A typical formulation for a linear mixed model is Y = X(be) + Z(u) where (be) is a vector of "fixed" parameters, (u) is a vector of "random effects", and X and Z are matrices whose columns consist of design variables and/or covariates. In many applications the variances of the random effects corresponding to a particular column of Z are not constant, but are more realistically modeled as being proportional to some power of E(Y) the mean of Y. In particular, if the variance is proportional to the square of E(Y), we have a constant CV model. This talk will give an example of such a model and show how -xtmixed- can be used to estimate it and do proper inference on the estimated parameters. Results will be compared with Bayesian estimation under WINBUGS.
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