使用 GLARMA 模型对计数时间序列进行稳健估计：环境和流行病学数据的应用

IF 4.4 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Applied Mathematical Modelling Pub Date : 2024-08-28 DOI:10.1016/j.apm.2024.115658

{"title":"使用 GLARMA 模型对计数时间序列进行稳健估计：环境和流行病学数据的应用","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":"{\"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}","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

摘要

广义线性自回归移动平均（GLARMA）模型已被用于流行病学研究，以评估空气污染物对健康的影响。由于数据的性质，本文提出了一种基于准似然比函数稳健化的 GLARMA 模型稳健方法。离群观测值分别通过协变量的权重函数和响应变量的 Huber 损失函数进行约束。本文讨论了与稳健方法相关的一些技术问题，蒙特卡罗研究表明，对于带有添加离群值的污染数据，稳健方法比传统方法更可靠。真实数据分析调查了 PM10 对巴西维托里亚呼吸系统疾病死亡人数的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Robust estimate for count time series using GLARMA models: An application to environmental and epidemiological data

查看原文本刊更多论文

Robust estimate for count time series using GLARMA models: An application to environmental and epidemiological data

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 ${PM}_{10}$ in the number of deaths by respiratory diseases in Vitória, Brazil.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Applied Mathematical Modelling 数学-工程：综合

CiteScore

9.80

自引率

8.00%

发文量

508

审稿时长

43 days

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.