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Estimation of generalized additive models

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  • Burman, Prabir

Abstract

Spline estimation of generalized additive models is considered here. Cross-validation is used as a criterion of model estimation. Some computationally simpler approximations to cross-validation are given.

Suggested Citation

  • Burman, Prabir, 1990. "Estimation of generalized additive models," Journal of Multivariate Analysis, Elsevier, vol. 32(2), pages 230-255, February.
    • Handle: RePEc:eee:jmvana:v:32:y:1990:i:2:p:230-255
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    Citations

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    Cited by:

    1. Hegland, Markus & McIntosh, Ian & Turlach, Berwin A., 1999. "A parallel solver for generalised additive models," Computational Statistics & Data Analysis, Elsevier, vol. 31(4), pages 377-396, October.
    2. Moral-Arce, Ignacio & Rodríguez-Póo, Juan M. & Sperlich, Stefan, 2011. "Low dimensional semiparametric estimation in a censored regression model," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 118-129, January.
    3. Siyu Zeng & Li luo & Fang Chen & Yue Li & Mei Chen & Xiaozhou He, 2021. "Association of outdoor air pollution with the medical expense of ischemic stroke: The case study of an industrial city in western China," International Journal of Health Planning and Management, Wiley Blackwell, vol. 36(3), pages 715-728, May.
    4. Schimek, Michael G. & Turlach, Berwin A., 1998. "Additive and generalized additive models: A survey," SFB 373 Discussion Papers 1998,97, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    5. Kamiar Rahnama Rad & Arian Maleki, 2020. "A scalable estimate of the out‐of‐sample prediction error via approximate leave‐one‐out cross‐validation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 965-996, September.

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