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Generalized Linear Models: With Applications in Engineering and the Sciences / Edition 1

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Overview

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.
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Editorial Reviews

From the Publisher
"The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities." (Technometrics, Vol. 44, No. 3, August 2002)

"...fulfills the need for an introductory textbook on the generalized linear model..." (Quarterly of Applied Mathematics, Vol. LX, No. 2, June 2002)

"I recommend...to anyone that has an interest...an excellent reference for both the practitioner and the academic alike...the organization, notation, and illustrative examples are well thought out, making this a fun text to read." (IIE Transactions in Operations Engineering)

"...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)

From The Critics
Suitable for graduate students or working engineers, this introduction to generalized linear models (GLMs) features examples of GLMs as applied to a variety of settings. It reviews the types of problems that support the use of GLMs and provides an overviews of the fundamental concepts of the filed. The authors teach at the Virginia Polytechnic Institute and Arizona State University. Annotation c. Book News, Inc., Portland, OR
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Product Details

  • ISBN-13: 9780471355731
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 11/28/2001
  • Series: Wiley Series in Probability and Statistics Series , #356
  • Edition description: Older Edition
  • Edition number: 1
  • Pages: 364
  • Product dimensions: 0.88 (w) x 6.14 (h) x 9.21 (d)

Meet the Author

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.

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Table of Contents

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.

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