Generalized Linear ModelsThe success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and ot |
Contents
An outline of generalized linear models | 24 |
Models for continuous data with constant variance | 48 |
Binary data | 98 |
Models for polytomous data | 149 |
Loglinear models | 193 |
Conditional likelihoods | 245 |
Models with constant coefficient of variation | 285 |
Quasilikelihood functions | 323 |
Model checking | 391 |
Models for survival data | 419 |
Components of dispersion | 432 |
Further topics | 455 |
Appendices | 469 |
References | 479 |
| 500 | |
| 506 | |
Joint modelling of mean and dispersion | 357 |
Models with additional nonlinear parameters | 372 |
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Common terms and phrases
algorithm analysis approximation assumption asymptotic B₁ Biometrika bivariate Chapter coefficient of variation components computed conditional distribution consider constant contrasts corresponding covariance matrix cumulants degrees of freedom density depends derivative deviance discussed dispersion parameter effect estimating equations estimating functions example exponential factor Fisher information Fisher information matrix fitted values gamma distribution given independent information matrix interaction inverse iteration J. R. Statist levels likelihood function linear logistic model linear models linear predictor link function log likelihood log-linear model logistic model marginal maximum maximum-likelihood estimate mean method model formula multinomial Nelder non-linear nuisance parameters observed obtained over-dispersion parameter estimates plot Poisson Poisson distribution probabilities quadratic quasi-likelihood random variables regression models residual response sample scale score score statistic Show standard errors sum of squares Suppose Table totals transformation usually variance function vector weights Y₁ zero