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Bayesian Data Analysis, Third Edition, 3rd Edition by Donald B. Rubin, Aki Vehtari, David B. Dunson, Hal S. Stern, John B. Carlin, Andrew Gelman

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Chapter 19

Parametric nonlinear models

The linear regression approach of Part IV suggests a presentation of statistical models in menu form, with a set of possible distributions for the response variable, a set of transformations to facilitate the use of those distributions, and the ability to include information in the form of linear predictors. In a generalized linear model, the expected value of y is a nonlinear function of the linear predictor: E(yX, β) = g-1(Xβ). Robust (Chapter 17) and mixture models (Chapter 22) generalize these by adding ...

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