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Smoothing: Local Regression Techniques

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  • Loader, Catherine
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    Abstract

    Smoothing methods attempt to find functional relationships between different measurements. As in the standard regression setting, the data is assumed to consist of measurements of a response variable, and one or more predictor variables. Standard regression techniques (Chapter ??) specify a functional form (such as a straight line) to describe the relation between the predictor and response variables. Smoothing methods take a more flexible approach, allowing the data points themselves to determine the form of the fitted curve. This article begins by describing several different approaches to smoothing, including kernel methods, local regression, spline methods and orthogonal series. A general theory of linear smoothing is presented, which allows us to develop methods for statistical inference, model diagnostics and choice of smoothing parameters. The theory is then extended to more general settings, including multivariate smoothing and likelihood models. --

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    Bibliographic Info

    Paper provided by Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) in its series Papers with number 2004,12.

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    Date of creation: 2004
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    Handle: RePEc:zbw:caseps:200412

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    1. Hardle, W., 1992. "Applied Nonparametric Methods," Papers 9206, Tilburg - Center for Economic Research.
    2. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, April.
    3. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, April.
    4. Oliver LINTON, . "Applied nonparametric methods," Statistic und Oekonometrie 9312, Humboldt Universitaet Berlin.
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    Cited by:
    1. Essama-Nssah, B., 2006. "Propensity score matching and policy impact analysis - a demonstration in EViews," Policy Research Working Paper Series 3877, The World Bank.
    2. Conti, Pier Luigi & Marella, Daniela & Scanu, Mauro, 2008. "Evaluation of matching noise for imputation techniques based on nonparametric local linear regression estimators," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 354-365, December.
    3. Stefan Hupfeld, 2011. "Non-monotonicity in the longevity–income relationship," Journal of Population Economics, Springer, vol. 24(1), pages 191-211, January.

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