On optimum experimental design for ridge estimates
On perturbations of strongly admissible prior distributions
On some alternative forms equivalent to Kruskal's condition for OLSE to be BLUE.
The necessary and sufficient condition for the ordinary least squares estimators (OLSE) to be the best linear unbiased estimators (BLUE) of the expected mean in the general univariate linear regression model was given by Kruskal (1968) using a coordinate-free approach. The purpose of this article is to present in the same manner some alternative forms of this condition and to prove two of the Haberman’s equivalent conditions in a different and simpler way. The results obtained in the general univariate...
On the Choice of Support of Re-Descending ...-Functions in Linear Models with Asymmetric Error Distributions.
On the confidence region of a vector parameter
On the equality of the ordinary least squares estimators and the best linear unbiased estimators in multivariate growth-curve models.
It is well known that there were proved several necessary and sufficient conditions for the ordinary least squares estimators (OLSE) to be the best linear unbiased estimators (BLUE) of the fixed effects in general linear models. The purpose of this article is to verify one of these conditions given by Zyskind [39, 40]: there exists a matrix Q such that ΩX = XQ, where X and Ω are the design matrix and the covariance matrix, respectively. It will be shown the accessibility of this condition in some...
On the Equivalence between Orthogonal Regression and Linear Model with Type-II Constraints
Orthogonal regression, also known as the total least squares method, regression with errors-in variables or as a calibration problem, analyzes linear relationship between variables. Comparing to the standard regression, both dependent and explanatory variables account for measurement errors. Through this paper we shortly discuss the orthogonal least squares, the least squares and the maximum likelihood methods for estimation of the orthogonal regression line. We also show that all mentioned approaches...
On the General Problem of Adjustment of Measured Values
On the Hsu condition in a replicated regression model
On the Invariance of Perturbed Null Vectors Under Column Scaling.
On the least quares fitting in a linear model.
On the Nonnegativity of XX* and its Relevance in Econometrics.
On the Performance of the Ordinary Least Squares Method under an Error Component Model.
On the principle of equivariance: concepts and applications.
On the relationship between regression analysis and mathematical programming.
On the robustness of multiple regression coefficient estimators obtained by the p-point method
On variance of the two-stage estimator in variance-covariance components model
The paper deals with a linear model with linear variance-covariance structure, where the linear function of the parameter of expectation is to be estimated. The two-stage estimator is based on the observation of the vector and on the invariant quadratic estimator of the variance-covariance components. Under the assumption of symmetry of the distribution and existence of finite moments up to the tenth order, an approach to determining the upper bound for the difference in variances of the estimators...
On variance-covariance components estimation in linear models with AR(1) disturbances.
One Case of Reduction of Nonlinear Regression to Linear
One multivariate linear model with nuisance parameters