Using the jackknife to estimate the variance of regression estimators from repeated measures studies

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Abstract
We discuss methods for analyzing data from repeated measures studies. The marginal distribution of the response at each time is assumed to be from the exponential family. The maximum likelihood regression estimators, assuming the repeated measurements on the same individual are independent, have been shown to be consistent by Liang and Zeger (1986). Liang and Zeger (1986) propose a ‘robust’ estimator of the asymptotic variance of the estimated regression parameters. We show that a ‘one-step’ jackknife estimator of variance is asymptotically equivalent to the Liang and Zeger variance estimator; we perform simulation runs to compare the small sample performance of the jackknife and Liang and Zeger's estimator. We also show that SAS Proc Jackreg (1986), which does jackknife regression for ordinary least squares, can be used to calculate jackknife estimates of the regression parameters and the estimated parameters' covariance matrix.

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