Growth Curve Models and Statistical Diagnostics

Softcover Nachdruck of the original 1. Auflage 2002
ISBN: 978-1-4419-2864-1
Verlag: Springer

Buch, Englisch, 388 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 616 g

Reihe: Springer Series in Statistics

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.

Zielgruppe

Research

Autoren/Hrsg.

Fang, Kai-Tai

Pan, Jian-Xin

Fachgebiete

• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung

Inhaltsverzeichnis

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.

Über Autor(innen)

Jian-Xin Pan is a lecturer in Medical Statistics of Keele University in the U.K. He has published more than twenty papers on growth curve models, statistical diagnostics and linear/non-linear mixed models. He has a long-standing research interest in longitudinal data analysis and repeated measures in medicine and agriculture. Kai-Tai Fang is a chair professor in Statistics of Hong Kong Baptist University and a fellow of the Institute of Mathematical Statistics. He has published several books with Springer-Verlag, Chapman & Hall, and Science Press and is an author or co-author of over one hundred papers. His research interest includes generalized multivariate analysis, elliptically contoured distributions and uniform design.