This book expands on the classical statistical multivariate analysis theory by focusing on
bilinear regression models a class of models comprising the classical growth curve model and
its extensions. In order to analyze the bilinear regression models in an interpretable way
concepts from linear models are extended and applied to tensor spaces. Further the book
considers decompositions of tensor products into natural subspaces and addresses maximum
likelihood estimation residual analysis influential observation analysis and testing
hypotheses where properties of estimators such as moments asymptotic distributions or
approximations of distributions are also studied. Throughout the text examples and several
analyzed data sets illustrate the different approaches and fresh insights into classical
multivariate analysis are provided. This monograph is of interest to researchers and Ph.D.
students in mathematical statistics signal processing and other fields where statistical
multivariate analysis is utilized. It can also be used as a text for second graduate-level
courses on multivariate analysis.
