An L2-boosting algorithm for estimation of a regression function

Authors:
Adil M. Bagirov;Conny Clausen;Michael Kohler
Affiliations:
School of Information Technology and Mathematical Sciences, University of Ballarat, Ballarat Victoria, Australia;Department of Mathematics, Universität des Saarlandes, Saarbrücken, Germany;Department of Mathematics, Technische Universität Darmstadt, Darmstadt, Germany
Venue:
IEEE Transactions on Information Theory
Year:
2010

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Abstract

An L2-boosting algorithm for estimation of a regression function from random design is presented, which consists of fitting repeatedly a function from a fixed nonlinear function space to the residuals of the data by least squares and by defining the estimate as a linear combination of the resulting least squares estimates. Splitting of the sample is used to decide after how many iterations of smoothing of the residuals the algorithm terminates. The rate of convergence of the algorithm is analyzed in case of an unbounded response variable. The method is used to fit a sum of maxima of minima of linear functions to a given data set, and is compared with other nonparametric regression estimates using simulated data.