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Modeling Mortality Based on Pollution and Temperature Using a New Birnbaum-Saunders Autoregressive Moving Average Structure with Regressors and Related-Sensors Data.
Saulo, Helton; Souza, Rubens; Vila, Roberto; Leiva, Víctor; Aykroyd, Robert G.
Afiliação
  • Saulo H; Department of Statistics, Universidade de Brasília, Brasília 70910-90, Brazil.
  • Souza R; Department of Statistics, Universidade de Brasília, Brasília 70910-90, Brazil.
  • Vila R; Department of Statistics, Universidade de Brasília, Brasília 70910-90, Brazil.
  • Leiva V; School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile.
  • Aykroyd RG; Department of Statistics, University of Leeds, Leeds, West Yorkshire LS2 9JT, UK.
Sensors (Basel) ; 21(19)2021 Sep 29.
Article em En | MEDLINE | ID: mdl-34640834
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Poluição Ambiental Tipo de estudo: Health_economic_evaluation Aspecto: Determinantes_sociais_saude Idioma: En Revista: Sensors (Basel) Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Brasil País de publicação: Suíça
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Poluição Ambiental Tipo de estudo: Health_economic_evaluation Aspecto: Determinantes_sociais_saude Idioma: En Revista: Sensors (Basel) Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Brasil País de publicação: Suíça