Robustness Properties of the F-Test and Best Linear Unbiased Estimators in Linear Models.

1982 ◽  
Author(s):  
Bimal Kumar Sinha ◽  
Hilmar Drygas
Keyword(s):  
Linear Models ◽  
F Test ◽  
Unbiased Estimators ◽  
Best Linear Unbiased ◽  
Best Linear Unbiased Estimators

scholarly journals Matrix rank and inertia formulas in the analysis of general linear models

2017 ◽  
Vol 15 (1) ◽  
pp. 126-150 ◽  
Author(s):  
Yongge Tian
Keyword(s):  
Statistical Analysis ◽  
Least Squares ◽  
Linear Models ◽  
Linear Regression Analysis ◽  
Ordinary Least Squares ◽  
Covariance Matrices ◽  
General Linear ◽  
General Linear Models ◽  
Best Linear Unbiased ◽  
Best Linear Unbiased Estimators

Abstract Matrix mathematics provides a powerful tool set for addressing statistical problems, in particular, the theory of matrix ranks and inertias has been developed as effective methodology of simplifying various complicated matrix expressions, and establishing equalities and inequalities occurred in statistical analysis. This paper describes how to establish exact formulas for calculating ranks and inertias of covariances of predictors and estimators of parameter spaces in general linear models (GLMs), and how to use the formulas in statistical analysis of GLMs. We first derive analytical expressions of best linear unbiased predictors/best linear unbiased estimators (BLUPs/BLUEs) of all unknown parameters in the model by solving a constrained quadratic matrix-valued function optimization problem, and present some well-known results on ordinary least-squares predictors/ordinary least-squares estimators (OLSPs/OLSEs). We then establish some fundamental rank and inertia formulas for covariance matrices related to BLUPs/BLUEs and OLSPs/OLSEs, and use the formulas to characterize a variety of equalities and inequalities for covariance matrices of BLUPs/BLUEs and OLSPs/OLSEs. As applications, we use these equalities and inequalities in the comparison of the covariance matrices of BLUPs/BLUEs and OLSPs/OLSEs. The work on the formulations of BLUPs/BLUEs and OLSPs/OLSEs, and their covariance matrices under GLMs provides direct access, as a standard example, to a very simple algebraic treatment of predictors and estimators in linear regression analysis, which leads a deep insight into the linear nature of GLMs and gives an efficient way of summarizing the results.

On Multilevel Best Linear Unbiased Estimators

2020 ◽  
Vol 8 (2) ◽  
pp. 601-635
Author(s):  
Daniel Schaden ◽  
Elisabeth Ullmann
Keyword(s):  
Unbiased Estimators ◽  
Best Linear Unbiased ◽  
Best Linear Unbiased Estimators

Estimation of the Parameters of Triangular Distribution by Order Statistics

2003 ◽  
Vol 54 (1-2) ◽  
pp. 45-56 ◽  
Author(s):  
Philip Samuel ◽  
P. Yageen Thomas
Keyword(s):  
Order Statistics ◽  
Triangular Distribution ◽  
Unbiased Estimators ◽  
Product Moments ◽  
Scale Parameters ◽  
U Statistics ◽  
Best Linear Unbiased ◽  
Best Linear Unbiased Estimators ◽  
Moments Of Order Statistics ◽  
Single And Product Moments

In this paper, we derive explicit expressions for the single and product moments of order statistics arising from the standard triangular distribution. Best linear unbiased estimators of the location and scale parameters of a triangular distribution based on order statistics are obtained. The efficiencies of these estimators are also compared with estimators based on U-statistics

Best Linear Unbiased Estimators for Stereology

Biometrics ◽  
1980 ◽  
Vol 36 (4) ◽  
pp. 595 ◽  
Author(s):  
Luis-M. Cruz-Orive
Keyword(s):  
Unbiased Estimators ◽  
Best Linear Unbiased ◽  
Best Linear Unbiased Estimators
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