Add to ORKGA new estimator in linear models with equi-correlated random errors is postulated. Consistency properties of the proposed estimator and the ordinary least squares estimator are studied. It is shown that the new estimator has smaller variance than the usual least squares estimator under some mild conditions. In addition, it is observed that the new estimato
AbstractA recent theorem of T. L. Hai, H. Robbins, and C. Z. Wei (J. Multivariate Anal. 9 (1979), 34...
This paper considers the linear regression model with multiple stochastic regressors, intercept, and...
AbstractIn a standard linear model, we explore the optimality of the least squares estimator under a...
AbstractLet Yn, n≥1, be a sequence of integrable random variables with EYn = xn1β1 + xn2β2 + … + xnp...
Necessary and sufficient conditions for the existence of maximum likelihood estimators of unknown pa...
This note formalizes bias and inconsistency results for ordinary least squares (OLS) on the linear p...
This paper deals with a homoskedastic errors-in-variables linear regression model and properties of ...
AbstractThe strong consistency of M-estimators in linear models is considered. Under some conditions...
summary:The consistency of the least trimmed squares estimator (see Rousseeuw [Rous] or Hampel et al...
The least squares estimator for the linear regression model is shown to converge to the true paramet...
AbstractThe paper uses empirical process techniques to study the asymptotics of the least-squares es...
summary:Linear relations, containing measurement errors in input and output data, are taken into acc...
In the usual linear regression model the sample regression coefficients converge with probability on...
AbstractThe strong consistency of least squares estimates in multiple regression models is establish...
This paper introduces an estimator for errors-in-variables models in which all measurements are corr...
AbstractA recent theorem of T. L. Hai, H. Robbins, and C. Z. Wei (J. Multivariate Anal. 9 (1979), 34...
This paper considers the linear regression model with multiple stochastic regressors, intercept, and...
AbstractIn a standard linear model, we explore the optimality of the least squares estimator under a...
AbstractLet Yn, n≥1, be a sequence of integrable random variables with EYn = xn1β1 + xn2β2 + … + xnp...
Necessary and sufficient conditions for the existence of maximum likelihood estimators of unknown pa...
This note formalizes bias and inconsistency results for ordinary least squares (OLS) on the linear p...
This paper deals with a homoskedastic errors-in-variables linear regression model and properties of ...
AbstractThe strong consistency of M-estimators in linear models is considered. Under some conditions...
summary:The consistency of the least trimmed squares estimator (see Rousseeuw [Rous] or Hampel et al...
The least squares estimator for the linear regression model is shown to converge to the true paramet...
AbstractThe paper uses empirical process techniques to study the asymptotics of the least-squares es...
summary:Linear relations, containing measurement errors in input and output data, are taken into acc...
In the usual linear regression model the sample regression coefficients converge with probability on...
AbstractThe strong consistency of least squares estimates in multiple regression models is establish...
This paper introduces an estimator for errors-in-variables models in which all measurements are corr...
AbstractA recent theorem of T. L. Hai, H. Robbins, and C. Z. Wei (J. Multivariate Anal. 9 (1979), 34...
This paper considers the linear regression model with multiple stochastic regressors, intercept, and...
AbstractIn a standard linear model, we explore the optimality of the least squares estimator under a...