Likelihood and parametric heteroscedasticity in normal connected linear models

• Autorzy: Joao Tiago Mexia , Pedro Corte Real
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

Kategorie tematyczne

• 62J99: None of the above, but in this section
• 62F10: Point estimation

• Wydawca: Faculty of Mathematics, Computer Science and Econometrics, University of Zielona Góra
• Czasopismo: Discussiones Mathematicae Probability and Statistics
• Rocznik: 2000
• Tom: 20
• Numer: 2
• Strony: 177-188

Daty

wydano

2000

poprawiono

1999-04-19

otrzymano

2000-03-15

Twórcy

autor

Joao Tiago Mexia

• Universidade Nova de Lisboa, Departamento de Matemática da Faculdade de Ciencias e Tecnologia, Quinta da Torre, 2825 Monte de Caparica

autor

Pedro Corte Real

• Universidade Nova de Lisboa, Departamento de Matemática da Faculdade de Ciencias e Tecnologia, Quinta da Torre, 2825 Monte de Caparica

Bibliografia

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• [8] J.L. Kelley, General Topology, Princeton, New Jersey 1961.
• [9] J.T. Mexia, Linear Models with Partially Controlled Heteroscedasticity, Trabalhos de Investigaçao 2. Departamento de Matemática - Faculdade de Ciencias e Tecnologia/Universidade Nova de Lisboa 1993.
• [10] R.L. Plackett, Principles of Regression Analysis, Oxford University Press, Oxford 1960.
• [11] S.J. Prais and H.S. Houthakker, The Analysis of Family Budgets, Cambridge University Press, Cambridge 1955.
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Identyfikatory

DOI

10.7151/dmps.1010