Modelling for Repeated Measures Data with Composite Covariance Structures

복합구조 반복측정자료에 대한 모형 연구

Lee, Jae-Hoon;Park, Tae-Sung

  • Received : 20091100
  • Accepted : 20091100
  • Published : 2009.12.31


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.


Repeat measures data;composite covariance structure;random effect;repeat factor


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Supported by : 한국학술진흥재단