Titre:	Modeling mortality based on pollution and temperature using a new Birnbaum–Saunders autoregressive moving average structure with regressors and related-sensors data
Auteur(s):	Saulo, Helton Souza, Rubens Batista de Vila, Roberto Aykroyd, Robert G.
metadata.dc.identifier.orcid:	https://orcid.org/0000-0002-4467-8652 https://orcid.org/0000-0002-1854-5805 https://orcid.org/0000-0003-1073-0114 https://orcid.org/0000-0003-4755-3270 https://orcid.org/0000-0003-3700-0816
Assunto::	Distribuições Birnbaum-Saunders Monte Carlo, Método de Software Linguagem de programação R
Date de publication:	2021
Editeur:	MDPI
Référence bibliographique:	SAULO, Helton et al. Modeling Mortality Based on Pollution and Temperature Using a New Birnbaum–Saunders Autoregressive Moving Average Structure with Regressors and Related-Sensors Data. Sensors, v. 21, n. 19, 6518, 2021. DOI: https://doi.org/10.3390/s21196518. Disponível em: https://www.mdpi.com/1424-8220/21/19/6518. Acesso em: 12 fev. 2022.
Abstract:	Environmental agencies are interested in relating mortality to pollutants and possible environmental contributors such as temperature. The Gaussianity assumption is often violated when modeling this relationship due to asymmetry and then other regression models should be considered. The class of Birnbaum–Saunders models, especially their regression formulations, has received considerable attention in the statistical literature. These models have been applied successfully in different areas with an emphasis on engineering, environment, and medicine. A common simplification of these models is that statistical dependence is often not considered. In this paper, we propose and derive a time-dependent model based on a reparameterized Birnbaum–Saunders (RBS) asymmetric distribution that allows us to analyze data in terms of a time-varying conditional mean. In particular, it is a dynamic class of autoregressive moving average (ARMA) models with regressors and a conditional RBS distribution (RBSARMAX). By means of a Monte Carlo simulation study, the statistical performance of the new methodology is assessed, showing good results. The asymmetric RBSARMAX structure is applied to the modeling of mortality as a function of pollution and temperature over time with sensor-related data. This modeling provides strong evidence that the new ARMA formulation is a good alternative for dealing with temporal data, particularly related to mortality with regressors of environmental temperature and pollution.
Licença::	Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY).
DOI:	https://doi.org/10.3390/s21196518
Collection(s) :	Artigos publicados em periódicos e afins

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