bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)
Dublin Core
Title
bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)
Subject
backfitting, distributional regression, gradient boosting, MCMC, penalization,
probabilistic forecasting, R.
probabilistic forecasting, R.
Description
Over the last decades, the challenges in applied regression and in predictive modeling have been changing considerably: (1) More flexible regression model specifications
are needed as data sizes and available information are steadily increasing, consequently
demanding for more powerful computing infrastructure. (2) Full probabilistic models by
means of distributional regression – rather than predicting only some underlying individual quantities from the distributions such as means or expectations – is crucial in many
applications. (3) Availability of Bayesian inference has gained in importance both as an
appealing framework for regularizing or penalizing complex models and estimation therein
as well as a natural alternative to classical frequentist inference. However, while there has
been a lot of research on all three challenges and the development of corresponding software packages, a modular software implementation that allows to easily combine all three
aspects has not yet been available for the general framework of distributional regression.
To fill this gap, the R package bamlss is introduced for Bayesian additive models for
location, scale, and shape (and beyond) – with the name reflecting the most important
distributional quantities (among others) that can be modeled with the software. At the
core of the package are algorithms for highly-efficient Bayesian estimation and inference
that can be applied to generalized additive models or generalized additive models for location, scale, and shape, or more general distributional regression models. However, its
building blocks are designed as “Lego bricks” encompassing various distributions (exponential family, Cox, joint models, etc.), regression terms (linear, splines, random effects,
tensor products, spatial fields, etc.), and estimators (MCMC, backfitting, gradient boosting, lasso, etc.). It is demonstrated how these can be easily combined to make classical
models more flexible or to create new custom models for specific modeling challenges.
are needed as data sizes and available information are steadily increasing, consequently
demanding for more powerful computing infrastructure. (2) Full probabilistic models by
means of distributional regression – rather than predicting only some underlying individual quantities from the distributions such as means or expectations – is crucial in many
applications. (3) Availability of Bayesian inference has gained in importance both as an
appealing framework for regularizing or penalizing complex models and estimation therein
as well as a natural alternative to classical frequentist inference. However, while there has
been a lot of research on all three challenges and the development of corresponding software packages, a modular software implementation that allows to easily combine all three
aspects has not yet been available for the general framework of distributional regression.
To fill this gap, the R package bamlss is introduced for Bayesian additive models for
location, scale, and shape (and beyond) – with the name reflecting the most important
distributional quantities (among others) that can be modeled with the software. At the
core of the package are algorithms for highly-efficient Bayesian estimation and inference
that can be applied to generalized additive models or generalized additive models for location, scale, and shape, or more general distributional regression models. However, its
building blocks are designed as “Lego bricks” encompassing various distributions (exponential family, Cox, joint models, etc.), regression terms (linear, splines, random effects,
tensor products, spatial fields, etc.), and estimators (MCMC, backfitting, gradient boosting, lasso, etc.). It is demonstrated how these can be easily combined to make classical
models more flexible or to create new custom models for specific modeling challenges.
Creator
Nikolaus Umlauf
Source
https://www.jstatsoft.org/article/view/v100i04
Publisher
https://www.jstatsoft.org/article/view/v100i04
Date
November 2021
Contributor
Fajar bagus w
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Nikolaus Umlauf, “bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond),” Repository Horizon University Indonesia, accessed March 13, 2025, https://repository.horizon.ac.id/items/show/8217.