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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
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 |