Bilinear Regression Analysis
An Introduction
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Beschreibung
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.
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.
Presents results for bilinear regression models and their connection to classical statistical multivariate analysis Sheds new light on the notion of linear and bilinear multivariate models Includes both advanced theory and results for the validation of models for applied data analysis Employs examples, plots for tensor products and analyzed data sets to facilitate understanding
Autor*in
Dietrich von Rosen
Themen in »Bilinear Regression Analysis«
62H12, 62H15, 62H99, 62J20, 15A69, 15A03, 15A63 Bilinear regression analysis Growth curve model Generalised growth curve model Influence analysis Likelihood ratio testing Maximum likelihood estimation Residual analysis Multivariate statistical analysis Influential observation analysis matrix theory
Stimmen zu »Bilinear Regression Analysis«
“It is an interesting book, strongly recommended to researchers who have an interest in the topic of bilinear regression.” (Michel H. Montoril, Mathematical Reviews, August, 2019)
“The present book offers a complete presentation of the statistical techniques concerning bilinear regression analysis. … A special mention goes to the bibliography that accompanies each chapter. Far from being a simple list of papers containing the results recalled in the text, it is a real history of statistics, where the early ideas of bilinear regression are highlighted.” (Fabio Rapallo, zbMATH 1398.62003, 2018)
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