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

• Autorzy: Yongge Tian
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

General linear model; Predictor; Estimator; Covariance matrix; Rank; Inertia; Equality; Inequality

Kategorie tematyczne

• 62J05: Linear regression
• 62H12: Estimation
• 15A09: Matrix inversion, generalized inverses
• 15A03: Vector spaces, linear dependence, rank

• Wydawca: De Gruyter Open
• Czasopismo: Open Mathematics
• Rocznik: 2017
• Tom: 15
• Numer: 1
• Strony: 126-150

Daty

wydano

2017-01-01

otrzymano

2016-03-15

zaakceptowano

2017-01-03

online

2017-02-27

Twórcy

autor

Yongge Tian

• China Economics and Management Academy, Central University of Finance and Economics, Beijing 100081,

Identyfikatory

DOI

10.1515/math-2017-0013