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001-es BibID:	BIBFORM075488
Első szerző:	Ispány Márton (informatikus, matematikus)
Cím:	An application of the GAM-PCA-VAR model to respiratory disease and air pollution data / Márton Ispány, Juliana Bottoni de Souza, Valdério A. Reisen, Glaura C. Franco, Pascal Bondon, Jane Meri Santos
Dátum:	2017
Megjegyzések:	The hybrid GAM-PCA-VAR model, which is the combination of the principal component analysis (PCA) and the generalized additive model (GAM) along with a vector autoregressive (VAR) process, is proposed for studying the health effects of air pollution. The model is applied to a real data set with the aim of quantifying the association between the number of hospital admissions for respiratory diseases as response variable and air pollution concentrations, especially, PM10, SO2, NO2, CO and O3, as covariates.
ISBN:	978-84-17293-01-7
Tárgyszavak:	Természettudományok Matematika- és számítástudományok előadáskivonat Generalized additive model Multicollinearity Principal component analysis Relative risk Serial correlation Vector autoregressive model
Megjelenés:	Proceedings ITISE 2017. International work-conference on Time Series / eds. Olga Valenzuela, Fernando Rojas, Héctor Pomares, Ignacio Rojas. - p. 319-320. -
További szerzők:	Souza, Juliana Bottoni de Reisen, Valdério Anselmo Franco, Glaura C. Bondon, Pascal Santos, Jane Meri
Internet cím:	Szerző által megadott URL Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM083792
035-os BibID:	(cikkazonosító)117080 (WoS)000510946800004 (Scopus)85075889215
Első szerző:	Machado, Milena
Cím:	Use of multivariate time series techniques to estimate the impact of particulate matter on the perceived annoyance / Milena Machado, Valdério Anselmo Reisen, Jane Meri Santos, Neyval Costa Reis, Severine Frère, Pascal Bondon, Márton Ispány, Higor Henrique Aranda Cotta
Dátum:	2020
ISSN:	1352-2310
Megjegyzések:	As well known, Particulate matter (PM) is an air pollutant that causes damage to the health of humans, other animals, plants, affects the climate and is a potential cause of annoyance through deposition on various surfaces. The perceived annoyance caused by particulate matter is related mainly to the increase of settled dust in urban and residential environments. PM can originate from many sources, i.e., paved and unpaved roads, buildings, agricultural operations and wind erosion represent the largest contributions beyond the relatively minor vehicular and industrial sources emissions. The aim of this paper is to quantify the relationship between perceived annoyance and particulate matter concentration and to estimate the relative risk (RR). The data was collected in the Metropolitan Region of Vitoria (MRV), Brazil. For this purpose, the variables of interest were modeled using vector time series model (VAR), principal component analysis (PCA), and logistic regression (LOG). The combination of these techniques resulted in a hybrid model denoted as LOG-PCA-VAR which allows to estimate RR by handling multipollutant effects. This study shows that there is a strong association between the perceived annoyance and different sizes of PM. The estimates of RR indicate that an increase in air pollutant concentrations significantly contributes in increasing the probability of being annoyed.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Annoyance principal component analysis logistic regression relative risk
Megjelenés:	Atmospheric Environment. - 222 (2020), p. 1-24. -
További szerzők:	Reisen, Valdério Anselmo Santos, Jane Meri Reis, Neyval Costa Frère, Severine Bondon, Pascal Ispány Márton (1966-) (informatikus, matematikus) Cotta, Higor Henrique Aranda
Pályázati támogatás:	EFOP-3.6.1-16-2016-00022 EFOP
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM070487
035-os BibID:	(WoS)000419981200008 (Scopus)85040639536
Első szerző:	Souza, Juliana Bottoni de
Cím:	Generalized additive models with principal component analysis: an application to time series of respiratory disease and air pollution data / de Souza, Juliana B., Reisen, Valdério A., Franco, Glaura C., Ispány, Márton, Bondon, Pascal, Santos, Jane Meri
Dátum:	2018
ISSN:	0035-9254
Megjegyzések:	Environmental epidemiological studies of the health effects of air pollution frequently utilize the generalized additive model (GAM) as the standard statistical methodology, considering the ambient air pollutants as explanatory covariates. Although exposure to air pollutants is multi-dimensional, the majority of these studies consider only a single pollutant as a covariate in the GAM model. This model restriction may be because the pollutant variables do not only have serial dependence but also interdependence between themselves. In an attempt to convey a more realistic model, we propose here the hybrid generalized additive model-principal component analysis-vector auto-regressive (GAM-PCA-VAR) model, which is a combination of PCA and GAMs along with a VAR process. The PCA is used to eliminate the multicollinearity between the pollutants whereas the VAR model is used to handle the serial correlation of the data to produce white noise processes as covariates in the GAM. Some theoretical and simulation results of the methodology proposed are discussed, with special attention to the effect of time correlation of the covariates on the PCA and, consequently, on the estimates of the parameters in the GAM and on the relative risk, which is a commonly used statistical quantity to measure the effect of the covariates, especially the pollutants, on population health. As a main motivation to the methodology, a real data set is analysed with the aim of quantifying the association between respiratory disease and air pollution concentrations, especially particulate matter PM10, sulphur dioxide, nitrogen dioxide, carbon monoxide and ozone. The empirical results show that the GAM-PCA-VAR model can remove the auto-correlations from the principal components. In addition, this method produces estimates of the relative risk, for each pollutant, which are not affected by the serial correlation in the data. This, in general, leads to more pronounced values of the estimated risk compared with the standard GAM model, indicating, for this study, an increase of almost 5.4% in the risk of PM10, which is one of the most important pollutants which is usually associated with adverse effects on human health.
Tárgyszavak:	Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Generalized additive model Multicollinearity Principal component analysis Relative risk Serial correlation Vector auto-regressive model
Megjelenés:	Journal of The Royal Statistical Society Series C-Applied Statistics. - 67 : 2 (2018), p. 453-480. -
További szerzők:	Reisen, Valdério Anselmo Franco, Glaura C. Ispány Márton (1966-) (informatikus, matematikus) Bondon, Pascal Santos, Jane Meri
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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