A time dependent Bayesian nonparametric model for air quality analysis
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2016Metadata
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Gutiérrez, Luis
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A time dependent Bayesian nonparametric model for air quality analysis
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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.
General note
Artículo de publicación ISI
Patrocinador
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
Identifier
URI: https://repositorio.uchile.cl/handle/2250/136780
DOI: DOI: 10.1016/j.csda.2015.10.002
Quote Item
Computational Statistics and Data Analysis 95 (2016) 161–175
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