Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression Models
This paper concerns kernel-type ridge estimators of parameters in a semiparametric model. These estimators are a generalization of the well-known Speckman’s approach based on kernel smoothing method. The most important factor in achieving this smoothing method is the selection of the smoothing para...
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| Format: | Article |
| Language: | English |
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Instituto Nacional de Estatística | Statistics Portugal
2021-03-01
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| Series: | Revstat Statistical Journal |
| Subjects: | |
| Online Access: | https://revstat.ine.pt/index.php/REVSTAT/article/view/331 |
| _version_ | 1828452848354983936 |
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| author | Ersin Yilmaz Bahadir Yuzbasi Dursun Aydin |
| author_facet | Ersin Yilmaz Bahadir Yuzbasi Dursun Aydin |
| author_sort | Ersin Yilmaz |
| collection | DOAJ |
| description |
This paper concerns kernel-type ridge estimators of parameters in a semiparametric model. These estimators are a generalization of the well-known Speckman’s approach based on kernel smoothing method. The most important factor in achieving this smoothing method is the selection of the smoothing parameter. In the literature, many selection criteria for comparing regression models have been produced. We will focus on six selection criterion improved version of Akaike information criterion (AICc), generalized cross-validation (GCV), Mallows’ Cp criterion, risk estimation using classical pilots (RECP), Bayes information criterion (BIC), and restricted maximum likelihood (REML). Real and simulated data sets are considered to illustrate the key ideas in the paper. Thus, suitable selection criterion are provided for optimum smoothing parameter selection.
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| first_indexed | 2024-12-10T23:58:52Z |
| format | Article |
| id | doaj.art-0ffdb31519b34173882b466203dcb889 |
| institution | Directory Open Access Journal |
| issn | 1645-6726 2183-0371 |
| language | English |
| last_indexed | 2024-12-10T23:58:52Z |
| publishDate | 2021-03-01 |
| publisher | Instituto Nacional de Estatística | Statistics Portugal |
| record_format | Article |
| series | Revstat Statistical Journal |
| spelling | doaj.art-0ffdb31519b34173882b466203dcb8892022-12-22T01:28:32ZengInstituto Nacional de Estatística | Statistics PortugalRevstat Statistical Journal1645-67262183-03712021-03-0119110.57805/revstat.v19i1.331Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression ModelsErsin Yilmaz 0Bahadir Yuzbasi 1Dursun Aydin 2Mugla Sitki Kocman UniversityInonu UniversityMugla Sitki Kocman University This paper concerns kernel-type ridge estimators of parameters in a semiparametric model. These estimators are a generalization of the well-known Speckman’s approach based on kernel smoothing method. The most important factor in achieving this smoothing method is the selection of the smoothing parameter. In the literature, many selection criteria for comparing regression models have been produced. We will focus on six selection criterion improved version of Akaike information criterion (AICc), generalized cross-validation (GCV), Mallows’ Cp criterion, risk estimation using classical pilots (RECP), Bayes information criterion (BIC), and restricted maximum likelihood (REML). Real and simulated data sets are considered to illustrate the key ideas in the paper. Thus, suitable selection criterion are provided for optimum smoothing parameter selection. https://revstat.ine.pt/index.php/REVSTAT/article/view/331semiparametric modelkernel smoothingridge type estimatorsmoothing parameter generalized cross-validation |
| spellingShingle | Ersin Yilmaz Bahadir Yuzbasi Dursun Aydin Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression Models Revstat Statistical Journal semiparametric model kernel smoothing ridge type estimator smoothing parameter generalized cross-validation |
| title | Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression Models |
| title_full | Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression Models |
| title_fullStr | Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression Models |
| title_full_unstemmed | Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression Models |
| title_short | Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression Models |
| title_sort | choice of smoothing parameter for kernel type ridge estimators in semiparametric regression models |
| topic | semiparametric model kernel smoothing ridge type estimator smoothing parameter generalized cross-validation |
| url | https://revstat.ine.pt/index.php/REVSTAT/article/view/331 |
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