Abstract:
Maximum likelihood estimation of parameter structures in the
case of multivariate normal samples is considered. The procedure provides
a new statistical methodology for maximum likelihood estimation which does
not require derivation and solution of the likelihood equations. It is a flexible
procedure for the analysis of specific structures in mean vectors and covariance
matrices – including the case where the sample size is small relative to the
dimension of the observations. Special cases include different variations of
the Behrens-Fisher problem, proportional covariancematrices and proportional
mean vectors. Specific structures are illustrated with real data examples.
