 하므로 공분산 행렬의 모형화는 쉽지 않다. 특히 다변량 경시적 자료분석을 위한 공분산행렬의 모형화는 더욱더 심층적인 방법론을 사용해야 한다. 본 논문은 다변량 경시적 자료분석을 위한 공분산행렬을 모형화하기 위해 두 가지 방법론을 고찰한다. 두 방법 모두 수정된 콜레스키 분해(modified Cholesky decomposition)를 이용하여 시간에 따른 응답변수들의 상관관계를 설명하고 있다. 하지만 같은 시간에서 관측된 응답변수들간의 상관관계를 설명하는 방법이 다르다. 첫 번째 방법론에서는 향상된 선형 공분산 모형(enhanced linear covariance models)을 사용하여 공분산행렬이 양정치성을 만족하도록 한다. 두 번째 방법론에서는 분산-공분산 분해(variance-correlation decomposition)와 초구분해(hypersphere decomposition)을 이용하여 공분산 행렬을 모형화 한다. 이 두 방법론의 성능을 비교하고자 모의실험을 진행한다.

• Ocean and Polar Research
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• 제30권2호
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• pp.129-140
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• 2008

As a preliminary effort to establish a data assimilative ocean forecasting system, we reviewed the theory of the Ensemble Kamlan Filter (EnKF) and developed practical techniques to apply the EnKF algorithm in a real ocean circulation modeling system. To verify the performance of the developed EnKF algorithm, a wind-driven double gyre was established in a rectangular ocean using the Regional Ocean Modeling System (ROMS) and the EnKF algorithm was implemented. In the ideal ocean, sea surface temperature and sea surface height were assimilated. The results showed that the multivariate background error covariance is useful in the EnKF system. We also tested the sensitivity of the EnKF algorithm to the localization and inflation of the background error covariance and the number of ensemble members. In the sensitivity tests, the ensemble spread as well as the root-mean square (RMS) error of the ensemble mean was assessed. The EnKF produces the optimal solution as the ensemble spread approaches the RMS error of the ensemble mean because the ensembles are well distributed so that they may include the true state. The localization and inflation of the background error covariance increased the ensemble spread while building up well-distributed ensembles. Without the localization of the background error covariance, the ensemble spread tended to decrease continuously over time. In addition, the ensemble spread is proportional to the number of ensemble members. However, it is difficult to increase the ensemble members because of the computational cost.

• International Journal of Aeronautical and Space Sciences
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• 제3권1호
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• pp.19-29
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• 2002

Decentralized filtering for a formation flight instrumentation system by INS/GPS integration is considered in this paper. An elaborate tuning method of the measurement noise covariance is suggested to compensate modeling errors caused by decentralizing the extended Kalman filter. It does not require large data transfer between formation vehicles. Covariance analysis exhibits the superior performance of the proposed approach when compared with the existent decentralized filter and the global filter, which has the target-filter performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.64-69
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• 1993

In this paper we present the performance bounds of the optimal FIR filter in continuous time systems with modeling uncertainty. The performance measure bounds are calculated from the estimation error covariance bounds of the optimal FIR filter and the suboptimal FIR filter. Performance error bounds range are expressed by the upper bounds on the estimation error covariance difference between the real and nominal values in case of the systems with noise uncertainty or model uncertainty. The performance bounds of the systems are derived on the assumption that the system uncertainty and the estimation error covariance are imperfectly known a priori. The estimation error bounds of the optimal FIR filter is compared with those of the Kalman filter via a numerical example applied to the estimation of the motion of an aircraft carrier at sea, which shows the former has better performances than the latter.