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Generalized Linear Models: with Applications in Engineering and the Sciences
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  • Wiley

More About This Title Generalized Linear Models: with Applications in Engineering and the Sciences

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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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English

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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"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)

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