Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, EpiSciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Reports

Unsupervised threshold determination for hybrid models

Abstract : A hybrid Gauss-Pareto model is considered for asymmetric heavy tailed data. The paper presents an unsupervised itera- tive algorithm to find successively the parameters of the Gaus- sian density and that of the Generalized Pareto distribution (GPD) with a continuity constrain on the hybrid density and its derivative at the junction point. Simulation results show that the proposed iterative algorithm provides reliable posi- tion for the junction point as well as an accurate estimation of the GPD parameters, compared to state of the art methods. Furthermore, a great advantage of the proposed method is that it can be adapted to any hybrid model.
Document type :
Reports
Complete list of metadata

Cited literature [21 references]  Display  Hide  Download

https://hal.inria.fr/hal-01367584
Contributor : Mamadou Mboup Connect in order to contact the contributor
Submitted on : Friday, September 16, 2016 - 1:59:09 PM
Last modification on : Thursday, October 14, 2021 - 1:10:05 PM
Long-term archiving on: : Saturday, December 17, 2016 - 3:03:29 PM

File

unsupervised_hybrid_2013.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01367584, version 1

Collections

Citation

Nehla Debbabi, Marie Kratz, Mamadou Mboup. Unsupervised threshold determination for hybrid models. [Research Report] Université de Reims Champagne Ardenne URCA. 2013. ⟨hal-01367584⟩

Share

Metrics

Record views

83

Files downloads

78