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A linear model for repeated measurements is proposed in which the correlation structure within each time sequence of measurements includes parameters for measurement error, variation between experimental units, and serial correlation within units. An approach to data analysis is presented which involves preliminary analysis by ordinary least squares, use of the empirical semi-variogram of residuals to suggest a suitable correlation structure, and formal inference using likelihood-based methods. Applications to two biological data sets are described.
Peter J. Diggle (Thu,) studied this question.