Tag: distributional
bamlss: A Lego toolbox for flexible Bayesian regression
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Modular R tools for Bayesian regression are provided by bamlss: From classic MCMC-based GLMs and GAMs to distributional models using the lasso or gradient boosting. Read more ›
Minimum CRPS vs. maximum likelihood
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In a new paper in Monthly Weather Review, minimum CRPS and maximum likelihood estimation are compared for fitting heteroscedastic (or nonhomogenous) regression models under different response distributions. Minimum CRPS is more robust to distributional misspecification while maximum likelihood is slightly more efficient under correct specification. An R implementation is available in the crch package. Read more ›
Thunderstorm forecasting with GAMs
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Boosted binary generalized additive models (GAMs) with stability selection and corresponding MCMC-based credibility intervals are discussed in a new MWR paper as a probabilistic forecasting method for the occurrence of thunderstorms. Read more ›
Distributional regression forests on arXiv
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Distributional regression trees and forests provide flexible data-driven probabilistic forecasts by blending distributional models (for location, scale, shape, and beyond) with regression trees and random forests. Accompanied by the R package disttree. Read more ›
BAMLSS paper published in JCGS
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Bayesian additive models for location, scale, and shape (and beyond) provide a general framework for distributional regression. Accompanied by the R package bamlss. Read more ›