Add to ORKGAbstract. A nonlinear regression model with correlated, normally distributed er-rors is investigated. The bias and the mean square error matrix of the approximate least squares estimator of regression parameters are derived and their limit proper-ties are studied. 1
Autocorrelation in errors and multicollinearity among the regressors are serious problems in regress...
This paper investigates an one-step procedure for the General Least Squares Estimation (GLSE) in the...
We compare the asymptotic covariance matrix of the ML estimator in a nonlinear measurement error mod...
. A nonlinear regression model with correlated, normally distributed errors is investigated. The bia...
AbstractThis paper is concerned with the linear regression model in which the variance of the depend...
summary:We derive expressions for the asymptotic approximation of the bias of the least squares esti...
AbstractThe rate of convergence of the least squares estimator in a non-linear regression model with...
Choosing the performance criterion to be mean squared error matrix, we have compared the least squar...
<p>Mean squared errors and squared correlation coefficients produced by regression models.</p
AbstractWe study nonlinear regression models whose both response and predictors are measured with er...
This paper shows that the nonlinear least squares estimator for unit root models has the limiting di...
AbstractThe paper uses empirical process techniques to study the asymptotics of the least-squares es...
Results on asymptotic and finite sample properties of an estimator of a nonlinear regression functio...
The goal of this work is to study the asymptotic and finite sample properties of an estimator of a no...
The ordinary least squares based estimator of the disturbance variance in a panel regression model w...
Autocorrelation in errors and multicollinearity among the regressors are serious problems in regress...
This paper investigates an one-step procedure for the General Least Squares Estimation (GLSE) in the...
We compare the asymptotic covariance matrix of the ML estimator in a nonlinear measurement error mod...
. A nonlinear regression model with correlated, normally distributed errors is investigated. The bia...
AbstractThis paper is concerned with the linear regression model in which the variance of the depend...
summary:We derive expressions for the asymptotic approximation of the bias of the least squares esti...
AbstractThe rate of convergence of the least squares estimator in a non-linear regression model with...
Choosing the performance criterion to be mean squared error matrix, we have compared the least squar...
<p>Mean squared errors and squared correlation coefficients produced by regression models.</p
AbstractWe study nonlinear regression models whose both response and predictors are measured with er...
This paper shows that the nonlinear least squares estimator for unit root models has the limiting di...
AbstractThe paper uses empirical process techniques to study the asymptotics of the least-squares es...
Results on asymptotic and finite sample properties of an estimator of a nonlinear regression functio...
The goal of this work is to study the asymptotic and finite sample properties of an estimator of a no...
The ordinary least squares based estimator of the disturbance variance in a panel regression model w...
Autocorrelation in errors and multicollinearity among the regressors are serious problems in regress...
This paper investigates an one-step procedure for the General Least Squares Estimation (GLSE) in the...
We compare the asymptotic covariance matrix of the ML estimator in a nonlinear measurement error mod...