A more efficient algorithm for Convex Nonparametric Least Squares

Chia Yen Lee, Andrew L. Johnson, Erick Moreno-Centeno, Timo Kuosmanen

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)

Abstract

Convex Nonparametric Least Squares (CNLSs) is a nonparametric regression method that does not require a priori specification of the functional form. The CNLS problem is solved by mathematical programming techniques; however, since the CNLS problem size grows quadratically as a function of the number of observations, standard quadratic programming (QP) and Nonlinear Programming (NLP) algorithms are inadequate for handling large samples, and the computational burdens become significant even for relatively small samples. This study proposes a generic algorithm that improves the computational performance in small samples and is able to solve problems that are currently unattainable. A Monte Carlo simulation is performed to evaluate the performance of six variants of the proposed algorithm. These experimental results indicate that the most effective variant can be identified given the sample size and the dimensionality. The computational benefits of the new algorithm are demonstrated by an empirical application that proved insurmountable for the standard QP and NLP algorithms.

Original languageEnglish
Pages (from-to)391-400
Number of pages10
JournalEuropean Journal of Operational Research
Volume227
Issue number2
DOIs
Publication statusPublished - 2013 Jun 1

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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