{"title":"Robust estimate for count time series using GLARMA models: An application to environmental and epidemiological data","authors":"","doi":"10.1016/j.apm.2024.115658","DOIUrl":null,"url":null,"abstract":"<div><p>The Generalized Linear Autoregressive Moving Average (GLARMA) model has been used in epidemiological studies to evaluate the impact of air pollutants on health. Due to the nature of the data, a robust approach for the GLARMA model is proposed here based on the robustification of the quasi-likelihood function. Outlying observations are bounded separately by weight functions on covariates and the Huber loss function on the response variable. Some technical issues related to the robust approach are discussed and a Monte Carlo study revealed that the robust approach is more reliable than the classic one for contaminated data with additive outliers. The real data analysis investigates the impact of <span><math><msub><mrow><mtext>PM</mtext></mrow><mrow><mn>10</mn></mrow></msub></math></span> in the number of deaths by respiratory diseases in Vitória, Brazil.</p></div>","PeriodicalId":50980,"journal":{"name":"Applied Mathematical Modelling","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0307904X24004116/pdfft?md5=f13e4f9f5bad93612c899d117fb838b4&pid=1-s2.0-S0307904X24004116-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Mathematical Modelling","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0307904X24004116","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
The Generalized Linear Autoregressive Moving Average (GLARMA) model has been used in epidemiological studies to evaluate the impact of air pollutants on health. Due to the nature of the data, a robust approach for the GLARMA model is proposed here based on the robustification of the quasi-likelihood function. Outlying observations are bounded separately by weight functions on covariates and the Huber loss function on the response variable. Some technical issues related to the robust approach are discussed and a Monte Carlo study revealed that the robust approach is more reliable than the classic one for contaminated data with additive outliers. The real data analysis investigates the impact of in the number of deaths by respiratory diseases in Vitória, Brazil.
期刊介绍：
Applied Mathematical Modelling focuses on research related to the mathematical modelling of engineering and environmental processes, manufacturing, and industrial systems. A significant emerging area of research activity involves multiphysics processes, and contributions in this area are particularly encouraged.
This influential publication covers a wide spectrum of subjects including heat transfer, fluid mechanics, CFD, and transport phenomena; solid mechanics and mechanics of metals; electromagnets and MHD; reliability modelling and system optimization; finite volume, finite element, and boundary element procedures; modelling of inventory, industrial, manufacturing and logistics systems for viable decision making; civil engineering systems and structures; mineral and energy resources; relevant software engineering issues associated with CAD and CAE; and materials and metallurgical engineering.
Applied Mathematical Modelling is primarily interested in papers developing increased insights into real-world problems through novel mathematical modelling, novel applications or a combination of these. Papers employing existing numerical techniques must demonstrate sufficient novelty in the solution of practical problems. Papers on fuzzy logic in decision-making or purely financial mathematics are normally not considered. Research on fractional differential equations, bifurcation, and numerical methods needs to include practical examples. Population dynamics must solve realistic scenarios. Papers in the area of logistics and business modelling should demonstrate meaningful managerial insight. Submissions with no real-world application will not be considered.
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