Generalized Additive Models: An Introduction withR

Functional Generalized Additive Models.

We introduce the functional generalized additive model (FGAM), a novel regression model for association studies between a scalar response and a functional predictor. We model the link-transformed mean response as the integral with respect to t of F{X(t), t} where F(·,·) is an unknown regression function and X(t) is a functional covariate. Rather than having an additive model in a finite number ...

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An Introduction to Generalized Linear Models

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An Introduction to Generalized Linear Mixed Models

Linear mixed models provide a powerful means of predicting breeding values. However, for many traits of economic importance the assumptions of linear responses, constant variance, and normality are questionable. Generalized linear mixed models provide a means of modeling these deviations from the usual linear mixed model. This paper will examine what constitutes a generalized linear mixed model...

Markov-switching generalized additive models

We consider Markov-switching regression models, i.e. models for time series regression analyses where the functional relationship between covariates and response is subject to regime switching controlled by an unobservable Markov chain. Building on the powerful hidden Markov model machinery and the methods for penalized B-splines routinely used in regression analyses, we develop a framework for...