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Smoothing parameter selection for smoothing splines: a simulation study

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  • Lee, Thomas C. M.

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  • Lee, Thomas C. M., 2003. "Smoothing parameter selection for smoothing splines: a simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 139-148, February.
    • Handle: RePEc:eee:csdana:v:42:y:2003:i:1-2:p:139-148
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    References listed on IDEAS

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    1. M. P. Wand, 2000. "A Comparison of Regression Spline Smoothing Procedures," Computational Statistics, Springer, vol. 15(4), pages 443-462, December.
    2. Hardle, W. & Marron, J. S., 1995. "Fast and simple scatterplot smoothing," Computational Statistics & Data Analysis, Elsevier, vol. 20(1), pages 1-17, July.
    3. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
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    Cited by:

    1. Valentina Rizzoli & Matilde Trevisani & Arjuna Tuzzi, 2023. "Portraying the life cycle of ideas in social psychology through functional (textual) data analysis: a toolkit for digital history," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5197-5226, September.
    2. Lauren N. Berry & Nathaniel E. Helwig, 2021. "Cross-Validation, Information Theory, or Maximum Likelihood? A Comparison of Tuning Methods for Penalized Splines," Stats, MDPI, vol. 4(3), pages 1-24, September.
    3. Malloy, Elizabeth J. & Spiegelman, Donna & Eisen, Ellen A., 2009. "Comparing measures of model selection for penalized splines in Cox models," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2605-2616, May.
    4. Dursun AYDIN & Ersin YILMAZ, 2017. "Bandwidth Selection Problem for Nonparametric Regression Model with Right-Censored Data," Romanian Statistical Review, Romanian Statistical Review, vol. 65(2), pages 81-104, June.
    5. Caren Hasler & Radu V. Craiu, 2020. "Nonparametric imputation method for nonresponse in surveys," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 25-48, March.
    6. Jang, Dongik & Oh, Hee-Seok, 2011. "Enhancement of spatially adaptive smoothing splines via parameterization of smoothing parameters," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1029-1040, February.
    7. Thomas A. Severini, 2016. "A nonparametric approach to measuring the sensitivity of an asset’s return to the market," Annals of Finance, Springer, vol. 12(2), pages 179-199, May.
    8. Sima, Diana M. & Van Huffel, Sabine, 2006. "A class of template splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3486-3499, August.
    9. Wei, Wen Hsiang, 2004. "Derivatives diagnostics and robustness for smoothing splines," Computational Statistics & Data Analysis, Elsevier, vol. 46(2), pages 335-356, June.
    10. Bhattacharjee, Arnab, 2004. "Estimation in hazard regression models under ordered departures from proportionality," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 517-536, October.
    11. Eduardo L. Montoya, 2020. "On the Number of Independent Pieces of Information in a Functional Linear Model with a Scalar Response," Stats, MDPI, vol. 3(4), pages 1-16, November.
    12. Victor M. Guerrero, 2008. "Estimating Trends with Percentage of Smoothness Chosen by the User," International Statistical Review, International Statistical Institute, vol. 76(2), pages 187-202, August.
    13. Kim, Young-Ju, 2011. "A comparative study of nonparametric estimation in Weibull regression: A penalized likelihood approach," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1884-1896, April.
    14. Lee, Thomas C. M., 2004. "Improved smoothing spline regression by combining estimates of different smoothness," Statistics & Probability Letters, Elsevier, vol. 67(2), pages 133-140, April.

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