• Corpus ID: 220381015

qgam: Bayesian non-parametric quantile regression modelling in R

@article{Fasiolo2020qgamBN,
  title={qgam: Bayesian non-parametric quantile regression modelling in R},
  author={Matteo Fasiolo and Simon N. Wood and Margaux Zaffran and Raphael Nedellec and Yannig Goude},
  journal={arXiv: Methodology},
  year={2020}
}
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 modelled 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… 
Additive Functional Cox Model
Abstract We propose the additive functional Cox model to flexibly quantify the association between functional covariates and time to event data. The model extends the linear functional proportional
Fixed or random? On the reliability of mixed‐effects models for a small number of levels in grouping variables
TLDR
It is found that mixed-effects models already correctly estimate the variance of a random effect with only two groups and inferring the correct random effect structure is of high importance to get correct statistical properties, which allow for more informative choices about study design and data analysis and thus make ecological inference with mixed- effects models more robust for small number of groups.
Estimation of Extreme Quantiles of Global Horizontal Irradiance: A Comparative Analysis Using an Extremal Mixture Model and a Generalised Additive Extreme Value Model
Solar power poses challenges to the management of grid energy due to its intermittency. To have an optimal integration of solar power on the electricity grid it is important to have accurate
Accurate Epigenetic Aging in Bottlenose Dolphins (Tursiops truncatus), an Essential Step in the Conservation of at-Risk Dolphins
TLDR
This multi-tissue epigenetic age estimation clock uses 110 longitudinal samples from 34 Navy bottlenose dolphins to identify 195 cytosine-phosphate-guanine sites associated with chronological aging via cross-validation with one individual left out in each fold.

References

SHOWING 1-10 OF 37 REFERENCES
Fast Calibrated Additive Quantile Regression
Abstract We propose a novel framework for fitting additive quantile regression models, which provides well-calibrated inference about the conditional quantiles and fast automatic estimation of the
Bayesian semiparametric additive quantile regression
Quantile regression provides a convenient framework for analyzing the impact of covariates on the complete conditional distribution of a response variable instead of only the mean. While frequentist
BayesX: Analyzing Bayesian Structural Additive Regression Models
TLDR
The capabilities of the free software package BayesX for estimating regression models with structured additive predictor based on MCMC inference are described, which extends the capabilities of existing software for semiparametric regression included in S-PLUS, SAS, R or Stata.
Scaling the Gibbs posterior credible regions
TLDR
In the important quantile regres- sion problem, it is shown numerically that the Gibbs posterior credible intervals, with scale selected by the GPS algorithm, are exactly calibrated and are more efficient than those obtained via other Bayesian-like methods.
BAMLSS: Bayesian Additive Models for Location, Scale, and Shape (and Beyond)
TLDR
A unified modeling architecture for distributional GAMs is established that exploits distributions, estimation techniques, and model terms and it is shown that within this framework implementing algorithms for complex regression problems, as well as the integration of already existing software, is relatively straightforward.
Smoothing Parameter and Model Selection for General Smooth Models
TLDR
A general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates, which reduces the implementation of new model classes to the coding of some standard derivatives of the log-likelihood.
Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood
TLDR
It is demonstrated that a simple adjustment to the covariance matrix of the posterior chain leads to asymptotically valid posterior inference, and simulation results confirm that the posterior inference is an attractive alternative to other asymPTotic approximations in quantile regression, especially in the presence of censored data.
Generalized additive models for location, scale and shape
Summary.  A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes
A general framework for updating belief distributions
TLDR
It is argued that a valid update of a prior belief distribution to a posterior can be made for parameters which are connected to observations through a loss function rather than the traditional likelihood function, which is recovered as a special case.
Additive models for quantile regression: Model selection and confidence bandaids
Additive models for conditional quantile functions provide an attractive framework for nonparametric regression applications focused on features of the response beyond its central tendency. Total
...
...