• 응용통계연구
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• 제17권2호
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• pp.337-346
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• 2004

랜덤표본에 관한 이원분할표의 독립성검정에는 통상 피어슨의 카이제곱적합도검정과 우도비검정을 사용한다. 그러나 랜덤표본이 아닌 집락자료에 관한 분할표의 경우에는 이들 검정법은 잘못된 결과를 나타낸다. 이러한 경우에는 공변량의 고정효과 외에 집락에 따른 변량효과를 함께 포함하는 일반화선형혼합모형을 고려함으로써 집락간의 이질성과 집락내의 종속성을 반영할 수 있다. 본 연구에서는 집락자료의 분할표에 대한 일반화선형혼합모형을 소개하고 실례를 통하여 이들 모형의 적합에 대해 논의한다.

• ETRI Journal
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• 제32권5호
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• pp.687-694
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• 2010

Breaks in presence (BIP) are those moments during virtual environment (VE) exposure in which participants become aware of their real world setting and their sense of presence in the VE becomes disrupted. In this study, we investigate participants' experience when they encounter technical anomalies during game play. We induced four technical anomalies and compared the BIP responses of a navigation mode game to that of a combat mode game. In our analysis, we applied a linear mixed model (LMM) and compared the results with those of a conventional regression model. Results indicate that participants felt varied levels of impact and recovery when experiencing the various technical anomalies. The impact of BIPs was clearly affected by the game mode, whereas recovery appears to be independent of game mode. The results obtained using the LMM did not differ significantly from those obtained using the general regression model; however, it was shown that treatment effects could be improved by consideration of random effects in the regression model.

• 한국정보통신학회논문지
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• 제3권4호
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• pp.875-885
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• 1999

This paper presents an application of LMI-based techniques to the mixed $H_2/ H_\infty$ control of an inverted pendulum. The linear model of the inverted pendulum represented by an LFR(Linear Fractional Representation) model of uncertainties is derived. Considered uncertainties are three nonlinear components and a parameter uncertainty Augmenting the LFR model by adding weighting functions, we get a generalized plant, for which we design a mixed $H_2/ H_\infty$ controller using the LMI technique. To evaluate control performances and robust stability of the mixed $H_2/ H_\infty$ controller designed, we compare it with the $ H_\infty$controller through the simulation and experiment. The mixed $H_2/ H_\infty$ controller shows the better control performances and robust stability than the $H_\infty$controller in the sense of pendulum angle.

• Journal of the Korean Data and Information Science Society
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• 제28권4호
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• pp.927-936
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• 2017

To analyze longitudinal count data, Poisson linear mixed models are commonly used. In the models the random effects covariance matrix explains both within-subject variation and serial correlation of repeated count outcomes. When the random effects covariance matrix is assumed to be misspecified, the estimates of covariates effects can be biased. Therefore, we propose reasonable and flexible structures of the covariance matrix using autoregressive and moving average Cholesky decomposition (ARMACD). The ARMACD factors the covariance matrix into generalized autoregressive parameters (GARPs), generalized moving average parameters (GMAPs) and innovation variances (IVs). Positive IVs guarantee the positive-definiteness of the covariance matrix. In this paper, we use the ARMACD to model the random effects covariance matrix in Poisson loglinear mixed models. We analyze epileptic seizure data using our proposed model.

• Communications for Statistical Applications and Methods
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• 제7권2호
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• pp.403-414
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• 2000

A diagnostic tool for testing homogeneity for random effects is proposed in unbalanced linear mixed model based on score statistic. The finite sample behavior of the test statistic is examined using Monte Carlo experiments examine the chi-square approximation of the test statistic under the null hypothesis.

