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Likelihood and parametric heteroscedasticity in normal connected linear models

100%

Mexia J., Real P.

Discussiones Mathematicae Probability and Statistics

2000

tom 20

nr 2

177-188

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.

Strong law of large numbers for additive extremum estimators

100%

Mexia J., Real P.

Discussiones Mathematicae Probability and Statistics

2001

tom 21

nr 2

81-88

Extremum estimators are obtained by maximizing or minimizing a function of the sample and of the parameters relatively to the parameters. When the function to maximize or minimize is the sum of subfunctions each depending on one observation, the extremum estimators are additive. Maximum likelihood estimators are extremum additive whenever the observations are independent. Another instance of additive extremum estimators are the least squares estimators for multiple regressions when the usual assumptions hold. A strong law of large numbers is derived for additive extremum estimators. This law requires only the existence of first order moments and may be of interest in connection with maximum likelihood estimators, since the usual assumption that the observations are identically distributed is discarded.

Ograniczanie wyników

2 Discussiones Mathematicae Probability and Statistics

2 Mexia J.

2 Real P.

1 2001

1 2000

Wyniki wyszukiwania

Likelihood and parametric heteroscedasticity in normal connected linear models

Strong law of large numbers for additive extremum estimators