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Growth Curve Models and Statistical Diagnostics

  • Jian-Xin Pan
  • Kai-Tai Fang

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Jian-Xin Pan, Kai-Tai Fang
    Pages 1-37
  3. Jian-Xin Pan, Kai-Tai Fang
    Pages 38-76
  4. Jian-Xin Pan, Kai-Tai Fang
    Pages 77-158
  5. Jian-Xin Pan, Kai-Tai Fang
    Pages 159-223
  6. Jian-Xin Pan, Kai-Tai Fang
    Pages 224-263
  7. Jian-Xin Pan, Kai-Tai Fang
    Pages 264-307
  8. Jian-Xin Pan, Kai-Tai Fang
    Pages 308-352
  9. Back Matter
    Pages 353-388

About this book

Introduction

Growth-curve models are generalized multivariate analysis-of-variance models. These models are especially useful for investigating growth problems on short times in economics, biology, medical research, and epidemiology. This book systematically introduces the theory of the GCM with particular emphasis on their multivariate statistical diagnostics, which are based mainly on recent developments made by the authors and their collaborators. The authors provide complete proofs of theorems as well as practical data sets and MATLAB code.

Keywords

Fitting Growth Curve Models Likelihood MATLAB Multivariate Analysis Statistical Diagnostics Variance calculus data analysis linear regression mathematical statistics sets statistics

Authors and affiliations

  • Jian-Xin Pan
    • 1
  • Kai-Tai Fang
    • 2
  1. 1.Centre for Medical Statistics, Department of MathematicsKeele UniversityStaffordshireUK
  2. 2.Department of MathematicsHong Kong Baptist UniversityKowloonHong Kong

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-21812-0
  • Copyright Information Springer-Verlag New York 2002
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4419-2864-1
  • Online ISBN 978-0-387-21812-0
  • Series Print ISSN 0172-7397
  • Buy this book on publisher's site
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