Likelihood and Parametric Heteroscedasticity In Normal Connected Linear Models

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A linear model in which the mean vector and covariance matrix depend on the sameparameters is connected. Limit results for these models are presented. The characteristicfunction 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 covariancematrix is studied.
Original languageEnglish
Pages (from-to)177-188
Number of pages12
JournalDiscussiones Mathematicae: Probability and Statistics
Issue number2
Publication statusPublished - 1 Jan 2000


  • linear model
  • connected model
  • normal model
  • maximum likelihood estimators
  • score function
  • Newton-Raphson method
  • connected models


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