Modeling mortality based on pollution and temperature using a new birnbaum–saunders autoregressive moving average structure with regressors and related-sensors data

Helton Saulo, Rubens Souza, Roberto Vila, Víctor Leiva, Robert G. Aykroyd

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number6518
JournalSensors
Volume21
Issue number19
DOIs
StatePublished - 1 Oct 2021
Externally publishedYes

Keywords

  • ARMA models
  • Birnbaum–Saunders distribution
  • Data dependent over time
  • Maximum likelihood methods
  • Model selection
  • Monte Carlo simulation
  • R software
  • Residuals
  • Sensing and data extraction

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