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Computational Optimization and Applications

, Volume 45, Issue 1, pp 159–179 | Cite as

An algorithm for the estimation of a regression function by continuous piecewise linear functions

  • Adil Bagirov
  • Conny Clausen
  • Michael Kohler
Article

Abstract

The problem of the estimation of a regression function by continuous piecewise linear functions is formulated as a nonconvex, nonsmooth optimization problem. Estimates are defined by minimization of the empirical L 2 risk over a class of functions, which are defined as maxima of minima of linear functions. An algorithm for finding continuous piecewise linear functions is presented. We observe that the objective function in the optimization problem is semismooth, quasidifferentiable and piecewise partially separable. The use of these properties allow us to design an efficient algorithm for approximation of subgradients of the objective function and to apply the discrete gradient method for its minimization. We present computational results with some simulated data and compare the new estimator with a number of existing ones.

Keywords

Nonsmooth optimization Nonparametric regression Subdifferential Semismooth functions 

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.School of Information Technology and Mathematical SciencesUniversity of BallaratBallaratAustralia
  2. 2.Department of MathematicsUniversität des SaarlandesSaarbrückenGermany
  3. 3.Department of MathematicsTechnische Universität DarmstadtDarmstadtGermany

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