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001-es BibID:BIBFORM075889
Első szerző:Ispány Márton (informatikus, matematikus)
Cím:On Generalized Additive Models with Dependent Time Series Covariates / Márton Ispány, Valdério A. Reisen, Glaura C. Franco, Pascal Bondon, Higor H. A. Cotta, Paulo R. P. Filho, Faradiba S. Serpa
Dátum:2018
Megjegyzések:The generalized additive model (GAM) is a standard statistical methodology and is frequently used in various fields of applied data analysis where the response variable is non-normal, e.g., integer-valued, and the explanatory variables are continuous, typically normally distributed. Standard assumptions of this model, among others, are that the explanatory variables are independent and identically distributed vectors which are not multicollinear. To handle the multicollinearity and serial dependence together a new hybrid model, called GAM-PCA-VAR model, was proposed in [17] (de Souza et al., J Roy Stat Soc C-Appl 2018) which is the combination of GAM with the principal component analysis (PCA) and the vector autoregressive (VAR) model. In this paper, some properties of the GAM-PCA-VAR model are discussed theoretically and verified by simulation. A real data set is also analyzed with the aim to describe the association between respiratory disease and air pollution concentrations.
ISBN:978-3-319-96943-5
Tárgyszavak:Természettudományok Matematika- és számítástudományok könyvfejezet
Air pollution
Generalized additive model
Multicollinearity
Principal component analysis
Time series
Vector autoregressive model
Megjelenés:Time Series Analysis and Forecasting / Hrsg. Ignacio Rojas, Héctor Pomares, Olga Valenzuela. - p. 289-308. -
További szerzők:Reisen, Valdério Anselmo Franco, Glaura C. Bondon, Pascal Cotta, Higor Henrique Aranda Filho, Paulo Roberto Prezotti Serpa, Faradiba S.
Pályázati támogatás:EFOP-3.6.1-16-2016-00022
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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
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3.

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
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