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Smoothing with Mixed Model Software

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  • Long Ngo
  • Matthew P. Wand
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    Abstract

    Smoothing methods that use basis functions with penalization can be formulated as fits in a mixed model framework. One of the major benefits is that software for mixed model analysis can be used for smoothing. We illustrate this for several smoothing models such as additive and varying coefficient models for both S-PLUS and SAS software. Code for each of the illustrations is available on the Internet.

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    File URL: http://www.jstatsoft.org/v09/i01/paper
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    Bibliographic Info

    Article provided by American Statistical Association in its journal Journal of Statistical Software.

    Volume (Year): 09 ()
    Issue (Month): i01 ()
    Pages:

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    Handle: RePEc:jss:jstsof:09:i01

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    Web page: http://www.jstatsoft.org/

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    1. repec:wop:humbsf:2000-50 is not listed on IDEAS
    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. E. E. Kammann & M. P. Wand, 2003. "Geoadditive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 1-18.
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    Cited by:
    1. Ciprian M. Crainiceanu & David Ruppert & Matthew P. Wand, . "Bayesian Analysis for Penalized Spline Regression Using WinBUGS," Journal of Statistical Software, American Statistical Association, vol. 14(i14).
    2. Paciorek, Christopher J., 2007. "Computational techniques for spatial logistic regression with large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3631-3653, May.
    3. Michael Wegener & Göran Kauermann, 2008. "Examining heterogeneity in implied equity risk premium using penalized splines," AStA Advances in Statistical Analysis, Springer, vol. 92(1), pages 35-56, February.
    4. Wunder, Christoph & Wiencierz, Andrea & Schwarze, Johannes & Küchenhoff, Helmut & Kleyer, Sara & Bleninger, Philipp, 2009. "Well-Being over the Life Span: Semiparametric Evidence from British and German Longitudinal Data," IZA Discussion Papers 4155, Institute for the Study of Labor (IZA).
    5. Wunder, Christoph & Schwarze, Johannes, 2009. "Is Posner Right? An Empirical Test of the Posner Argument for Transferring Health Spending from Old Women to Old Men," IZA Discussion Papers 4485, Institute for the Study of Labor (IZA).
    6. Bellio, Ruggero & Grassetti, Luca, 2011. "Semiparametric stochastic frontier models for clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 71-83, January.
    7. Antoniadis, Anestis & Sapatinas, Theofanis, 2007. "Estimation and inference in functional mixed-effects models," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4793-4813, June.
    8. Yu Yue & Paul Speckman & Dongchu Sun, 2012. "Priors for Bayesian adaptive spline smoothing," Annals of the Institute of Statistical Mathematics, Springer, vol. 64(3), pages 577-613, June.
    9. Stremersch, S. & Lemmens, A., 2008. "Sales Growth of New Pharmaceuticals Across the Globe: The Role of Regulatory Regimes," ERIM Report Series Research in Management ERS-2008-026-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
    10. Haonan Wang & Hari Iyer, 2007. "Application of local linear embedding to nonlinear exploratory latent structure analysis," Psychometrika, Springer, vol. 72(2), pages 199-225, June.
    11. Donald, Margaret & Alston, Clair L. & Young, Rick R. & Mengersen, Kerrie L., 2011. "A Bayesian analysis of an agricultural field trial with three spatial dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3320-3332, December.

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