We compare the asymptotic covariance matrix of the ML estima-tor in a nonlinear measurement error model to the asymptotic covari-ance matrices of the CS and SQS estimators studied in Kukush et al (2002). For small measurement error variances they are equal up to the order of the measurement error variance and thus nearly equally efficient
. A nonlinear regression model with correlated, normally distributed errors is investigated. The bia...
In this thesis we study the effect of regressors measured with an error on an estimated coefficients...
summary:In many cases we can consider the regression parameters as realizations of a random variable...
We compare the asymptotic covariance matrix of the ML estimator in a nonlinear measurement error mod...
We study a nonlinear measurement model where the response vari-able has a density belonging to the e...
In this paper we consider measurement error models when the observed random vectors are independent ...
AbstractThe problem of simultaneous estimation of the regression parameters in a multiple regression...
Asymptotic Properties of the Maximum Likelihood Estimators in the Nonlinear Regression Model with No...
Inference on linear functionals of the latent distribution in measurement error models is considered...
AbstractWe study nonlinear regression models whose both response and predictors are measured with er...
Measurement error models assume that errors occur in both the response and predictor variables. In u...
The issues of identification and estimation of nonlinear errors-in-variables models are explored. Th...
We consider least absolute error estimation in a nonlinear dynamic model with neither independent no...
AbstractWe consider a Poisson model, where the mean depends on certain covariates in a log-linear wa...
A multivariate ultrastructural measurement error model is considered and it is assumed that some pri...
. A nonlinear regression model with correlated, normally distributed errors is investigated. The bia...
In this thesis we study the effect of regressors measured with an error on an estimated coefficients...
summary:In many cases we can consider the regression parameters as realizations of a random variable...
We compare the asymptotic covariance matrix of the ML estimator in a nonlinear measurement error mod...
We study a nonlinear measurement model where the response vari-able has a density belonging to the e...
In this paper we consider measurement error models when the observed random vectors are independent ...
AbstractThe problem of simultaneous estimation of the regression parameters in a multiple regression...
Asymptotic Properties of the Maximum Likelihood Estimators in the Nonlinear Regression Model with No...
Inference on linear functionals of the latent distribution in measurement error models is considered...
AbstractWe study nonlinear regression models whose both response and predictors are measured with er...
Measurement error models assume that errors occur in both the response and predictor variables. In u...
The issues of identification and estimation of nonlinear errors-in-variables models are explored. Th...
We consider least absolute error estimation in a nonlinear dynamic model with neither independent no...
AbstractWe consider a Poisson model, where the mean depends on certain covariates in a log-linear wa...
A multivariate ultrastructural measurement error model is considered and it is assumed that some pri...
. A nonlinear regression model with correlated, normally distributed errors is investigated. The bia...
In this thesis we study the effect of regressors measured with an error on an estimated coefficients...
summary:In many cases we can consider the regression parameters as realizations of a random variable...