Generalized Linear Models

McCullagh, P. (1989). Generalized Linear Models. Chapman & Hall.

Type

Book

Intellectual base

Review or monograph

Year

1989

DOI

10.1007/978-1-4899-3242-6

ISBN

9780412317606

DOI registrant

Springer Nature

Link

https://doi.org/10.1007/978-1-4899-3242-6

Cited from

statistics_experimental_design

Cited by

4 citations across 4 artifacts.

Primes

• Distributional Assumption
  • The distributional assumption is part of the specification but not synonymous with it, as McCullagh and Nelder (1989) make explicit in decomposing generalized linear models into random component, systematic component, and link function.

Domain-specific

• Binomial regression
  • Binomial regression models a count \(Y_i\) with \(Y_i ∼ Binomial(n_i,p_i)\) and relates its success probability to predictors through a link such as \(g(p_i) = x_i^Tβ\).
• Hierarchical generalized linear model
  • HGLMs use h-likelihood or related hierarchical specifications to estimate fixed effects, random effects, dispersion, and dependence for nested, repeated, spatial, survival, count, and overdispersed data.
• Variance function
  • A wrong variance function misstates precision even when the linear predictor for the mean is correctly specified.

Verification

Does it exist? Confirmed. This work's DOI resolves to a registered record, which fixes its identity. That is all it fixes.

Does it back the claim? Not recorded. Neither this nor any other of the 4 citations of this work carries a recorded support check.

Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.

See how references were verified.

Registry ID ref:a9c92b2c92bf · see in the full table