A penalized method for multivariate concave least squares with application to productivity analysis
Abstract
We propose a penalized method for the least squares estimator of a multivariate concave regression function. This estimator is formulated as a quadratic programming (QP) problem with constraints, where n is the number of observations. Computing such an estimator is a very time-consuming task, and the computational burden rises dramatically as the number of observations increases. By introducing a quadratic penalty function, we reformulate the concave least squares estimator as a QP with only non-negativity constraints. This reformulation can be adapted for estimating variants of shape restricted least squares, i.e. the monotonic-concave/convex least squares. The experimental results and an empirical study show that the reformulated problem and its dual are solved significantly faster than the original problem. The Matlab and R codes for implementing the penalized problems are provided in the paper.
Cite
@article{arxiv.1608.03393,
title = {A penalized method for multivariate concave least squares with application to productivity analysis},
author = {Abolfazl Keshvari},
journal= {arXiv preprint arXiv:1608.03393},
year = {2016}
}