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Smoothing with Mixed Model Software

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  • Ngo, Long
  • Wand, Matthew P.

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.

Suggested Citation

  • Ngo, Long & Wand, Matthew P., 2004. "Smoothing with Mixed Model Software," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i01).
    • Handle: RePEc:jss:jstsof:v:009:i01
    DOI: http://hdl.handle.net/10.18637/jss.v009.i01
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    References listed on IDEAS

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    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, October.
    2. Brent A. Coull & David Ruppert & M. P. Wand, 2001. "Simple Incorporation of Interactions into Additive Models," Biometrics, The International Biometric Society, vol. 57(2), pages 539-545, June.
    3. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, October.
    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, January.
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    Cited by:

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    2. O. Gimenez & C. Crainiceanu & C. Barbraud & S. Jenouvrier & B. J. T. Morgan, 2006. "Semiparametric Regression in Capture–Recapture Modeling," Biometrics, The International Biometric Society, vol. 62(3), pages 691-698, September.
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    6. Tingting Zhou & Michael R. Elliott & Roderick J. A. Little, 2021. "Robust Causal Estimation from Observational Studies Using Penalized Spline of Propensity Score for Treatment Comparison," Stats, MDPI, vol. 4(2), pages 1-21, June.
    7. 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 of Labor Economics (IZA).
    8. Haonan Wang & Hari Iyer, 2007. "Application of local linear embedding to nonlinear exploratory latent structure analysis," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 199-225, June.
    9. 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.
    10. Crainiceanu, Ciprian M. & Ruppert, David & Wand, Matthew P., 2005. "Bayesian Analysis for Penalized Spline Regression Using WinBUGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i14).
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    12. Michael Wegener & Göran Kauermann, 2008. "Examining heterogeneity in implied equity risk premium using penalized splines," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 35-56, February.
    13. A. Giffin & B. J. Reich & S. Yang & A. G. Rappold, 2023. "Generalized propensity score approach to causal inference with spatial interference," Biometrics, The International Biometric Society, vol. 79(3), pages 2220-2231, September.
    14. 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.
    15. Guangyu Zhang & Roderick Little, 2009. "Extensions of the Penalized Spline of Propensity Prediction Method of Imputation," Biometrics, The International Biometric Society, vol. 65(3), pages 911-918, September.
    16. Lee, Dae-Jin & Durbán, María & Eilers, Paul, 2013. "Efficient two-dimensional smoothing with P-spline ANOVA mixed models and nested bases," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 22-37.
    17. Haonan Wang & M. Giovanna Ranalli, 2007. "Low-Rank Smoothing Splines on Complicated Domains," Biometrics, The International Biometric Society, vol. 63(1), pages 209-217, March.
    18. Stefan Stremersch & Aurélie Lemmens, 2009. "Sales Growth of New Pharmaceuticals Across the Globe: The Role of Regulatory Regimes," Marketing Science, INFORMS, vol. 28(4), pages 690-708, 07-08.
    19. 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 of Labor Economics (IZA).
    20. Tingting Zhou & Michael R. Elliott & Roderick J. A. Little, 2022. "Addressing Disparities in the Propensity Score Distributions for Treatment Comparisons from Observational Studies," Stats, MDPI, vol. 5(4), pages 1-17, December.
    21. 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 University Rotterdam.
    22. Bellio, Ruggero & Grassetti, Luca, 2011. "Semiparametric stochastic frontier models for clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 71-83, January.
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