• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.211-219
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• 2015

Generalized linear mixed models are used to analyze longitudinal categorical data. Random effects specify the serial dependence of repeated outcomes in these models; however, the estimation of a random effects covariance matrix is challenging because of many parameters in the matrix and the estimated covariance matrix should satisfy positive definiteness. Several approaches to model the random effects covariance matrix are proposed to overcome these restrictions: modified Cholesky decomposition, moving average Cholesky decomposition, and partial autocorrelation approaches. We review several approaches and present potential future work.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.281-296
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• 2020

Repeated outcomes from the same subjects are referred to as longitudinal data. Analysis of the data requires different methods unlike cross-sectional data analysis. It is important to model the covariance matrix because the correlation between the repeated outcomes must be considered when estimating the effects of covariates on the mean response. However, the modeling of the covariance matrix is tricky because there are many parameters to be estimated, and the estimated covariance matrix should be positive definite. In this paper, we consider analysis of multivariate longitudinal data via two modeling methodologies for the covariance matrix for multivariate longitudinal data. Both methods describe serial correlations of multivariate longitudinal outcomes using a modified Cholesky decomposition. However, the two methods consider different decompositions to explain the correlation between simultaneous responses. The first method uses enhanced linear covariance models so that the covariance matrix satisfies a positive definiteness condition; in addition, and principal component analysis and maximization-minimization algorithm (MM algorithm) were used to estimate model parameters. The second method considers variance-correlation decomposition and hypersphere decomposition to model covariance matrix. Simulations are used to compare the performance of the two methodologies.

• Journal of Korean Society of Transportation
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• v.22 no.1 s.72
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• pp.43-62
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• 2004

Conventionally the estimation method of the origin-destination Matrix has been developed by implementing the expansion of sampled data obtained from roadside interview and household travel survey. In the survey process, the bigger the sample size is, the higher the level of limitation, due to taking time for an error test for a cost and a time. Estimating the O-D matrix from observed traffic count data has been applied as methods of over-coming this limitation, and a gradient model is known as one of the most popular techniques. However, in case of the gradient model, although it may be capable of minimizing the error between the observed and estimated traffic volumes, a prior O-D matrix structure cannot maintained exactly. That is to say, unwanted changes may be occurred. For this reason, this study adopts a conjugate gradient algorithm to take into account two factors: estimation of the O-D matrix from the conjugate gradient algorithm while reflecting the prior O-D matrix structure maintained. This development of the O-D matrix estimation model is to minimize the error between observed and estimated traffic volumes. This study validates the model using the simple network, and then applies it to a large scale network. There are several findings through the tests. First, as the consequence of consistency, it is apparent that the upper level of this model plays a key role by the internal relationship with lower level. Secondly, as the respect of estimation precision, the estimation error is lied within the tolerance interval. Furthermore, the structure of the estimated O-D matrix has not changed too much, and even still has conserved some attributes.

• Journal of Korean Society of Transportation
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• v.19 no.4
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• pp.109-121
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• 2001

본 연구는 공간적 정보를 수집할 수 있는 영상검지기를 이용하여 주어진 대기행렬길이를 기반으로 하는 최적주기 알고리즘을 개발함으로써 교통신호 제어에 대한 새로운 신호계획을 제공한다. 본 연구에서는 교통수요의 공간적인 정보를 획득하는 방안으로서 영상검지기 기반의 대기행렬길이를 사용한다. 전략적 측면에서 다양한 교통상태를 적용하였으며, 주요 결과는 아래와 같다. 1. 영상검지기 기반의 대기행렬길이 계산방안을 제안한다. 이 방법은 한 링크의 상류부와 하류부에 2대의 영상검지기를 설치하여 대기행렬길이를 산출하는 방안이다. 2. 신호제어 변수인 주기 계산모형이 개발된다. 이 방법 역시 영상검지기를 기반으로 하는 대기행렬길이를 사용한다.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.12
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• pp.1243-1249
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• 2012

Finite element models are updated in two stages in this paper. In the first stage, damping is neglected, and mass and stiffness matrices of a finite element model are updated using an optimization technique. The objective function for optimization consists of natural frequencies and mode shapes obtained from experimental modal testing data and finite element analysis. In the second stage, damping is considered with the mass and stiffness matrices fixed. A damping matrix is estimated assuming a proportional damping system. Then the damping matrix is adjusted using an optimization process so that the difference between the analytical and measured frequency response functions becomes minimum. This procedure of model updating has been applied to a simulated system and an experimental cantilever beam.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.923-932
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• 2014

The Hurdle model can to analyze zero-inflated count data. This model is a mixed model of the logit model for a binary component and a truncated Poisson model of a truncated count component. We propose a new hurdle model with a general heterogeneous random effects covariance matrix to analyze longitudinal zero-inflated count data using modified Cholesky decomposition. This decomposition factors the random effects covariance matrix into generalized autoregressive parameters and innovation variance. The parameters are modeled using (generalized) linear models and estimated with a Bayesian method. We use these methods to carefully analyze a real dataset.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.79-87
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• 2011

A spatial time series model was used for analyzing the method of spatial time series (not the ARIMA model that is popular for analyzing spatial time series) by using chicken pox data which is a highly contagious disease and grid data due to ARIMA not reflecting the spatial processes. Time series model contains a weighting matrix, because that spatial time series model influences the time variation as well as the spatial location. The weighting matrix reflects that the more geographically contiguous region has the higher spatial dependence. It is hypothesized that the weighting matrix gives neighboring areas the same influence in the study of the spatial time series model. Therefore, we try to present the conclusion with a weighting matrix in a way that gives the same weight to existing neighboring areas in the study of the suitability of the STARMA model, spatial time series model and STBL model, in the comparative study of the predictive power for statistical inference, and the results. Furthermore, through the Kalman-Filter method we try to show the superiority of the Kalman-Filter method through a parameter assumption and the processes of prediction.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.548-551
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• 2004

합리적인 업무 수행도의 예측을 통해 기업은 기존 업무절차의 평가뿐 아니라 업무 개선방안과 새로운 업무의 설계기준을 제시할 수 있다. 본 연구는 업무효율지표들 중 가장 중요한 요소인 업무절차의 수행시간을 예측하는 모형을 제시한다. 일반적으로 기업의 업무는 예측가능하며 장기적으로 안정된 성격을 가진다. 우리는 이러한 특성을 바탕으로 한 대기행렬 모형을 구축하고 그것을 분석하여 정적인 방식의 업무실행 시 수행시간을 예측하였다. 그리고 모형의 성능을 시뮬레이션 기법을 사용하여 평가하였다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.1133-1136
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• 2005

Markovian Service Process(MSP)는 기존의 Markovian Arrival Process(MAP)에서 사용하던 위상 개념을 고객의 서비스 과정에 대응시킨 모형이다. 이는 서버의 상태에 따라 달라질 수 있는 서비스 상태를 위상 변화에 대응시키는 모형이다. 본 논문에서는 대기행렬 모형의 중요한 성능 척도인 고객 수 분포에 관하여 임의시점, 고객 도착 직전 시점, 고객 이탈 직후 시점에서의 관계식을 유도한다.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.433-443
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• 2011

We consider a compound Poisson risk model in which the premiums may depend on the state of the surplus process. By using the overflow probability of the workload process in the corresponding M/G/1 queueing model, we obtain the probability that the ruin occurs before the surplus reaches a given large value in the risk model. We also examplify the ruin probability in case of exponential claims.