Introduction to modern Generalized Additive Models in R

Organised by

Royal Statistical Society

Presenter

Dr Matteo Fasiolo & Prof. Simon Wood

Date

15/06/2021

Venue

Online

Map

View in Google Maps  (EC1Y 8LX)

Contact

training@rss.org.uk

Description

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.


Learning Outcomes

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.

Topics Covered

  • model building, inference and fitting methods 
  • key Bayes empirical smoothing theory
  • types of smooth and random effects
  • visual methods and diagnostics 
  • Generalized Additive Models for Location Scale and Shape
  • Quantile GAMs
  • GAM modelling in R


Target Audience

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.


Knowledge Assumed

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.

Level

Advanced (specialised prior knowledge)

Cost

£377 - £522 (inc. VAT)

Website and registration

Region

Greater London

Keywords

Quantitative Data Handling and Data Analysis

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