1 research outputs found
A Bayesian Binomial Regression Model for Ozone Levels in Northern Italy
No full textThis study proposes a Bayesian binomial regression model
for monthly counts of extreme ozone levels. We implement a hierarchical
modeling framework, involving different hierarchies of mixed effects, to
best explain pollution data provided by ARPA Lombardia. We consider
two different daily thresholds, providing two associated datasets count-
ing the number of days above the threshold for each month. The data
are collected over multiple years through a wide network of monitoring
stations. The model we propose accounts for both spatial structure and
time-varying covariates in a convenient and flexible structure. We apply
the same Bayesian model to the two datasets on extreme ozone pollution
in Northern Italy, in particular focusing on potential differences in the
posterior inference
