Univariate generalized additive models for simulated stationary and non-stationary generalized Pareto distribution
Generalized additive models as a predictor in regression approaches, are made up over cubic spline basis and penalized regression splines. Despite of linear predictor in GLM, generalized additive models use a sum of smooth functions of covariates as a predictor. The data which are used in this study...
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| Format: | Article |
| Language: | English |
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Science Publications
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/63632/ http://psasir.upm.edu.my/id/eprint/63632/1/Univariate%20Generalized%20Additive%20Models%20for%20Simulated%20Stationary%20and%20Non-Stationary%20Generalized%20Pareto%20Distribution.pdf |
| _version_ | 1848854834587893760 |
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| author | Behzadi, Mostafa Adam, Mohd Bakri Fitrianto, Anwar |
| author_facet | Behzadi, Mostafa Adam, Mohd Bakri Fitrianto, Anwar |
| author_sort | Behzadi, Mostafa |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Generalized additive models as a predictor in regression approaches, are made up over cubic spline basis and penalized regression splines. Despite of linear predictor in GLM, generalized additive models use a sum of smooth functions of covariates as a predictor. The data which are used in this study have generalized Pareto distribution and have been simulated by inversion method. The data are generated in two types, the stationary case and the non-stationary case. The method of root mean square of errors as a method of measurement is used for comparison between power of predictions which are based on penalized regression splines as a method in univariate generalized additive models and linear regression based on maximum likelihood estimation. The finding of this research illustrates that the amount of accuracy of estimation of parameter of location in UGAM approach as an alternative promising of modelling through each specialized GPD's models, has less RMSE in compare with MLE. |
| first_indexed | 2025-11-15T11:16:10Z |
| format | Article |
| id | upm-63632 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:16:10Z |
| publishDate | 2017 |
| publisher | Science Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-636322018-11-07T09:03:23Z http://psasir.upm.edu.my/id/eprint/63632/ Univariate generalized additive models for simulated stationary and non-stationary generalized Pareto distribution Behzadi, Mostafa Adam, Mohd Bakri Fitrianto, Anwar Generalized additive models as a predictor in regression approaches, are made up over cubic spline basis and penalized regression splines. Despite of linear predictor in GLM, generalized additive models use a sum of smooth functions of covariates as a predictor. The data which are used in this study have generalized Pareto distribution and have been simulated by inversion method. The data are generated in two types, the stationary case and the non-stationary case. The method of root mean square of errors as a method of measurement is used for comparison between power of predictions which are based on penalized regression splines as a method in univariate generalized additive models and linear regression based on maximum likelihood estimation. The finding of this research illustrates that the amount of accuracy of estimation of parameter of location in UGAM approach as an alternative promising of modelling through each specialized GPD's models, has less RMSE in compare with MLE. Science Publications 2017 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/63632/1/Univariate%20Generalized%20Additive%20Models%20for%20Simulated%20Stationary%20and%20Non-Stationary%20Generalized%20Pareto%20Distribution.pdf Behzadi, Mostafa and Adam, Mohd Bakri and Fitrianto, Anwar (2017) Univariate generalized additive models for simulated stationary and non-stationary generalized Pareto distribution. Journal of Mathematics and Statistics, 13 (2). 169 - 176. ISSN 1549-3644 10.3844/jmssp.2017.169.176 |
| spellingShingle | Behzadi, Mostafa Adam, Mohd Bakri Fitrianto, Anwar Univariate generalized additive models for simulated stationary and non-stationary generalized Pareto distribution |
| title | Univariate generalized additive models for simulated stationary and non-stationary generalized Pareto distribution |
| title_full | Univariate generalized additive models for simulated stationary and non-stationary generalized Pareto distribution |
| title_fullStr | Univariate generalized additive models for simulated stationary and non-stationary generalized Pareto distribution |
| title_full_unstemmed | Univariate generalized additive models for simulated stationary and non-stationary generalized Pareto distribution |
| title_short | Univariate generalized additive models for simulated stationary and non-stationary generalized Pareto distribution |
| title_sort | univariate generalized additive models for simulated stationary and non-stationary generalized pareto distribution |
| url | http://psasir.upm.edu.my/id/eprint/63632/ http://psasir.upm.edu.my/id/eprint/63632/ http://psasir.upm.edu.my/id/eprint/63632/1/Univariate%20Generalized%20Additive%20Models%20for%20Simulated%20Stationary%20and%20Non-Stationary%20Generalized%20Pareto%20Distribution.pdf |