Univariate Generalized Additive Models for Simulated Stationary and Non-Stationary Generalized Pareto Distribution
Mostafa Behzadi, Mohd Bakri Adam and Anwar Fitrianto
DOI : 10.3844/jmssp.2017.169.176
Journal of Mathematics and Statistics
Volume 13, Issue 2
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.
© 2017 Mostafa Behzadi, Mohd Bakri Adam and Anwar Fitrianto. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.