Geodesic
Likelihood and parametric heteroscedasticity in normal connected linear models
Discussiones Mathematicae. Probability and Statistics, Tome 20 (2000) no. 2, pp. 177-188

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A linear model in which the mean vector and covariance matrix depend on the same parameters is connected. Limit results for these models are presented. The characteristic function of the gradient of the score is obtained for normal connected models, thus, enabling the study of maximum likelihood estimators. A special case with diagonal covariance matrix is studied.
Keywords: linear model, connected model, normal model, maximum likelihood estimators, score function, Newton-Raphson method 
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     title = {Likelihood and parametric heteroscedasticity in normal connected linear models},
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Mexia, Joao; Real, Pedro. Likelihood and parametric heteroscedasticity in normal connected linear models. Discussiones Mathematicae. Probability and Statistics, Tome 20 (2000) no. 2, pp. 177-188. http://geodesic.mathdoc.fr/item/DMPS_2000_20_2_a1/