응용통계연구 (The Korean Journal of Applied Statistics)

  • 제28권2호
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  • Pages.211-219
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  • 2015
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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일반화 선형혼합모형의 임의효과 공분산행렬을 위한 모형들의 조사 및 고찰

Survey of Models for Random Effects Covariance Matrix in Generalized Linear Mixed Model

  • Kim, Jiyeong (Department of Statistics, Sungkyunkwan University) ;
  • Lee, Keunbaik (Department of Statistics, Sungkyunkwan University)
  • 투고 : 2015.03.13
  • 심사 : 2015.03.30
  • 발행 : 2015.04.30
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⟨ 이전 논문 다음 논문 ⟩

초록

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.

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.

키워드

참고문헌

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피인용 문헌

  1. Bayesian modeling of random effects precision/covariance matrix in cumulative logit random effects models vol.24, pp.1, 2017, https://doi.org/10.5351/CSAM.2017.24.1.081