Some remarks on comparison of predictors in seemingly unrelated linear mixed models

For a general vector of all unknown vectors in a constrained linear mixed model (CLMM), this study compared the dispersion matrices of the best linear unbiased predictors with any symmetric matrix for determining the optimality of predictors among others. Using the methodology of block matrix inertias and ranks as well as elementary block matrix operations, some equalities and inequalities are derived for comparisons. Additionally, the comparison findings for the CLMM reduce to special instances of the general vector of all unknown vectors. We also provide some comparison findings between the CLMM and its unconstrained form. Finally, we give a numerical example to illustrate our theoretical findings.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Some remarks on comparison of predictors in seemingly unrelated linear mixed models

Cited by 5 publications

References 0 publications

Characterizing relationships between BLUPs under linear mixed model and some associated reduced models

Characterizing relationships between BLUPs under linear mixed model and some associated reduced models

Comparison of predictors under constrained general linear model and its future observations

A matrix approach to determine optimal predictors in a constrained linear mixed model

Contact Info

Product

Resources

About