Non-normal variation across repeated measurements leads to nonlinear and heteroskedastic regression to the mean unlike the simple linear and homoskedastic regression to the mean found in normal models. This paper investigates the nature of the regression to the mean phenomenon in non-normal settings using (a) small variance approximations and (b) exact results obtained using normal mixtures to approximate non-normal distributions.
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