Dynamic generalized extreme value modeling via particle filters

DOI10.1080/03610918.2016.1202275zbMath1388.62241OpenAlexW2497934016MaRDI QIDQ4638827

Gabriel Huerta, Yonghua Wei

Publication date: 30 April 2018

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2016.1202275

zbMATH Keywords

particle filtersMCMCnonlinear observationGEV distributiontopix dataset

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Extreme value theory; extremal stochastic processes (60G70) Signal detection and filtering (aspects of stochastic processes) (60G35) Sequential estimation (62L12)

Uses Software

• ismev

Cites Work

• Particle learning and smoothing
• Generalized extreme value distribution with time-dependence using the AR and MA models in state space form
• Smoothing sample extremes with dynamic models
• Sequential Monte Carlo Methods in Practice
• Particle Markov Chain Monte Carlo for Efficient Numerical Simulation
• Maximum likelihood estimation in a class of nonregular cases
• Sequential Monte Carlo Methods for Dynamic Systems
• Filtering via Simulation: Auxiliary Particle Filters
• Bayesian Methods in Extreme Value Modelling: A Review and New Developments
• Monte Carlo sampling methods using Markov chains and their applications
• An introduction to statistical modeling of extreme values
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