A time dependent Bayesian nonparametric model for air quality analysis
Author
dc.contributor.author
Gutiérrez, Luis
Author
dc.contributor.author
Mena, Ramsés H.
Author
dc.contributor.author
Ruggiero, Matteo
Admission date
dc.date.accessioned
2016-01-26T19:18:20Z
Available date
dc.date.available
2016-01-26T19:18:20Z
Publication date
dc.date.issued
2016
Cita de ítem
dc.identifier.citation
Computational Statistics and Data Analysis 95 (2016) 161–175
en_US
Identifier
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DOI: 10.1016/j.csda.2015.10.002
Identifier
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https://repositorio.uchile.cl/handle/2250/136780
General note
dc.description
Artículo de publicación ISI
en_US
Abstract
dc.description.abstract
Air quality monitoring is based on pollutants concentration levels, typically recorded in
metropolitan areas. These exhibit spatial and temporal dependence as well as seasonality
trends, and their analysis demands flexible and robust statistical models. Here we propose
to model the measurements of particulate matter, composed by atmospheric carcinogenic
agents, by means of a Bayesian nonparametric dynamic model which accommodates the
dependence structures present in the data and allows for fast and efficient posterior
computation. Lead by the need to infer the probability of threshold crossing at arbitrary
time points, crucial in contingency decision making, we apply the model to the timevarying
density estimation for a PM2.5 dataset collected in Santiago, Chile, and analyze
various other quantities of interest derived from the estimate.
en_US
Patrocinador
dc.description.sponsorship
Program U-INICIA VID
University of Chile
U-INICIA 02/12A
Fondecyt grant
11140013
CONACyT project
241195
European Research Council (ERC) through StG "N-BNP"
306406
project PAPIIT-UNAM
IN106114-3