AbstractAdmissibility of linear estimators of a regression coefficient in linear models with and without the assumption that the underlying distribution is normal is discussed under a balanced loss function. In the non-normal case, a necessary and sufficient condition is given for linear estimators to be admissible in the space of homogeneous linear estimators. In the normal case, a sufficient condition is provided for restricted linear estimators to be admissible in the space of all estimators having finite risks under the balanced loss function. Furthermore, the sufficient condition is proved to be necessary in the normal case if additional conditions are assumed
Graduation date: 1986We describe a general finite-dimensional inner product space setting for studyi...
Under the general mixed linear model, linear admissible estimators for linear functions of fixed and...
The linearly sufficient and admissible linear estimators with bounded mean squared error function in...
AbstractAdmissibility of linear estimators of a regression coefficient in linear models with and wit...
AbstractThis article investigates linear minimax estimators of regression coefficient in a linear mo...
AbstractIn this paper we investigate the admissibility of linear estimators in the multivariate line...
summary:In this paper, we study the admissibility of linear estimator of regression coefficient in l...
AbstractIn this paper, we study the characterization of admissible linear estimators of regression c...
This article considers a linear regression model when a set of exact linear restrictions binding the...
We derive the optimal heterogeneous, homogeneous and homogeneous unbiased estimators of the coeffici...
AbstractThis article investigates the minimaxity of matrix linear estimators of regression coefficie...
AbstractUsing a technique originated by A. Olsen, J. Seely, and D. Birkes (Ann. Statist. 4 (1976), 8...
AbstractIn this paper it is demonstrated that a homogeneous linear estimator for the vector of param...
This paper extends the balanced loss function to a more general set up. The ordinary least squares a...
summary:It was recently shown that all estimators which are locally best in the relative interior of...
Graduation date: 1986We describe a general finite-dimensional inner product space setting for studyi...
Under the general mixed linear model, linear admissible estimators for linear functions of fixed and...
The linearly sufficient and admissible linear estimators with bounded mean squared error function in...
AbstractAdmissibility of linear estimators of a regression coefficient in linear models with and wit...
AbstractThis article investigates linear minimax estimators of regression coefficient in a linear mo...
AbstractIn this paper we investigate the admissibility of linear estimators in the multivariate line...
summary:In this paper, we study the admissibility of linear estimator of regression coefficient in l...
AbstractIn this paper, we study the characterization of admissible linear estimators of regression c...
This article considers a linear regression model when a set of exact linear restrictions binding the...
We derive the optimal heterogeneous, homogeneous and homogeneous unbiased estimators of the coeffici...
AbstractThis article investigates the minimaxity of matrix linear estimators of regression coefficie...
AbstractUsing a technique originated by A. Olsen, J. Seely, and D. Birkes (Ann. Statist. 4 (1976), 8...
AbstractIn this paper it is demonstrated that a homogeneous linear estimator for the vector of param...
This paper extends the balanced loss function to a more general set up. The ordinary least squares a...
summary:It was recently shown that all estimators which are locally best in the relative interior of...
Graduation date: 1986We describe a general finite-dimensional inner product space setting for studyi...
Under the general mixed linear model, linear admissible estimators for linear functions of fixed and...
The linearly sufficient and admissible linear estimators with bounded mean squared error function in...