Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimators of the true parameter for a nonlinear regression model naving random regressors and a multiplicative disturbance term. Special cases of this result include the least absolute value and the least squares estimation procedures. © 1992, Taylor & Francis Group, LLC. All rights reserved
A strongly consistent sequence of estimators of the variance of the disturbance term in a nonlinear ...
Standard consistency proofs of the maximum likelihood estimator rely on the assumption that the obse...
SIGLECNRS RS 17660 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
Conditions are given which ensure the nonexistence of a sequence of strongly consistent M-estimators...
Conditions are given which ensure the nonexistence of a sequence of strongly consistent M-estimators...
In this paper, we study conditions sufficient for strong consistency of a class of estimators of par...
A strongly consistent sequence of estimators of the variance of the disturbance term in a nonlinear ...
AbstractThe strong consistency of least squares estimates in multiple regression models is establish...
A strongly consistent sequence of estimators of the variance of the disturbance term in a nonlinear ...
AbstractMultiple linear regression models with non random regressors in continuous time are consider...
AbstractA recent theorem of T. L. Hai, H. Robbins, and C. Z. Wei (J. Multivariate Anal. 9 (1979), 34...
A strongly consistent sequence of estimators of the variance of the disturbance term in a nonlinear ...
Standard consistency proofs of the maximum likelihood estimator rely on the assumption that the obse...
SIGLECNRS RS 17660 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
Conditions are given which ensure the nonexistence of a sequence of strongly consistent M-estimators...
Conditions are given which ensure the nonexistence of a sequence of strongly consistent M-estimators...
In this paper, we study conditions sufficient for strong consistency of a class of estimators of par...
A strongly consistent sequence of estimators of the variance of the disturbance term in a nonlinear ...
AbstractThe strong consistency of least squares estimates in multiple regression models is establish...
A strongly consistent sequence of estimators of the variance of the disturbance term in a nonlinear ...
AbstractMultiple linear regression models with non random regressors in continuous time are consider...
AbstractA recent theorem of T. L. Hai, H. Robbins, and C. Z. Wei (J. Multivariate Anal. 9 (1979), 34...
A strongly consistent sequence of estimators of the variance of the disturbance term in a nonlinear ...
Standard consistency proofs of the maximum likelihood estimator rely on the assumption that the obse...
SIGLECNRS RS 17660 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc
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