GPD Threshold Estimation Using Measure of Surprise

Authors

  • Abraham Manurung Department of Statistics, Faculty of Mathematics and Natural Science, Bogor Agricultural University, Bogor, Indonesia
  • Aji Hamim Wigena Department of Statistics, Faculty of Mathematics and Natural Science, Bogor Agricultural University, Bogor, Indonesia
  • Anik Djuraidah Department of Statistics, Faculty of Mathematics and Natural Science, Bogor Agricultural University, Bogor, Indonesia

Keywords:

Bayes, GPD, measure of surprise, posterior predictive distribution, rainfall, threshold estimation.

Abstract

Threshold is used to estimate parameters of Generalized Pareto distribution to estimate return value. This return value shows the extreme value in the period of time. Threshold can be estimated using Mean Residual Life Plot, Threshold Stability Plot, or the upper 10% rule but this estimation is usually subjective. An alternative method is measure of surprise based on Bayes method and Monte Carlo Markov Chain technique. This paper aims to estimate a GPD threshold based on simulation data and to apply measure of surprise method to rainfall data in the period of 1981-2012 in Bogor, Indonesia.  The simulation result showed that the predicted threshold is exactly the same as the true threshold. The result of application to rainfall data showed the threshold was approximately 210 mm.

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Published

2018-11-05

How to Cite

Manurung, A., Wigena, A. H., & Djuraidah, A. (2018). GPD Threshold Estimation Using Measure of Surprise. International Journal of Sciences: Basic and Applied Research (IJSBAR), 42(3), 16–25. Retrieved from https://www.gssrr.org/index.php/JournalOfBasicAndApplied/article/view/9430

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