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Convergence of the backfitting algorithm for additive models

Part of: Linear inference, regression Nonparametric inference Numerical approximation and computational geometry

Published online by Cambridge University Press:  09 April 2009

Craig F. Ansley  and
Robert Kohn
Craig F. Ansley
Affiliation:
Department of Accounting and Finance, University of Auckland, Private Bag, Auckland, New Zealand
Robert Kohn
Affiliation:
Australian Graduate School of Management, University of NSW, Kensington, New South Wales, Australia
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Abstract

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The backfitting algorithm is an iterative procedure for fitting additive models in which, at each step, one component is estimated keeping the other components fixed, the algorithm proceeding component by component and iterating until convergence. Convergence of the algorithm has been studied by Buja, Hastie, and Tibshirani (1989). We give a simple, but more general, geometric proof of the convergence of the backfitting algorithm when the additive components are estimated by penalized least squares. Our treatment covers spline smoothers and structural time series models, and we give a full discussion of the degenerate case. Our proof is based on Halperin's (1962) generalization of von Neumann's alternating projection theorem.

Keywords

Additive modelalternating projectionconcurvitynonparametric regressionpenalized least squarespartial splinetrendseasonal.

MSC classification

Secondary: 62G05: Estimation 62J02: General nonlinear regression 65D10: Smoothing, curve fitting

Information

Type
Research Article
Information
Journal of the Australian Mathematical Society , Volume 57 , Issue 3 , December 1994 , pp. 316 - 329
Copyright
Copyright © Australian Mathematical Society 1994

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