A Bayesian approach to zero-inflated data in extremes

The generalized extreme value (GEV) distribution is known as the limiting result for the modeling of maxima blocks of size n, which is used in the modeling of extreme events. However, it is possible for the data to present an excessive number of zeros when dealing with extreme data, making it diffic...

Full description

Saved in:
Bibliographic Details
Published in:Communications in statistics. Theory and methods 2020-09, Vol.49 (17), p.4150-4161
Main Authors: Quadros Gramosa, Alexandre Henrique, Ferraz do Nascimento, Fernando, Castro Morales, Fidel Ernesto
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian Inference
Computer simulation
Extreme value theory
Extreme values
Markov Chain Monte Carlo
Markov chains
Mathematical models
Maxima
Monte Carlo simulation
Parameter estimation
precipitation data
Value analysis
zero-Inflated
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The generalized extreme value (GEV) distribution is known as the limiting result for the modeling of maxima blocks of size n, which is used in the modeling of extreme events. However, it is possible for the data to present an excessive number of zeros when dealing with extreme data, making it difficult to analyze and estimate these events by using the usual GEV distribution. The Zero-Inflated Distribution (ZID) is widely known in literature for modeling data with inflated zeros, where the inflator parameter w is inserted. The present work aims to create a new approach to analyze zero-inflated extreme values, that will be applied in data of monthly maximum precipitation, that can occur during months where there was no precipitation, being these computed as zero. An inference was made on the Bayesian paradigm, and the parameter estimation was made by numerical approximations of the posterior distribution using Markov Chain Monte Carlo (MCMC) methods. Time series of some cities in the northeastern region of Brazil were analyzed, some of them with predominance of non-rainy months. The results of these applications showed the need to use this approach to obtain more accurate and with better adjustment measures results when compared to the standard distribution of extreme value analysis.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2019.1594305