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A more efficient algorithm for Convex Nonparametric Least Squares

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Author Info

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

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.

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Bibliographic Info

Article provided by Elsevier in its journal European Journal of Operational Research.

Volume (Year): 227 (2013)
Issue (Month): 2 ()
Pages: 391-400

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Handle: RePEc:eee:ejores:v:227:y:2013:i:2:p:391-400

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Web page: http://www.elsevier.com/locate/eor

Related research

Keywords: Convex Nonparametric Least Squares; Frontier estimation; Productive efficiency analysis; Model reduction; Computational complexity;

References

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Cited by:
  1. Wang, Yongqiao & Wang, Shouyang & Dang, Chuangyin & Ge, Wenxiu, 2014. "Nonparametric quantile frontier estimation under shape restriction," European Journal of Operational Research, Elsevier, vol. 232(3), pages 671-678.
  2. Pang Du & Christopher F. Parmeter & Jeffrey S. Racine, 2012. "Nonparametric Kernel Regression with Multiple Predictors and Multiple Shape Constraints," Department of Economics Working Papers 2012-08, McMaster University.

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