On Seifert's ANOVA-like test for variance components
On small sample inference for common mean in heteroscedastic one-way model
In this paper we consider and compare several approximate methods for making small-sample statistical inference on the common mean in the heteroscedastic one-way random effects model. The topic of the paper was motivated by the problem of interlaboratory comparisons and is also known as the (traditional) common mean problem. It is also closely related to the problem of multicenter clinical trials and meta-analysis. Based on our simulation study we suggest to use the approach proposed by Kenward...
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 some properties of ML and REML estimators in mixed normal models with two variance components
In the paper, the problem of estimation of variance components σ₁² and σ₂² by using the ML-method and REML-method in a normal mixed linear model 𝒩 {Y,E(Y) = Xβ, Cov(Y) = σ₁²V + σ₂²Iₙ} is considered. This paper deal with properties of estimators of variance components, particularly when an explicit form of these estimators is unknown. The conditions when the ML and REML estimators can be expressed in explicit forms are given, too. The simulation study for one-way classification unbalanced random...
On testing hypotheses in the generalized Skillings-Mack random blocks setting
The testing of the null hypothesis of no treatment effect against the alternative of increasing treatment effect by means of rank statistics is extended from the classical Friedman random blocks model into an unbalanced design allowing treatments not to be applied simultaneously in each random block. The asymptotic normality of the constructed rank test statistic is proved both in the setting not allowing ties and also for models with presence of ties. As a by-product of the proofs a multiple comparisons...
On testing variance components in unbalanced mixed linear model
The paper presents some approximate and exact tests for testing variance components in general unbalanced mixed linear model. It extends the results presented by Seifert (1992) with emphasis on the computational aspects of the problem.
On the Behrens-Fisher distribution and its generalization to the pairwise comparisons
Weerahandi (1995b) suggested a generalization of the Fisher's solution of the Behrens-Fisher problem to the problem of multiple comparisons with unequal variances by the method of generalized p-values. In this paper, we present a brief outline of the Fisher's solution and its generalization as well as the methods to calculate the p-values required for deriving the conservative joint confidence interval estimates for the pairwise mean differences, refered to as the generalized Scheffé intervals....
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 small-area estimators: fixed vs. random area parameters.
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.