• Communications for Statistical Applications and Methods
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• 제22권5호
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• pp.507-518
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• 2015

It is not easy to select a linear mixed model since the main interest for model building could be different and the number of parameters in the model could not be clearly defined. In this paper, performance of conditional Akaike Information Criteria and its bias-corrected version are compared with marginal Bayesian and Akaike Information Criteria through a simulation study. The results from the simulation study indicate that bias-corrected conditional Akaike Information Criteria shows promising performance when candidate models exclude large models containing the true model, but bias-corrected one prefers over-parametrized models more intensively when a set of candidate models increases. Marginal Bayesian and Akaike Information Criteria also have some difficulty to select the true model when the design for random effects is nested.

• 응용통계연구
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• 제23권6호
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• pp.1169-1178
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• 2010

본 논문은 불균형 자료에서 선형혼합모형에 적용되는 Akaike Information Criterion(AIC)의 효율에 대한 연구이다. Vaida와 Balanchard (2005)에 의해 제안된 cAIC(conditional AIC)는 mAIC(marginal AIC)가 임의효과의 예측에 대한 불확실성을 모형선택에서 반영하지 못하는 단점을 극복할 수 있는 방법이다. cAIC에 대한 이론적인 성질과 확장은 Liang 등 (2008)과 Greven과 Kneib (2010)에 의하여 연구되었다. cAIC의 형태는 자료의 구조에 영향을 받지는 않지만 선형혼합모형에서 모수의 추정 효율은 자료의 불균형의 정도에 따라 많은 영향을 받는 것이 알려져 있다. 기존의 연구에서 실시한 모든 모의실험이 자료가 균형인 경우에만 실행되어 자료의 불균형이 AIC에 근거한 혼합모형 선택 방법의 효율에 어떤 영향을 미치는지 알려져 있지 않다. 본 논문은 자료의 불균형이 모형선택 방법의 효율에 미치는 영향을 모의실험을 통하여 알아보았다. 자료의 불균형이 심해짐에 따라 AIC에 근거한 모형선택방법은 복잡한 모형을 선택하는 경향이 낮아짐을 보였다.

• 산업경영시스템학회지
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• 제44권1호
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• pp.26-36
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• 2021

The topic of this study is the field of humanitarian logistics for disaster response. Many existing studies have revealed that compliance with the golden time in response to a disaster determines the success or failure of relief activities, and logistics costs account for 80% of the disaster response cost. Besides, the agility, responsiveness, and effectiveness of the humanitarian logistics system are emphasized in consideration of the disaster situation's characteristics, such as the urgency of life-saving and rapid environmental changes. In other words, they emphasize the importance of logistics activities in disaster response, which includes the effective and efficient distribution of relief supplies. This study proposes a mathematical model for establishing a transport plan to distribute relief supplies in a disaster situation. To determine vehicles' route and the amount of relief for cities suffering a disaster, it mainly considers the urgency, effectiveness (restoration rate), and uncertainty in the logistics system. The model is initially developed as a mixed-integer nonlinear programming (MINLP) model containing some nonlinear functions and transform into a Mixed-integer linear programming (MILP) model using a logarithmic transformation and piecewise linear approximation method. Furthermore, a minimax problem is suggested to search for breakpoints and slopes to define a piecewise linear function that minimizes the linear approximation error. A numerical experiment is performed to verify the MILP model, and linear approximation error is also analyzed in the experiment.

• Journal of the Korean Statistical Society
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• 제19권2호
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• pp.171-175
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• 1990

A diagnostic test for detecting nonconstant variance in mixed linear models based on the score statistic is derived through the technique of model expansion, and compared to the log likelihood ratio test.

• Journal of the Korean Data and Information Science Society
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• 제20권2호
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• pp.445-452
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• 2009

Credibility models are actuarial tools to distribute premiums fairly among a heterogeneous group of policyholders. Many existing credibility models can be expressed as special cases of linear mixed effects models. In this paper we propose a nonlinear credibility regression model by reforming the linear mixed effects model through kernel machine. The proposed model can be seen as prediction method applicable in any setting where repeated measures are made for subjects with different risk levels. Experimental results are then presented which indicate the performance of the proposed estimating procedure.