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• W2912572111 abstract "This chapter considers regression methods, starting out with the canonical normal linear regression model, and alternative prior schemes on the coefficients and residual variance. It considers model checking using MCMC techniques of the assumptions of normal linear regression. Generalised linear models (GLMs) are discussed, including binary, Poisson, ordinal and multinomial regression, as well as the use of augmented data priors for binary and Poisson regression. Multinomial and nested logit schemes are compared. Predictor selection (including reversible jump methods) and model choice in both linear regression and GLMs is considered and worked examples included. Bayes factor calculations for regression models (in R and BUGS) are included." @default.
• W2912572111 created "2019-02-21" @default.
• W2912572111 creator A5001636587 @default.
• W2912572111 date "2014-06-06" @default.
• W2912572111 modified "2023-10-14" @default.
• W2912572111 title "Regression techniques" @default.
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• W2912572111 doi "https://doi.org/10.1002/9781118895047.ch3" @default.
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