Projects per year
Abstract
This paper applies conformal prediction to derive predictive distributions that are valid under a nonparametric assumption. Namely, we introduce and explore predictive distribution functions that always satisfy a natural property of validity in terms of guaranteed coverage for IID observations. The focus is on a prediction algorithm that we call the Least Squares Prediction Machine (LSPM). The LSPM generalizes the classical DempsterHill predictive distributions to nonparametric regression problems. If the standard parametric assumptions for Least Squares linear regression hold, the LSPM is as efficient as the DempsterHill procedure, in a natural sense. And if those parametric assumptions fail, the LSPM is still valid, provided the observations are IID.
Original language  English 

Pages (fromto)  445474 
Number of pages  30 
Journal  Machine Learning 
Volume  108 
Early online date  17 Aug 2018 
DOIs  
Publication status  Published  15 Mar 2019 
Keywords
 Conformal prediction
 Least Squares
 Predictive distributions
 Regression
 Nonparametric regression
Projects
 1 Finished

Mining the Network Behaviour of Bots
Cavallaro, L., Gammerman, A., Vovk, V., Shanahan, H. & Luo, Z.
Eng & Phys Sci Res Council EPSRC
16/06/13 → 17/04/17
Project: Research