TITLE: Directed Regression

SPEAKER: Professor Ben Van Roy

ABSTRACT:

When used to guide decisions, linear regression analysis typically 
involves estimation of regression coefficients via ordinary least 
squares and their subsequent use in an optimization problem. When 
features are not chosen perfectly, it can be beneficial to account for 
the decision objective when computing regression coefficients. 
Empirical optimization does so but sacrifices performance when 
features are well-chosen or training data are insufficient. We propose 
directed regression, an efficient algorithm that combines merits of 
ordinary least squares and empirical optimization. We demonstrate 
through computational studies that directed regression generates 
performance gains over either alternative. We also develop a theory 
that motivates the algorithm.