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Growth Curve Models and Statistical Diagnostics

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 empha...

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Bibliographic Details
Main Authors: Pan, Jian-Xin, Fang, Kai-Tai (Author)
Format: eBook
Language:English
Published: New York, NY Springer New York 2002, 2002
Edition:1st ed. 2002
Series:Springer Series in Statistics
Subjects:
Statistical Theory And Methods
Statistics 
Biostatistics
Probability Theory
Biometry
Probabilities
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 1 Introduction
  • 1.1 General Remarks
  • 1.2 Statistical Diagnostics in Multivariate Analysis
  • 1.3 Growth Curve Model (GCM)
  • 1.4 Summary
  • 1.5 Preliminary Results
  • 1.6 Further Readings
  • 2 Generalized Least Square Estimation
  • 2.1 General Remarks
  • 2.2 Generalized Least Square Estimation
  • 2.3 Admissible Estimate of Regression Coefficient
  • 2.4 Bibliographical Notes
  • 3 Maximum Likelihood Estimation
  • 3.1 Maximum Likelihood Estimation
  • 3.2 Rao’s Simple Covariance Structure (SCS)
  • 3.3 Restricted Maximum Likelihood Estimation
  • 3.4 Bibliographical Notes
  • 4 Discordant Outlier and Influential Observation
  • 4.1 General Remarks
  • 4.2 Discordant Outlier Detection in the GCM with SCS
  • 4.3 Influential Observation in the GCM with SCS
  • 4.4 Discordant Outlier Detection in the GCM with UC
  • 4.5 Influential Observation in the GCM with UC
  • 4.6 Bibliographical Notes
  • 5 Likelihood-Based Local Influence
  • 5.1 General Remarks
  • 5.2 Local Influence Assessment in the GCM with SCS
  • 5.3 Local Influence Assessment in the GCM with UC
  • 5.4 Bibliographical Notes
  • 6 Bayesian Influence Assessment
  • 6.1 General Remarks
  • 6.2 Bayesian Influence Analysis in the GCM with SCS
  • 6.3 Bayesian Influence Analysis in the GCM with UC
  • 6.4 Bibliographical Notes
  • 7 Bayesian Local Influence
  • 7.1 General Remarks
  • 7.2 Bayesian Local Influence in the GCM with SCS
  • 7.3 Bayesian Local Influence in the GCM with UC
  • 7.4 Bibliographical Notes
  • Appendix Data sets used in this book
  • References
  • Author Index

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