• Asian-Australasian Journal of Animal Sciences
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• 제13권3호
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• pp.413-422
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• 2000

In the class of models which include random effects, the variance component estimates are important to obtain accurate predictors and estimators. Variance component estimation is straightforward for balanced data but not for unbalanced data. Since orthogonality among factors is absent in unbalanced data, various methods for variance component estimation are available. REML estimation is the most widely used method in animal breeding because of its attractive statistical properties. Recently, Bayesian approach became feasible through Markov Chain Monte Carlo methods with increasingly powerful computers. Furthermore, advances in variance component estimation with complicated models such as generalized linear mixed models enabled animal breeders to analyze non-normal data.

• Communications for Statistical Applications and Methods
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• 제21권1호
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• pp.45-60
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• 2014

We introduce a MCMC sampling for a generalized linear normal random effects model with the ordered probit link function based on latent variables from suitable truncated normal distribution. Such models have proven useful in practice and we have observed numerically reasonable results in the estimation of fixed effects when the random effect term is provided. Applications that utilize Korean Welfare Panel Study data can be difficult to model; subsequently, we find that an ordered probit model with the random effects leads to an improved analyses with more accurate and precise inferences.

• 응용통계연구
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• 제28권2호
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• pp.335-342
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• 2015

경시적 자료는 각 환자마다 시간에 따라 반복 측정되는 코호트 연구 등에서 많이 쓰인다. 본 연구는 반응변수 간 상관성을 고려할 수 있는 결합 다단계 일반화 선형모형을 이용하여, 다변량 경시적 자료 분석을 수행하였다. 한국 유전체 역학 연구에서 실시한 코호트 자료를 적합하고 결과를 해석한다. 조건부 아카이케 정보 기준을 이용하여 모형 선택을 하고, 변량효과들의 추정치들을 설명한다.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2002년도 추계 학술발표회 논문집
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• pp.141-146
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• 2002

In this article we consider the problem of constructing confidence intervals for a linear regression model with nested error structure. A popular approach is the likelihood-based method employed by PROC MIXED of SAS. In this paper, we examine the ability of MIXED to produce confidence intervals that maintain the stated confidence coefficient. Our results suggest the intervals for the regression coefficients work well, but the intervals for the variance component associated with the primary level cannot be recommended. Accordingly, we propose alternative methods for constructing confidence intervals on the primary level variance component. Computer simulation is used to compare the proposed methods. A numerical example and SAS code are provided to demonstrate the methods.

• 응용통계연구
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• 제28권2호
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• pp.211-219
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• 2015

일반화 선형혼합모델은 일반적으로 경시적 범주형 자료를 분석하는데 사용된다. 이 모델에서 임의효과는 반복 측정치들의 시간에 따른 의존성을 설명한다. 임의효과 공분산행렬의 추정은 여러가지 제약조건들 때문에 쉽지 않은 문제이다. 제약조건으로는 행렬의 모수들의 수가 많으며, 또한 추정된 공분산행렬은 양정치성을 만족하여야 한다. 이러한 제한을 극복하기 위해, 임의효과 공분산행렬의 모형화를 위한 여러가지 방법이 제안되었다: 수정 단냠레스키분해, 이동평균 단냠레스키분해와 부분 자기상관행렬을 이용한 방법이 있다. 이 논문에서 위의 제안된 방법들을 소개한다.

• Communications for Statistical Applications and Methods
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• 제9권2호
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• pp.515-520
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• 2002

In general, we use the hierarchical Poisson-gamma model for the Poisson data in generalized linear model. Time effect will be emphasized for the analysis of the observed data to be collected annually for the time period. An extended model with time effect for estimating the effect is proposed. In particularly, we discuss the Quasi likelihood function which is used to numerical approximation for the likelihood function of the parameter.

• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.589-598
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• 2015

In longitudinal studies missing data are common and require a complicated analysis. There are two popular modeling frameworks, pattern mixture model (PMM) and selection models (SM) to analyze the missing data. We focus on the PMM and we also propose Bayesian pattern mixture models using generalized linear mixed models (GLMMs) for longitudinal binary data. Sensitivity analysis is used under the missing not at random assumption.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1999년도 제14차 학술회의논문집
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• pp.88-91
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• 1999

The purpose of this paper is to propose an approach to design a robust servo controller based on the Mixed H$_2$/H$\sub$$\infty$/ theory. In order to do this, we first modify the generalized plant for the usual H$\sub$$\infty$/ servo problem to a structure of the Mixed H$_2$/H$\sub$$\infty$/ minimization problem by virtue of the internal model principle. By doing this, we can divide specifications adopted for robust servo system design into H$_2$and H$\sub$$\infty$/ performance criteria, respectively. Then, the mixed H$_2$/H$\sub$$\infty$/ problem is solved in order to find the best solution, by which we can minimize H$_2$-norm of the transfer function under the condition of H$\sub$$\infty$/-norm value, through Linear Matrix Equality (LMI).

• 응용통계연구
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• 제23권5호
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• pp.891-908
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• 2010

근래 소지역 추정(small area estimation)에 관한 연구는 비교적 활발하게 이루어진 편인데 비해, 우리나라의 국가통계 작성에 실제 활용된 사례는 거의 없는 실정이다. 이는 소지역 추정이 갖는 많은 장점에도 불구하고 공식통계 활용 여부를 판단하기가 그만큼 어렵기 때문이다. 본 연구는 소지역 추정방법에 의해 우리나라 시군구 실업자 통계를 생산하는 방법을 모색하고자 한다. 시군구 실업자수 추정은 로지스틱 선형혼합모형에 의한 EBLUP 타입(EBLUP-type) 추정량을 사용하였다. 실제자료분석과 모의실험 결과에 대해 다양한 평가 방법을 적용하고, 추정량의 특성을 비교 분석하였다. 그 결과 본 연구에서 적용한 로지스틱 선형혼합모형 기반 EBLUP 타입 추정량은 우리나라 시군구 실업자수 추정에 활용 가능성이 높은 것으로 평가되었다.