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001-es BibID:BIBFORM062893
035-os BibID:(WoS)000362557800011 (Scopus)84943815601
Első szerző:Bourguignon, Marcelo
Cím:A Poisson INAR(1) process with a seasonal structure / Marcelo Bourguignon, Klaus L.P. Vasconcellos, Valdério A. Reisen, Márton Ispány
Dátum:2016
ISSN:0094-9655 1563-5163
Megjegyzések:This paper introduces a non-negative integer-valued autoregressive (INAR) process with seasonal structureof first order, which is an extension of the standard INAR(1) model proposed by Al-Osh and Alzaid [First-order integer-valued autoregressive (INAR(1)) process. J Time Ser Anal. 1987;8:261-275]. The main properties of the model are derived such as its stationarity and autocorrelation function (ACF),among others. The conditional least squares and conditional maximum likelihood estimators of the model parameters are studied and their asymptotic properties are established. Some detailed discussion is dedicated to the case where the marginal distribution of the process is Poisson. A Monte Carlo experiment is conducted to evaluate and compare the performances of these estimators for finite sample sizes. The standard Yule-Walker approach is also considered for comparison purposes. The empirical results indicate that, in general, the conditional maximum likelihood estimator presents much better performance in terms of bias and mean square error. The model is illustrated using a real data set.
Tárgyszavak:Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
INAR(1) model
conditional least squares
conditional maximum likelihood
seasonal period
Yule-Walker
Megjelenés:Journal Of Statistical Computation And Simulation. - 86 : 2 (2016), p. 373-387. -
További szerzők:Vasconcellos, Klaus L.P. Reisen, Valdério Anselmo Ispány Márton (1966-) (informatikus, matematikus)
Pályázati támogatás:TÁMOP-4.2.2.C-11/1/KONV-2012-0001
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2.

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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3.

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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4.

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
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5.

001-es BibID:BIBFORM093210
035-os BibID:(WoS)000656909500005 (Scopus)85103972195
Első szerző:Prezotti Filho, Paulo Roberto
Cím:A periodic and seasonal statistical model for non-negative integer-valued time series with an application to dispensed medications in respiratory diseases / Paulo Roberto Prezotti Filho, Valderio Anselmo Reisen, Pascal Bondon, Márton Ispány, Milena Machado Melo, Faradiba Sarquis Serpa
Dátum:2021
ISSN:0307-904X
Megjegyzések:This paper introduces a new class of models for non-negative integer-valued time series with a periodic and seasonal autoregressive structure. Some properties of the model are discussed and the conditional quasi-maximum likelihood method is used to estimate the parameters. The consistency and asymptotic normality of the estimators are established. Their performances are investigated for finite sample sizes and the empirical results indi- cate that the method gives accurate estimates. The proposed model is applied to analyse the daily number of antibiotic dispensing medication for the treatment of respiratory diseases, registered in a health center of Vitória, Brazil.
Tárgyszavak:Természettudományok Matematika- és számítástudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Count time series
Periodicity
Seasonality
Consistency
Forecast
Air pollution problems
Megjelenés:Applied Mathematical Modelling. - 96 (2021), p. 545-558. -
További szerzők:Reisen, Valdério Anselmo Bondon, Pascal Ispány Márton (1966-) (informatikus, matematikus) Melo, Milena Machado Serpa, Faradiba S.
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6.

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
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7.

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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