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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 EFOP
Internet cím:	Szerző által megadott URL DOI 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:	BIBFORM083793
035-os BibID:	(Scopus)85070544877
Első szerző:	Reisen, Valdério Anselmo
Cím:	An Overview of Robust Spectral Estimators / Valdério Anselmo Reisen, Céline Lévy-Leduc, Higor Henrique Aranda Cotta, Pascal Bondon, Marton Ispány, Paulo Roberto Prezotti Filho
Dátum:	2020
Megjegyzések:	The periodogram function is widely used to estimate the spectral density of time series processes and it is well-known that this function is also very sensitive to outliers. In this context, this paper deals with robust estimation functions to estimate the spectral density of univariate and periodic time series with short and long-memory properties. The two robust periodogram functions discussed and compared here were previously explicitly and analytically derived in Fajardo et al. (2018), Reisen et al. (2017) and Fajardo et al. (2009) in the case of long-memory processes. The first two references introduce the robust periodogram based on M-regression estimator. The third reference is based on the robust autocovariance function introduced in Ma and Genton (2000) and studied theoretically and empirically in Lévy-Leduc et al. (2011). Here, the theoretical results of these estimators are discussed in the case of short and long-memory univariate time series and periodic processes. A special attention is given to the M-periodogram for short-memory processes. In this case, Theorem 1 and Corollary 1 derive the asymptotic distribution of this spectral estimator. As the application of the methodologies, robust estimators for the parameters of AR, ARFIMA and PARMA processes are discussed. Their finite sample size properties are addressed and compared in the context of absence and presence of atypical observations. Therefore, the contributions of this paper come to fill some gaps in the literature of modeling univariate and periodic time series to handle additive outliers.
ISBN:	978-3-030-22528-5
Tárgyszavak:	Műszaki tudományok Informatikai tudományok előadáskivonat könyvrészlet Time series M-estimation Long-memory Periodic processes Robustness
Megjelenés:	Cyclostationarity : Theory and Methods : IV / eds. Fakher Chaari, Jacek Leskow, Radoslaw Zimroz, Agnieszka Wyłomańska, Anna Dudek. - Vol. 4., p. 204-224. -
További szerzők:	Lévy-Leduc, Céline Cotta, Higor Henrique Aranda Bondon, Pascal Ispány Márton (1966-) (informatikus, matematikus) Filho, Paulo Roberto Prezotti
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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