Markov-switching generalized additive models

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...

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 Models

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...

Explicit-Duration Markov Switching Models

Markov switching models (MSMs) are probabilistic models that employ multiple sets of parameters to describe different dynamic regimes that a time series may exhibit at different periods of time. The switching mechanism between regimes is controlled by unobserved random variables that form a first-order Markov chain. Explicit-duration MSMs contain additional variables that explicitly model the d...