PLACE:

SPEAKER:
Yuhong Yang
Department of Statistics
Iowa State University

TITLE:
Adaptive Regression by Mixing

ABSTRACT:

Model  averaging provides an alternative to model selection. A computationally feasible algorithm ARM rooted in information theory is proposed to combine different regression models/methods. It produces a convex combination of the original estimators with data-dependent weights. For the determination of the weights, the data is split into two parts. The first one is used for estimation by each model or procedure and the accuracies of these estimators are assessed using the second half of the data. The accuracies are then used to assign the weights in a way such that a connection between function estimation and information theory ensures a desired theoretical capability of adaptation over different models and/or regression procedures. A simulation is conducted to show its effectiveness in both parametric and nonparametirc regression.
 
 

COFFEE: 3:45 p.m., 104 Snedecor Hall