Multivariate convex regression with adaptive partitioning

Published

Journal Article

We propose a new, nonparametric method for multivariate regression subject to convexity or concavity constraints on the response function. Convexity constraints are common in economics, statistics, operations research, financial engineering and optimization, but there is currently no multivariate method that is stable and computationally feasible for more than a few thousand observations. We introduce convex adaptive partitioning (CAP), which creates a globally convex regression model from locally linear estimates fit on adaptively selected covariate partitions. CAP is a computationally efficient, consistent method for convex regression. We demonstrate empirical performance by comparing the performance of CAP to other shape-constrained and unconstrained regression methods for predicting weekly wages and value function approximation for pricing American basket options. © 2013 Lauren A. Hannah and David B. Dunson.

Duke Authors

Cited Authors

  • Hannah, LA; Dunson, DB

Published Date

  • November 1, 2013

Published In

Volume / Issue

  • 14 /

Start / End Page

  • 3153 - 3188

Electronic International Standard Serial Number (EISSN)

  • 1533-7928

International Standard Serial Number (ISSN)

  • 1532-4435

Citation Source

  • Scopus