Introduction to modern Generalised Additive Models in R
|Royal Statistical Society|
Dr. Matteo Fasiolo and Prof. Simon Wood
12 Errol Street, London
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Generalized Additive Models (GAMs) models are an extension of traditional regression models, and have proved to be highly useful for both predictive and inferential purposes in a variety of scientific and commercial applications. One reason behind the popularity of GAMs is that they strike a balance between flexibility and interpretability, while being able to handle large data sets. The thought part of the course will provide an overview of GAM theory, methods and software, while the hands-on sessions will make sure that the attendees will be ready to start doing GAM modelling in R as soon as the course is over.
Attendees with no experience in GAM modelling will get an understanding of what GAM models are, when are they useful and how can they be used to perform statistical analysis, for inferential or predictive purposes. Attendees who have some experience with GAMs will learn about the new Big Data and visual GAM methods, as well as about GAMLSS models and quantile GAMs.
The target audience are practictioners, either in industry or academia, interested in learning new powerful statistical methods, which can be used in a wide variety of applications such as rainfall modelling, electricity demand forecasting, survival analysis and air pollution modelling to name a few.
Attendees should have some background on (linear) regression modelling, and a good understanding of fundamental statistical concepts such as probability densities, quantiles, etc. Some basic proficiency with R (eg. loading data, accessing data frames, basic use of the lm() function), at a level equivalent to a couple of days of self-study, is also assumed.
Attendees will need to bring a laptop with a recent version of R installed. Prior to the course attendees will be asked to install some additional R packages.
Advanced (specialised prior knowledge)
£377.00 - £522.00 (inc. VAT)
Website and registration
Quantitative Data Handling and Data Analysis, Generalized Additive Models , GAM
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