AbstractIn modeling of an economic system, there may occur some stochastic constraints, that can cause some changes in the estimators and their respective behaviors. In this approach we formulate the simultaneous equation models into the problem of estimating the regression parameters of a multiple regression model, under elliptical errors. We define five different sorts of estimators for the vector-parameter. Their exact risk expressions are also derived under the balanced loss function. Comparisons are then made to clarify the performance of the proposed estimators. It is shown that the shrinkage factor of the Stein estimator is robust with respect to departures from normality assumption
AbstractFor the unknown positive parameter σ2 in a general linear model ℳ={y,Xβ,σ2Σ}, the two common...
AbstractFor the linear regression model y=Xβ+ϵ, we assume that for a given positive definite scale m...
The dissertation addresses three issues in the use of Stein-like estimators of the classical normal ...
AbstractThe problem of simultaneous estimation of the regression parameters in a multiple regression...
The performances of two full information techniques, Three Stage Least Squares (3SLS) and Full Infor...
We study a nonlinear measurement model where the response variable has a density belonging to the ex...
AbstractThis paper considers a general family of Stein rule estimators for the coefficient vector of...
summary:In mixed linear statistical models the best linear unbiased estimators need a known covarian...
AbstractFor the simple linear model Y=θ1+βx+ϵ where the error vector follows the elliptically contou...
summary:Unknown parameters of the covariance matrix (variance components) of the observation vector ...
A new Liu type of estimator for the seemingly unrelated regression (SUR) models is proposed that may...
This paper treats the problem of simultaneously estimating the precision matrices in multivariate no...
In this dissertation, we consider an estimation problem of the regression coefficients in both multi...
AbstractWhen estimating, under quadratic loss, the location parameterθof a spherically symmetric dis...
AbstractLet X,V1,…,Vn−1 be n random vectors in Rp with joint density of the formf(X−θ)′Σ−1(X−θ)+∑j=1...
AbstractFor the unknown positive parameter σ2 in a general linear model ℳ={y,Xβ,σ2Σ}, the two common...
AbstractFor the linear regression model y=Xβ+ϵ, we assume that for a given positive definite scale m...
The dissertation addresses three issues in the use of Stein-like estimators of the classical normal ...
AbstractThe problem of simultaneous estimation of the regression parameters in a multiple regression...
The performances of two full information techniques, Three Stage Least Squares (3SLS) and Full Infor...
We study a nonlinear measurement model where the response variable has a density belonging to the ex...
AbstractThis paper considers a general family of Stein rule estimators for the coefficient vector of...
summary:In mixed linear statistical models the best linear unbiased estimators need a known covarian...
AbstractFor the simple linear model Y=θ1+βx+ϵ where the error vector follows the elliptically contou...
summary:Unknown parameters of the covariance matrix (variance components) of the observation vector ...
A new Liu type of estimator for the seemingly unrelated regression (SUR) models is proposed that may...
This paper treats the problem of simultaneously estimating the precision matrices in multivariate no...
In this dissertation, we consider an estimation problem of the regression coefficients in both multi...
AbstractWhen estimating, under quadratic loss, the location parameterθof a spherically symmetric dis...
AbstractLet X,V1,…,Vn−1 be n random vectors in Rp with joint density of the formf(X−θ)′Σ−1(X−θ)+∑j=1...
AbstractFor the unknown positive parameter σ2 in a general linear model ℳ={y,Xβ,σ2Σ}, the two common...
AbstractFor the linear regression model y=Xβ+ϵ, we assume that for a given positive definite scale m...
The dissertation addresses three issues in the use of Stein-like estimators of the classical normal ...