AbstractThe strong consistency of least squares estimates in multiple regression models is established under minimal assumptions on the design and weak dependence and moment restrictions on the errors
AbstractThe strong law of large numbers is considered for a multivariate martingale normed by a sequ...
This paper looks at the strong consistency of the ordinary least squares (OLS) estimator in linear r...
A new estimator in linear models with equi-correlated random errors is postulated. Consistency prope...
AbstractA recent theorem of T. L. Hai, H. Robbins, and C. Z. Wei (J. Multivariate Anal. 9 (1979), 34...
AbstractThe strong consistency of least squares estimates in multiple regression models is establish...
AbstractMultiple linear regression models with non random regressors in continuous time are consider...
AbstractLet Yn, n≥1, be a sequence of integrable random variables with EYn = xn1β1 + xn2β2 + … + xnp...
AbstractThe strong consistency of M-estimators in linear models is considered. Under some conditions...
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
AbstractUnder minimum assumptions on the stochastic regressors, strong consistency of Bayes estimate...
SIGLECNRS RS 17660 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc
The least squares estimator for the linear regression model is shown to converge to the true paramet...
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
AbstractA semimartingale driven continuous time linear regression model is studied. Assumptions conc...
In the usual linear regression model the sample regression coefficients converge with probability on...
AbstractThe strong law of large numbers is considered for a multivariate martingale normed by a sequ...
This paper looks at the strong consistency of the ordinary least squares (OLS) estimator in linear r...
A new estimator in linear models with equi-correlated random errors is postulated. Consistency prope...
AbstractA recent theorem of T. L. Hai, H. Robbins, and C. Z. Wei (J. Multivariate Anal. 9 (1979), 34...
AbstractThe strong consistency of least squares estimates in multiple regression models is establish...
AbstractMultiple linear regression models with non random regressors in continuous time are consider...
AbstractLet Yn, n≥1, be a sequence of integrable random variables with EYn = xn1β1 + xn2β2 + … + xnp...
AbstractThe strong consistency of M-estimators in linear models is considered. Under some conditions...
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
AbstractUnder minimum assumptions on the stochastic regressors, strong consistency of Bayes estimate...
SIGLECNRS RS 17660 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc
The least squares estimator for the linear regression model is shown to converge to the true paramet...
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
AbstractA semimartingale driven continuous time linear regression model is studied. Assumptions conc...
In the usual linear regression model the sample regression coefficients converge with probability on...
AbstractThe strong law of large numbers is considered for a multivariate martingale normed by a sequ...
This paper looks at the strong consistency of the ordinary least squares (OLS) estimator in linear r...
A new estimator in linear models with equi-correlated random errors is postulated. Consistency prope...
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