### Fast calibrated additive quantile regression

Statistics Seminar

20th April 2018, 3:30 pm – 4:30 pm

Main Maths Building, SM3

Generalized Additive Models (GAMs) are an extension of Generalized Linear Models (GLMs), which

allow the inclusion of random effects, complex non-linear effects (built using spline bases) and,

more recently, response distributions outside the exponential family. In this talk I will describe a new

framework for fitting quantile GAMs, which are based on the pinball loss, rather than on a

probabilistic response distribution. The new framework selects both the smoothing parameter and

the so-called "learning-rate" automatically and efficiently, and provides posterior credible intervals that

are approximately calibrated in a frequentist sense. I will illustrate the new methods in the context of

electricity demand forecasting, where they provide a predictive performance that is competitive

with that of Gradient Boosting (GB), but at a fraction of GB's computational cost.

*Organiser*: Song Liu

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