Fast Calibrated Additive Quantile Regression

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 smoothing parameters, for model structures as diverse as those usable with distributional generalized additive m...

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Bibliographic Details
Published in:	Journal of the American Statistical Association 2021-07, Vol.116 (535), p.1402-1412
Main Authors:	Fasiolo, Matteo, Wood, Simon N., Zaffran, Margaux, Nedellec, Raphaël, Goude, Yannig
Format:	Article
Language:	English
Subjects:	Additives Calibrated Bayes Calibration Electrical loads Electricity Electricity load forecasting Estimation Forecasting Generalized additive models Inference Learning Penalized regression splines Quantile regression Quantiles Regression analysis Regression models Smoothing Statistical analysis Statistical methods Statistics Uncertainty wood
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