Structured Additive Regression Models: An R Interface to BayesX

@article{Umlauf2015StructuredAR,
  title={Structured Additive Regression Models: An R Interface to BayesX},
  author={Nikolaus Umlauf and Daniel Adler and Thomas Kneib and Stefan Lang and Achim Zeileis},
  journal={Journal of Statistical Software},
  year={2015},
  volume={63},
  pages={1-46}
}
Structured additive regression (STAR) models provide a flexible framework for modeling possible nonlinear effects of covariates: They contain the well established frameworks of generalized linear models and generalized additive models as special cases but also allow a wider class of effects, e.g., for geographical or spatio-temporal data, allowing for specification of complex and realistic models. BayesX is standalone software package providing software for fitting general class of STAR models… Expand
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References

SHOWING 1-10 OF 135 REFERENCES
Generalized structured additive regression based on Bayesian P-splines
TLDR
A Bayesian version of GAM's and extensions to generalized structured additive regression (STAR) are developed and for the first time, Bayesian semiparametric inference for the widely used multinomial logit model is presented. Expand
BayesX: Analyzing Bayesian Structural Additive Regression Models
There has been much recent interest in Bayesian inference for generalized additive and related models. The increasing popularity of Bayesian methods for these and other model classes is mainly causedExpand
Multilevel structured additive regression
TLDR
An in depth description of several highly efficient sampling schemes that allow to estimate complex models with several hierarchy levels and a large number of observations within a couple of minutes (often even seconds) is provided. Expand
Mixed model based inference in structured additive regression
TLDR
Several possibilities to model non-standard covariate effects such as nonlinear effects of continuous covariates, temporal effects, spatial effects, interaction effects or unobserved heterogeneity are reviewed and embedded in the general framework of structured additive regression. Expand
Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additiveExpand
Generalized Additive Models
Likelihood-based regression models such as the normal linear regression model and the linear logistic model, assume a linear (or some other parametric) form for the covariates Xlt X2, ■•-, Xp. WeExpand
Structured additive regression for categorical space-time data: a mixed model approach.
Motivated by a space-time study on forest health with damage state of trees as the response, we propose a general class of structured additive regression models for categorical responses, allowingExpand
Bayesian inference for generalized additive mixed models based on Markov random field priors
Most regression problems in practice require flexible semiparametric forms of the predictor for modelling the dependence of responses on covariates. Moreover, it is often necessary to add randomExpand
PENALIZED STRUCTURED ADDITIVE REGRESSION FOR SPACE-TIME DATA: A BAYESIAN PERSPECTIVE
We propose extensions of penalized spline generalized additive models for analyzing space-time regression data and study them from a Bayesian per- spective. Non-linear effects of continuousExpand
Inference in generalized additive mixed modelsby using smoothing splines
Generalized additive mixed models are proposed for overdispersed and correlated data, which arise frequently in studies involving clustered, hierarchical and spatial designs. This class of modelsExpand
...
1
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4
5
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