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Predicting pollution incidents through semiparametric quantile regression models

Author:
Roca-Pardiñas, J., Ordóñez, C.
Source:
Stochastic environmental research and risk assessment 2019 v.33 no.3 pp. 673-685
ISSN:
1436-3240
Subject:
algorithms, coal, emissions, pollutants, pollution, power plants, prediction, regression analysis, Spain
Abstract:
In this paper we present a method to forecast pollution episodes using measurements of the pollutant concentration along time. Specifically, we use a backfitting algorithm with local polynomial kernel smoothers to estimate a semiparametric additive quantile regression model. We also propose a statistical hypothesis test to determine critical values, i.e., the values of the concentration that are significant to forecast the pollution episodes. This test is based on a wild bootstrap approach modified to suit asymmetric loss functions, as occurs in quantile regression. The validity of the method was checked with both simulated and real data, the latter from [Formula: see text] emissions from a coal-fired power station located in Northern Spain.
Agid:
6364935