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Direct Determination of Smoothing Parameter for Penalized Spline Regression

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  • Takuma Yoshida

Abstract

Penalized spline estimator is one of the useful smoothing methods. To construct the estimator, having goodness of fit and smoothness, the smoothing parameter should be appropriately selected. The purpose of this paper is to select the smoothing parameter using the asymptotic property of the penalized splines. The new smoothing parameter selection method is established in the context of minimization asymptotic form of MISE of the penalized splines. The mathematical and the numerical properties of the proposed method are studied. First we organize the new method in univariate regression model. Next we extend to the additive models. A simulation study to confirm the efficiency of the proposed method is addressed.

Suggested Citation

  • Takuma Yoshida, 2014. "Direct Determination of Smoothing Parameter for Penalized Spline Regression," Journal of Probability and Statistics, Hindawi, vol. 2014, pages 1-11, April.
    • Handle: RePEc:hin:jnljps:203469
    DOI: 10.1155/2014/203469
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