Small sample robust approach to outliers and correlation of atmospheric pollution and health effects in Santiago de Chile

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F19%3A43914762" target="_blank" >RIV/62156489:43110/19:43914762 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.chemolab.2018.12.010" target="_blank" >https://doi.org/10.1016/j.chemolab.2018.12.010</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.chemolab.2018.12.010" target="_blank" >10.1016/j.chemolab.2018.12.010</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Small sample robust approach to outliers and correlation of atmospheric pollution and health effects in Santiago de Chile

Popis výsledku v původním jazyce

Adverse effects of air pollution on health are a global problem. Chile is no exception due to the increase of urban population and increasing pollution sources. For several years in the winter months in Santiago de Chile, environmental pre-emergency is decreed, which is due to the increase of measurements of contaminants and the risk that this means to health. In order to model the effects of pollution on health we consider a hierarchical Bayesian generalized linear mixed autoregressive model proposed by Ref. [18]. In particular, we apply the model to the number of children with respiratory diseases in the town of Santiago for the period June-August 2011, using the PM2.5 data as covariate obtained by a spatiotemporal pollution model. In order to detect anomalous data, we apply to residuals both robust normality tests together with novel method of probabilities for mild or extreme outliers. We detected significant heterogeneity between stations which offer us better monitoring planning for the future.

Název v anglickém jazyce

Small sample robust approach to outliers and correlation of atmospheric pollution and health effects in Santiago de Chile

Popis výsledku anglicky

Adverse effects of air pollution on health are a global problem. Chile is no exception due to the increase of urban population and increasing pollution sources. For several years in the winter months in Santiago de Chile, environmental pre-emergency is decreed, which is due to the increase of measurements of contaminants and the risk that this means to health. In order to model the effects of pollution on health we consider a hierarchical Bayesian generalized linear mixed autoregressive model proposed by Ref. [18]. In particular, we apply the model to the number of children with respiratory diseases in the town of Santiago for the period June-August 2011, using the PM2.5 data as covariate obtained by a spatiotemporal pollution model. In order to detect anomalous data, we apply to residuals both robust normality tests together with novel method of probabilities for mild or extreme outliers. We detected significant heterogeneity between stations which offer us better monitoring planning for the future.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA16-07089S" target="_blank" >GA16-07089S: Robustní přístup testování normality chybového členu v ekonometrických modelech</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Chemometrics and Intelligent Laboratory Systems

ISSN

0169-7439

e-ISSN

—

Svazek periodika

185

Číslo periodika v rámci svazku

15 February

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

73-84

Kód UT WoS článku

000458943100009

EID výsledku v databázi Scopus

2-s2.0-85059744473