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Fast stable direct fitting and smoothness selection for generalized additive models

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  • Simon N. Wood
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    Abstract

    Existing computationally efficient methods for penalized likelihood generalized additive model fitting employ iterative smoothness selection on working linear models (or working mixed models). Such schemes fail to converge for a non-negligible proportion of models, with failure being particularly frequent in the presence of concurvity. If smoothness selection is performed by optimizing 'whole model' criteria these problems disappear, but until now attempts to do this have employed finite-difference-based optimization schemes which are computationally inefficient and can suffer from false convergence. The paper develops the first computationally efficient method for direct generalized additive model smoothness selection. It is highly stable, but by careful structuring achieves a computational efficiency that leads, in simulations, to lower mean computation times than the schemes that are based on working model smoothness selection. The method also offers a reliable way of fitting generalized additive mixed models. Copyright (c) 2008 Royal Statistical Society.

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    Bibliographic Info

    Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series B (Statistical Methodology).

    Volume (Year): 70 (2008)
    Issue (Month): 3 ()
    Pages: 495-518

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    Handle: RePEc:bla:jorssb:v:70:y:2008:i:3:p:495-518

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    Cited by:
    1. Marra, Giampiero & Wood, Simon N., 2011. "Practical variable selection for generalized additive models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 55(7), pages 2372-2387, July.
    2. Roberto Basile & Luigi Benfratello & Davide Castellani, 2012. "Geoadditive models for regional count data: an application to industrial location," ERSA conference papers ersa12p83, European Regional Science Association.
    3. Sylvie Charlot & Riccardo Crescenzi & Antonio Musolesi, 2014. "Augmented and Unconstrained: revisiting the Regional Knowledge Production Function," SEEDS Working Papers, SEEDS, Sustainability Environmental Economics and Dynamics Studies 2414, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Aug 2014.
    4. Wang, Haiyan & Higgins, James & Blasi, Dale, 2010. "Distribution-free tests for no effect of treatment in heteroscedastic functional data under both weak and long range dependence," Statistics & Probability Letters, Elsevier, Elsevier, vol. 80(5-6), pages 390-402, March.
    5. Sara Bleninger, 2013. "Welfare Effects of the Euro Cash Changeover: Do Assumptions Really Matter?," SOEPpapers on Multidisciplinary Panel Data Research 577, DIW Berlin, The German Socio-Economic Panel (SOEP).
    6. Mazzanti, M. & Musolesi, A., 2013. "Nonlinearity, heterogeneity and unobserved effects in the carbon dioxide emissions-economic development relation for advanced countries," Working Papers, Grenoble Applied Economics Laboratory (GAEL) 2013-08, Grenoble Applied Economics Laboratory (GAEL).
    7. Mestekemper, Thomas & Kauermann, Göran & Smith, Michael S., 2013. "A comparison of periodic autoregressive and dynamic factor models in intraday energy demand forecasting," International Journal of Forecasting, Elsevier, Elsevier, vol. 29(1), pages 1-12.
    8. Strasak, Alexander M. & Umlauf, Nikolaus & Pfeiffer, Ruth M. & Lang, Stefan, 2011. "Comparing penalized splines and fractional polynomials for flexible modelling of the effects of continuous predictor variables," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 55(4), pages 1540-1551, April.
    9. Klein, Nadja & Denuit, Michel & Lang, Stefan & Kneib, Thomas, 2014. "Nonlife ratemaking and risk management with Bayesian generalized additive models for location, scale, and shape," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 225-249.
    10. Chiappori, Pierre-André & Gandhi, Amit & Salanié, Bernard & Salanié, François, 2012. "From Aggregate Betting Data to Individual Risk Preferences," IDEI Working Papers, Institut d'Économie Industrielle (IDEI), Toulouse 810, Institut d'Économie Industrielle (IDEI), Toulouse.
    11. Coussement, Kristof & Buckinx, Wouter, 2011. "A probability-mapping algorithm for calibrating the posterior probabilities: A direct marketing application," European Journal of Operational Research, Elsevier, Elsevier, vol. 214(3), pages 732-738, November.
    12. Tetsuya Tsurumi & Shunsuke Managi, 2010. "Decomposition of the environmental Kuznets curve: scale, technique, and composition effects," Environmental Economics and Policy Studies, Society for Environmental Economics and Policy Studies - SEEPS, Society for Environmental Economics and Policy Studies - SEEPS, vol. 11(1), pages 19-36, February.
    13. Kim, Young-Ju, 2011. "A comparative study of nonparametric estimation in Weibull regression: A penalized likelihood approach," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 55(4), pages 1884-1896, April.
    14. Tetsuya, Tsurumi & Shunsuke, Managi, 2011. "The Effect of Trade Openness on Deforestation: Empirical Analysis for 142 Countries," MPRA Paper 35805, University Library of Munich, Germany.
    15. Wang, Haiyan & Maldonado, Diego & Silwal, Sharad, 2011. "A nonparametric-test-based structural similarity measure for digital images," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 55(11), pages 2925-2936, November.
    16. Longhi, Christian & Musolesi, Antonio & Baumont, Catherine, 2014. "Modeling structural change in the European metropolitan areas during the process of economic integration," Economic Modelling, Elsevier, Elsevier, vol. 37(C), pages 395-407.

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