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Univariate generalized additive models for simulated stationary and non-stationary generalized Pareto distribution


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


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.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Science
Institute for Mathematical Research
DOI Number: https://doi.org/10.3844/jmssp.2017.169.176
Publisher: Science Publications
Keywords: Generalized Pareto distribution; Univariate generalized; Additive model; Smooth function; Penalized regression spline; Cubic spline basis; Simulated data; Maximum likelihood estimation; Root mean square of errors
Depositing User: Mohd Hafiz Che Mahasan
Date Deposited: 07 Nov 2018 09:03
Last Modified: 07 Nov 2018 09:03
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.3844/jmssp.2017.169.176
URI: http://psasir.upm.edu.my/id/eprint/63632
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