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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:	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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