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Generalized Linear Models and Extensions - Fourth Edition (Paperback, 4th edition)
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Generalized Linear Models and Extensions - Fourth Edition (Paperback, 4th edition)
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Generalized linear models (GLMs) extend linear regression to models
with a non-Gaussian, or even discrete, response. GLM theory is
predicated on the exponential family of distributions-a class so
rich that it includes the commonly used logit, probit, and Poisson
models. Although one can fit these models in Stata by using
specialized commands (for example, logit for logit models), fitting
them as GLMs with Stata's glm command offers some advantages. For
example, model diagnostics may be calculated and interpreted
similarly regardless of the assumed distribution. This text
thoroughly covers GLMs, both theoretically and computationally,
with an emphasis on Stata. The theory consists of showing how the
various GLMs are special cases of the exponential family, showing
general properties of this family of distributions, and showing the
derivation of maximum likelihood (ML) estimators and standard
errors. Hardin and Hilbe show how iteratively reweighted least
squares, another method of parameter estimation, are a consequence
of ML estimation using Fisher scoring.
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