qgam: Bayesian Nonparametric Quantile Regression Modeling in R

qgam: Bayesian Nonparametric Quantile Regression Modeling in R

: Bayesian quantile regression, generalized additive models, regression splines, calibrated Bayes, fast Bayesian inference, R.

Generalized additive models (GAMs) are flexible non-linear regression models, which
can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R
package. While the GAM methods provided by mgcv are based on the assumption that
the response distribution is modeled parametrically, here we discuss more flexible methods
that do not entail any parametric assumption. In particular, this article introduces the
qgam package, which is an extension of mgcv providing fast calibrated Bayesian methods
for fitting quantile GAMs (QGAMs) in R. QGAMs are based on a smooth version of the
pinball loss of Koenker (2005), rather than on a likelihood function, hence jointly achieving
satisfactory accuracy of the quantile point estimates and coverage of the corresponding
credible intervals requires adopting the specialized Bayesian fitting framework of Fasiolo,
Wood, Zaffran, Nedellec, and Goude (2021b). Here we detail how this framework is
implemented in qgam and we provide examples illustrating how the package should be
used in practi

Matteo Fasiolo

https://www.jstatsoft.org/article/view/v100i09

University of Bristo

November 2020

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