Includes thorough treatment of logistic and Poisson regression.
* Introduction to generalized estimating questions.
* Numerous examples in fields ranging from biology and biopharmaceuticals to engineering and quality assurance.
* Provides guidance in using widely available software to illustrate all aspects of model-fitting, inference, and diagnostic testing.
Rezensionen / Stimmen
"...fulfills the need for an introductory textbook on the generalized linear model..." (Quarterly of Applied Mathematics, Vol. LX, No. 2, June 2002) "...recommended for students, engineers, scientists, and statisticians who want to familiarize themselves with generalized linear models." (Mathematical Reviews, 2002j) "...an excellent, up--to--date introduction to the field...easily used as a user guide...those less mathematically minded have no need to fear this book... I recommend this book to you all and am delighted to have had the opportunity to review it." (Statistical Methods in Medical Research, Vol. 11, 2002) "...a good textbook a good reference..." (The American Statistician, Vol. 57, No. 1, February 2003)
Reihe
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Illustrationen
Maße
Höhe: 24.1 cm
Breite: 16.3 cm
Gewicht
ISBN-13
978-0-471-35573-1 (9780471355731)
Schweitzer Klassifikation
RAYMOND H. MYERS is Professor Emeritus in the Department of Statistics at Virginia Tech in Blacksburg, Virginia.
DOUGLAS C. MONTGOMERY is Professor in the Department of Industrial Engineering at Arizona State University in Tempe, Arizona.
G. GEOFFREY VINING is Professor and Head of the Department of Statistics at Virginia Tech in Blacksburg, Virginia.
Preface.
1. Introduction to Generalized Linear Models.
2. Linear Regression Models.
3. Nonlinear Regression Models.
4. Logistic and Poisson Regression Models.
5. The Family of Generalized Linear Models.
6. Generalized Estimating Equations.
7. Further Advances and Applications in GLM.
Appendix 1: Background on Basic Test Statistics.
Appendix 2: Background from the Theory of Linear Models.
Appendix 3: The Gauss-Markov Theroem.
Appendix 4: The Relationship Between Maximum Likelihood Estimation of the Logistic Regression Model and Weighted Least Squares.
Appendix 5: Computational Details for GLMs for a Canonical Link.
Appendix 6: Computational Details for GLMs for a Noncanonical Link.
References.
Index.
