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Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen, Technische Universität Dortmund >
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http://hdl.handle.net/10419/36620
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| Title: | | Convergence analysis of generalized iteratively reweighted least squares algorithms on convex function spaces  |
| Authors: | | Bissantz, Nicolai Dümbgen, Lutz Munk, Axel Stratmann, Bernd |
| Issue Date: | | 2008 |
| Series/Report no.: | | Technical Report // Sonderforschungsbereich 475, Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund 2008,25 |
| Abstract: | | The computation of robust regression estimates often relies on minimization of a convex functional on a convex set. In this paper we discuss a general technique for a large class of convex functionals to compute the minimizers iteratively which is closely related to majorization-minimization algorithms. Our approach is based on a quadratic approximation of the functional to be minimized and includes the iteratively reweighted least squares algorithm as a special case. We prove convergence on convex function spaces for general coercive and convex functionals F and derive geometric convergence in certain unconstrained settings. The algorithm is applied to TV penalized quantile regression and is compared with a step size corrected Newton-Raphson algorithm. It is found that typically in the first steps the iteratively reweighted least squares algorithm performs significantly better, whereas the Newton type method outpaces the former only after many iterations. Finally, in the setting of bivariate regression with unimodality constraints we illustrate how this algorithm allows to utilize highly efficient algorithms for special quadratic programs in more complex settings. |
| Subjects: | | regression analysis monotone regression quantile regression shape constraints L1 regression nonparametric regression total variation semi-norm reweighted least squares Fermat's problem convex approximation quadratic approximation pool adjacent violators algorithm |
| Document Type: | | Working Paper |
| Appears in Collections: | | Technical Reports, SFB 475, TU Dortmund
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