Date: 2014-03-14

Time: 15:30-16:30

Location: BURN 1205

Abstract:

In this talk, I will present extensions of tree-based and random forest methods for the case of clustered data. The proposed methods can handle unbalanced clusters, allows observations within clusters to be splitted, and can incorporate random effects and observation-level covariates. The basic tree-building algorithm for a continuous outcome is implemented using standard algorithms within the framework of the EM algorithm. The extension to other types of outcomes (e.g., binary, count) uses the penalized quasi-likelihood (PQL) method for the estimation and the EM algorithm for the computation. Simulation results show that the proposed methods provides substantial improvements over standard trees and forests when the random effects are non negligible. The use of the method will be illustrated with real data sets.

Speaker

Denis Larocque is a professor of statistics at HEC Montreal.