Loading…
This event has ended. Visit the official site or create your own event on Sched.
Click here to return to main conference site. For a one page, printable overview of the schedule, see this.
View analytic
Thursday, June 30 • 1:10pm - 1:15pm
Scalable semi-parametric regression with mgcv package and bam procedure

Log in to save this to your schedule and see who's attending!

The mgcv package proposes a flexible framework for fitting Generalized additive regression models.nHowever, classical fitting procedure can be computationally intensive. The bam procedure brings about substantial computational savings, by adapting standard fitting algorithms to provide scalability to "big" data sets [1].nIn particular, parallel approaches have been implemented to exploit multi-core architectures and to reduce memory footprint. We will present the results of joint work between the University of Bristol and one R&D team of EDF (the major French electrical utility). The new bam procedure has been used to model electrical load time series freely availablenfrom the NYC ISO. The new optimization algorithm (FREML) of bam allows the user to fit scalable additive models on data up to millions of observations and thousands of estimated parameters.

Moderators
avatar for Julie Josse

Julie Josse

INRIA/Agrocampus

Speakers

Thursday June 30, 2016 1:10pm - 1:15pm
SIEPR 130 366 Galvez St, Stanford, CA 94305

Attendees (38)