Bilinear Regression Analysis

An Introduction

  • Dietrich┬ávon Rosen

Part of the Lecture Notes in Statistics book series (LNS, volume 220)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Dietrich von Rosen
    Pages 1-37
  3. Dietrich von Rosen
    Pages 99-175
  4. Dietrich von Rosen
    Pages 177-220
  5. Dietrich von Rosen
    Pages 221-280
  6. Dietrich von Rosen
    Pages 281-361
  7. Dietrich von Rosen
    Pages 363-421
  8. Back Matter
    Pages 423-468

About this book


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.


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

Authors and affiliations

  • Dietrich┬ávon Rosen
    • 1
  1. 1.Department of Energy and TechnologySwedish University of Agricultural SciencesUppsalaSweden

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-78782-4
  • Online ISBN 978-3-319-78784-8
  • Series Print ISSN 0930-0325
  • Series Online ISSN 2197-7186
  • Buy this book on publisher's site