Optimal response modeling is studied using logistic regression, random forests, and I* algorithm of building tuned regressions. Transforming categorical variables into either WOE (weight of evidence) or probability of response coupled with equal frequency binning of size 10 results in improved...

This paper proposes a new framework for estimating instrumental variable (IV) quantile models. The first part of our proposal can be cast as a mixed integer linear program (MILP), which allows us to capitalize on recent progress in mixed integer optimization. The computational advantage of the...

We study an approach to regression that we call objective-aligned fitting, which is applicable when the regression model is used to predict uncertain parameters of some objective problem. Rather than minimizing a typical loss function, such as squared error, we approximately minimize the...