In practical applications of regression models, we may meet with the situation where a true model is misspecified in some other forms due to certain unforeseeable reasons, so that estimation and statistical inference results obtained under the true and misspecified regression models are not necessarily the same, and therefore, it is necessary to compare these results and to establish certain links between them for the purpose of reasonably explaining and utilizing the misspecified regression models. In this paper, we propose and investigate some comparison and equivalence analysis problems on estimations and predictions under true and misspecified multivariate general linear models. We first give the derivations and presentations of the best linear unbiased predictors (BLUPs) and the best linear unbiased estimators (BLUEs) of unknown parametric matrices under a true multivariate general linear model and its misspecified form. We then derive a variety of necessary and sufficient conditions for the BLUPs/BLUEs under the two competing models to be equal using a series of highly-selective formulas and facts associated with ranks, ranges and generalized inverses of matrices, as well as block matrix operations.

This paper is concerned with the relationships between best linear minimum biased predictors (BLMBPs) in the context of a general linear model (GLM) and its transformed general linear models (TGLMs). We shall establish a mathematical procedure by means of some exact and analytical tools in matrix theory that were developed in recent years. The coverage includes constructing a general vector composed of all unknown parameters in the context of a GLM and its TGLMs, deriving the exact expressions of the BLMBPs through the technical use of analytical solutions of a constrained quadratic matrix-valued function optimization problem in the Löwner partial ordering, and discussing a variety of theoretical performances and properties of the BLMBPs. We also give a series of characterizations of relationships between BLMBPs under a given GLM and its TGLMs.