Germán Rodríguez

Generalized Linear Models
Princeton University
The material of this course is covered in detail in the lecture notes. The following references are pointers to more detailed supplementary discussions, classified by subject.

Weisberg, S. (2013).
*Applied Linear Regression*, 4th Edition.
New York: John Wiley and Sons.
My favorite regression text, with good coverage of the basics and
a lucid presentation of regression diagnostics.

Fox, J. (2015).
*Applied Regression Analysis and Generalized Linear Models*, 3rd Edition.
Thousand Oaks: Sage Publications.
A nice discussion aimed at sociologists and other social scientists,
with plenty of examples. The second edition expanded the treatment of
generalized linear models in Chapters 14 and 15, a change reflected in a new title.
A third edition was published recently.

McCullagh, P. and Nelder, J.A. (1989).
*Generalized Linear Models*, 2nd Edition.
London: Chapman and Hall.
The "bible" on generalized linear models, absolutely brilliant but
rather on the terse side. Aimed at the more advanced statistics student.

Hardin, J. and Hilbe, J. (2012).
*Generalized Linear Models and Extensions*, 3rd Edition.
College Station, Texas: Stata Press.
A more applied book covering the fundamentals and including worked out
analyses using Stata.

Long, J. S. and Freese, J (2014).
*Regression Models for Categorical Dependent Variables Using Stata*, 4th Edition.
College Station, Texas: Stata Press.
A nice discussion of models for binary, ordinal, nominal, and count
data with emphasis on post-estimation aids to interpretation and
effective use of Stata.

Gelman, A. and Hill, J. (2006).
*Data Analysis Using Regression and Multilevel/Hierarchical Models*.
A very nice and accessible discussion of regression modeling with
extensions into causal inference and multilevel models, with a
Bayesian flavor and examples using R and WinBugs.

Wooldridge, J. M. (2010).
*Econometric Analysis of Cross Section and Panel Data*, 2nd Edition.
Cambridge, MA: The MIT Press.
A comprehensive treatise that will be particularly useful to economists,
covering the models for cross-sectional data discussed in the course
as well as extensions for longitudinal data.

Powers, D. A. and Xie, Y. (2008).
*Statistical Methods for Categorical Data Analysis*. 2nd Edition.
New York: Academic Press.
Covers a wider range of models that you might think from the title,
and includes many examples, in a discussion aimed at social scientists.

Agresti, A. (2012).
*Categorical Data Analysis*, 3rd Edition.
New York: John Wiley and Sons.
An excellent book on models for contingency tables.

Cameron, A. C. and Trivedi, P. K. (2013).
*Regression Analysis of Count Data*, 2nd Edition.
Cambridge: Cambridge University Press.
A comprehensive discussion of Poisson regression,
with extensions to negative binomial and related models.

Cox, D.R. and Oakes, D. (1984).
*Analysis of Survival Data*.
London: Chapman and Hall.
An excellent book on survival analysis, brief and to the point.
The first author is the statistician who gave us proportional hazard models.

Hosmer, D.W. and Lemeshow, S. (2013).
*Applied Logistic Regression*, 3rd Edition.
New York: John Wiley and Sons.
A more detailed discussion of logistic regression models with applications.

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