by Simon Wood and
Matteo Fasiolo,
University Walk, Bristol, United Kingdom
This course devoted to GAM generalized linear regression models in which the linear predictor depends on smooth functions of predictor variables. It will be practically focused, starting with the basics of basis-penalty smoothing, before moving on to look at the representation and estimation of GAMs, with reference to the R package mgcv. The wide range of smooth functions that can form model components will then be covered, followed by material on model checking, selection and scalable computation. A second part of the course will open some perspectives about quantile GAMS, applications in electrical consumption context as well as in distributed environments.
1-day IRSDI-ECAS Course
October 19, 2017, 9:00-17:00
EDF Labs, Paris-Saclay, France