Generalized additive model for count time series: An application to quantify the impact of air pollutants on human health - CentraleSupélec Access content directly
Journal Articles Pesquisa Operacional Year : 2021

Generalized additive model for count time series: An application to quantify the impact of air pollutants on human health

Abstract

The generalized additive model (GAM) has been used in many epidemiological studies where frequently the response variable is a nonnegative integer-valued time series. However, GAM assume that the observations are independent, which is generally not the case in time series. In this paper, an autoregressive moving average (ARMA) component is incorporated to the GAM. The resulting GAM-ARMA model is based on the generalized linear autoregressive moving average (GLARMA) model where some linear components are replaced by natural splines. Numerical simulations are presented and show that the ARMA component influences the estimation. In a real data analysis of the effects of air pollution on respiratory disease in the metropolitan area of Belo Horizonte, Brazil, it is shown that the proposed model presents a better fit when compared to the classical GAM approach, that does not take into account the autocorrelation of the data.
Fichier principal
Vignette du fichier
Generalized additive model for count time series An application to quantify the impact of air pollutants on human health.pdf (241.02 Ko) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

hal-03437160 , version 1 (19-11-2021)

Identifiers

Cite

Ana Julia A. Camara, Glaura Franco, Valderio Reisen, Pascal Bondon. Generalized additive model for count time series: An application to quantify the impact of air pollutants on human health. Pesquisa Operacional, 2021, 41, pp.e241120. ⟨10.1590/0101-7438.2021.041.00241120⟩. ⟨hal-03437160⟩
30 View
162 Download

Altmetric

Share

Gmail Facebook X LinkedIn More