gPdtest

Pdtest: Bootstrap goodness-of-fit test for the generalized Pareto distribution. This package computes the bootstrap goodness-of-fit test for the generalized Pareto distribution by Villasenor-Alva and Gonzalez-Estrada (2009). The null hypothesis includes heavy and non-heavy tailed gPd’s. A function for fitting the gPd to data using the parameter estimation methods proposed in the same article is also provided.

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References in zbMATH (referenced in 10 articles , 1 standard article )

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  1. Eling, Martin; Schnell, Werner: Capital requirements for cyber risk and cyber risk insurance: an analysis of Solvency II, the U.S. Risk-Based Capital Standards, and the Swiss Solvency Test (2020)
  2. Chu, J.; Dickin, O.; Nadarajah, S.: A review of goodness of fit tests for Pareto distributions (2019)
  3. Chan, Debora; Rey, Andrea; Gambini, Juliana; Frery, Alejandro C.: Sampling from the (\mathcalG_I^0) distribution (2018)
  4. González-Estrada, E.; Villaseñor, J. A.: An R package for testing goodness of fit: goft (2018)
  5. Bessi, Alessandro: On the statistical properties of viral misinformation in online social media (2017)
  6. Eling, Martin; Loperfido, Nicola: Data breaches: goodness of fit, pricing, and risk measurement (2017)
  7. Cirillo, Pasquale; Taleb, Nassim Nicholas: On the statistical properties and tail risk of violent conflicts (2016)
  8. Meintanis, Simos G.; Gamero, Ma Dolores Jiménez; Alba-Fernández, V.: A class of goodness-of-fit tests based on transformation (2014)
  9. Nadarajah, Saralees; Afuecheta, Emmanuel; Chan, Stephen: A double generalized Pareto distribution (2013)
  10. Villaseñor-Alva, José A.; González-Estrada, Elizabeth: A bootstrap goodness of fit test for the generalized Pareto distribution (2009)