Adaptively Partitioned Convex Nonparametric Least Squares


This research overcomes both the decreased accuracy of Convex Adaptive Partitioning on real production survey datasets and the cross-validation performance challenges of CNLS to create a robust and scalable adaptive partitioning-based convex regression method.

We discover that real production datasets often contain local monotonicity violations, which affect CAP’s ability to propose feasible basis region splits. Moreover, we note that CNLS’s error minimization strategy within the observed dataset results in poor estimations on unobserved firms due to over-fitting. We create a hybrid of both methods that preserves their most favorable properties at a small computational time expense. The paper summarizing this research is available on Arxiv.

Posted in Ongoing work.