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Bilinear Regression Analysis - An Introduction (Paperback, 1st ed. 2018)
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Bilinear Regression Analysis - An Introduction (Paperback, 1st ed. 2018)
Series: Lecture Notes in Statistics, 220
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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.
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