Direct Determination of Smoothing Parameter for Penalized Spline Regression
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 penalize...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2014/203469 |
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| author | Takuma Yoshida |
| author_facet | Takuma Yoshida |
| author_sort | Takuma Yoshida |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d4e38714e5d040138038c5c1296abe23 |
| institution | Kabale University |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-d4e38714e5d040138038c5c1296abe232025-02-03T05:51:37ZengWileyJournal of Probability and Statistics1687-952X1687-95382014-01-01201410.1155/2014/203469203469Direct Determination of Smoothing Parameter for Penalized Spline RegressionTakuma Yoshida0Graduate School of Science and Engineering, Kagoshima University, Kagoshima 890-8580, JapanPenalized 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.http://dx.doi.org/10.1155/2014/203469 |
| spellingShingle | Takuma Yoshida Direct Determination of Smoothing Parameter for Penalized Spline Regression Journal of Probability and Statistics |
| title | Direct Determination of Smoothing Parameter for Penalized Spline Regression |
| title_full | Direct Determination of Smoothing Parameter for Penalized Spline Regression |
| title_fullStr | Direct Determination of Smoothing Parameter for Penalized Spline Regression |
| title_full_unstemmed | Direct Determination of Smoothing Parameter for Penalized Spline Regression |
| title_short | Direct Determination of Smoothing Parameter for Penalized Spline Regression |
| title_sort | direct determination of smoothing parameter for penalized spline regression |
| url | http://dx.doi.org/10.1155/2014/203469 |
| work_keys_str_mv | AT takumayoshida directdeterminationofsmoothingparameterforpenalizedsplineregression |