[Generalized Additive Models]: Rejoinder

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 ...

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...

Genome-wide generalized additive models

13 Chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq) is a widely used 14 approach to study protein-DNA interactions. To analyze ChIP-Seq data, practitioners are 15 required to combine tools based on different statistical assumptions and dedicated to spe16 cific applications such as calling protein occupancy peaks or testing for differential occu17 pancies. Here, we present Ge...

Generalized Additive Models with Spatio-temporal Data

Generalized additive models (GAMs) have been widely used. While the procedure for fitting a generalized additive model to independent data has been well established, not as much work has been done when the data are correlated. The currently available methods are not completely satisfactory in practice. A new approach is proposed to fit generalized additive models with spatio-temporal data via t...

Generalized Likelihood Ratio Tests for Additive Models