nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

On Generalized Additive Models with Dependent Time Series Covariates

verfasst von : Márton Ispány, Valdério A. Reisen, Glaura C. Franco, Pascal Bondon, Higor H. A. Cotta, Paulo R. P. Filho, Faradiba S. Serpa

Erschienen in: Time Series Analysis and Forecasting

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel The Analysis of Variability of Short Data Sets Based on Mahalanobis Distance Calculation and Surrogate Time Series Testing

Nächstes Kapitel A Bayesian Approach to Astronomical Time Delay Estimations

Al-Osh, M.A., Alzaid, A.A.: First-order integer valued autoregressive (INAR(1)) process. J. Time Ser. Anal. 8, 261–275 (1987)MathSciNetCrossRef

Barczy, M., Ispány, M., Pap, G., Scotto, M.G., Silva, M.E.: Additive outliers in INAR(1) models. Stat. Pap. 53, 935–949 (2012)MathSciNetCrossRef

Benjamin, M.A., Rigby, R.A., Stasinopoulos, D.M.: Generalized autoregressive moving average models. J. Amer. Statist. Assoc. 98, 214–223 (2003)MathSciNetCrossRef

Brockwell, P.J., Davis, R.A.: Time Series: Theory and Methods. Springer Series in Statistics. Springer-Verlag, New York (1991)CrossRef

Chen, R.J., Chu, C., Tan, J., Cao, J., Song, W., Xu, X., Jiang, C., Ma, W., Yang, C., Chen, B., Gui, Y., Kan, H.: Ambient air pollution and hospital admission in Shanghai. China. J. Hazard. Mater. 181, 234–240 (2010)CrossRef

Davis, R.A., Dunsmuir, W.T.M., Streett, S.B.: Observation-driven models for Poisson counts. Biometrika 90, 777–790 (2003)MathSciNetCrossRef

Dionisio, K.L., Chang, H.H., Baxter, L.K.: A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models. Environ. Health 15, 114 (2016)CrossRef

Durbin, J., Koopman, S.J.: Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives. J. Roy. Stat. Soc. B 62, 3–56 (2000)MathSciNetCrossRef

Ferland, R., Latour, A., Oraichi, D.: Integer-valued GARCH process. J. Time Ser. Anal. 27(6), 923–942 (2006)MathSciNetCrossRef

10.

Gamerman, D., Santos, T.R., Franco, G.C.: A non-Gaussian family of state-space models with exact marginal likelihood. J. Time Ser. Anal. 34, 625–645 (2013)MathSciNetCrossRef

11.

Hastie, T.J., Tibshirani, R.J.: Generalized Additive Models. Chapman and Hall, London (1990)MATH

12.

Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, New York (2002)MATH

13.

Nascimento, A.P., Santos, J.M., Mil, J.G., de Souza, J.B., Reis Júnior, N.C., Reisen, V.A.: Association between the concentration of fine particles in the atmosphere and acute respiratory diseases in children. Rev. Saude Publ. 51, 3 (2017)

14.

Ostro, B.D., Eskeland, G.S., Sánchez, J.M., Feyzioglu, T.: Air pollution and health effects: a study of medical visits among children in Santiago. Chile. Environ. Health Persp. 107, 69–73 (1999)CrossRef

15.

Roberts, S., Martin, M.: Using supervised principal components analysis to assess multiple pollutant effects. Environ. Health Persp. 114(12), 1877–1882 (2006)CrossRef

16.

Schwartz, J.: Harvesting and long term exposure effects in the relationship between air pollution and mortality. Am. J. Epidemiol. 151, 440–448 (2000)CrossRef

17.

de Souza, J.B., Reisen, V.A., Franco, G.C., Ispány, M., Bondon, P., Santos, J.M.: Generalized additive models with principal component analysis: an application to time series of respiratory disease and air pollution data. J. Roy. Stat. Soc. C-Appl. 67(2), 453–480 (2018)

18.

de Souza, J.B., Reisen, V.A., Santos, J.M., Franco, G.C.: Principal components and generalized linear modeling in the correlation between hospital admissions and air pollution. Rev. Saude Publ. 48(3), 451–8 (2014)CrossRef

19.

Wang, Y., Pham, H.: Analyzing the effects of air pollution and mortality by generalized additive models with robust principal components. Int. J. Syst. Assur. Eng. Manag. 2, 253–259 (2011)CrossRef

20.

Wood, S.N.: Generalized Additive Models: An Introduction with R. 2nd edn. Chapman and Hall/CRC (2017)

21.

Zamprogno, B.: PCA in time series with short and long-memory time series. Ph.D. Thesis at the Programa de Pós-Graduação em Engenharia Ambiental do Centro Tecnológico, UFES, Vitória, Brazil (2013)

22.

Zhao, J., Cao, J., Tian, S., Chen, Y., Zhang, S., Wang, Z., Zhou, X.: A comparison between two GAM models in quantifying relationships of environmental variables with fish richness and diversity indices. Aquat. Ecol. 48, 297–312 (2014)CrossRef

Titel: On Generalized Additive Models with Dependent Time Series Covariates
verfasst von: Márton Ispány
Valdério A. Reisen
Glaura C. Franco
Pascal Bondon
Higor H. A. Cotta
Paulo R. P. Filho
Faradiba S. Serpa
Verlag: Springer International Publishing
Buch: Time Series Analysis and Forecasting
Print ISBN: 978-3-319-96943-5

Electronic ISBN: 978-3-319-96944-2

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-96944-2_20

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"