BAMLSS: Bayesian Additive Models for Location, Scale, and Shape (and Beyond)

DOI10.1080/10618600.2017.1407325OpenAlexW2593046461WikidataQ57263749 ScholiaQ57263749MaRDI QIDQ89961

Achim Zeileis, Nikolaus Umlauf, Nikolaus Umlauf, Nadja Klein, Nadja Klein

Publication date: 14 June 2018

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2017.1407325

zbMATH Keywords

softwareBUGSMCMCGAMLSSdistributional regression

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items

Spatial CART classification trees, Marginally Calibrated Deep Distributional Regression, Distributional (Single) Index Models, Two-parameter link functions, with applications to negative binomial, Weibull and quantile regression, A primer on Bayesian distributional regression, Bayesian Semiparametric Analysis of Multivariate Continuous Responses, With Variable Selection, Variable Selection Using a Smooth Information Criterion for Distributional Regression Models, Modular regression -- a Lego system for building structured additive distributional regression models with tensor product interactions, bamlss

Uses Software

• R
• WinBUGS
• BayesX
• SemiPar
• BUGS
• JAGS
• spectralGP
• GAMLSS
• Stan
• Formula
• bamlss

Cites Work

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• On the half-Cauchy prior for a global scale parameter
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• On Gibbs sampling for state space models
• Semiparametric Regression
• Boosting joint models for longitudinal and time‐to‐event data
• Flexible Bayesian additive joint models with an application to type 1 diabetes research
• Regression
• Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data
• A Mixed Model Approach for Geoadditive Hazard Regression
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• Generalized Additive Models for Location, Scale and Shape
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)