The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Modelling for Repeated Measures Data with Composite Covariance Structures

복합구조 반복측정자료에 대한 모형 연구

  • Lee, Jae-Hoon (Department of Statistics, Seoul National University) ;
  • Park, Tae-Sung (Department of Statistics, Seoul National University)
  • Received : 20091100
  • Accepted : 20091100
  • Published : 2009.12.31
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Abstract

In this paper, we investigated the composite covariance structure models for repeated measures data with multiple repeat factors. When the number of repeat factors is more than three, it is infeasible to fit the composite covariance models using the existing statistical packages. In order to fit the composite covariance structure models to real data, we proposed two approaches: the dimension reduction approach for repeat factors and the random effect model approximation approach. Our proposed approaches were illustrated by using the blood pressure data with three repeat factors obtained from 883 subjects.

본 논문에서는 반복인자가 여러 개인 반복측정자료에 대하여 반복인자간의 상관성을 고려한 복합공분산(composite covariance) 모형을 살펴보았다. 그러나 반복인자가 3개 이상인 경우에는 기존의 통계프로그램을 이용하여 적합하는 것이 불가능하다. 복합공분산 모형을 실제 자료에 적합하기위해 반복인자의 차원을 축소한 모형과 랜덤효과 모형을 이용하여 근사적으로 적합하는 방법을 제시하고 883명으로부터 수집한 반복인자가 3개인 혈압자료에 적용하였다.

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References

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