带有误差测量的空间滞后协变量的偏差校正方法

IF 2.5 2区 数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY
Mohammad Masjkur , Asep Saefuddin , I. Wayan Mangku , Henk Folmer , Arno J. Van der Vlist , Marco Grzegorczyk
{"title":"带有误差测量的空间滞后协变量的偏差校正方法","authors":"Mohammad Masjkur ,&nbsp;Asep Saefuddin ,&nbsp;I. Wayan Mangku ,&nbsp;Henk Folmer ,&nbsp;Arno J. Van der Vlist ,&nbsp;Marco Grzegorczyk","doi":"10.1016/j.spasta.2025.100909","DOIUrl":null,"url":null,"abstract":"<div><div>This paper compares three widely applied bias correction methods for spatially lagged covariates measured with error, namely, Monte Carlo expectation-maximization (MCEM), instrumental variables (IV), and Bayesian analysis (BA). We cross-compare these correction methods on simulated data for the special case of one single lagged covariate. We use the root mean squared error (RMSE) as evaluation criterion. The findings indicate that BA is the best bias correction method.</div></div>","PeriodicalId":48771,"journal":{"name":"Spatial Statistics","volume":"68 ","pages":"Article 100909"},"PeriodicalIF":2.5000,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bias correction methods for spatially lagged covariates measured with errors\",\"authors\":\"Mohammad Masjkur ,&nbsp;Asep Saefuddin ,&nbsp;I. Wayan Mangku ,&nbsp;Henk Folmer ,&nbsp;Arno J. Van der Vlist ,&nbsp;Marco Grzegorczyk\",\"doi\":\"10.1016/j.spasta.2025.100909\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper compares three widely applied bias correction methods for spatially lagged covariates measured with error, namely, Monte Carlo expectation-maximization (MCEM), instrumental variables (IV), and Bayesian analysis (BA). We cross-compare these correction methods on simulated data for the special case of one single lagged covariate. We use the root mean squared error (RMSE) as evaluation criterion. The findings indicate that BA is the best bias correction method.</div></div>\",\"PeriodicalId\":48771,\"journal\":{\"name\":\"Spatial Statistics\",\"volume\":\"68 \",\"pages\":\"Article 100909\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2025-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Spatial Statistics\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2211675325000314\",\"RegionNum\":2,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spatial Statistics","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2211675325000314","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

本文比较了蒙特卡罗期望最大化法(MCEM)、工具变量法(IV)和贝叶斯分析法(BA)三种应用广泛的空间滞后协变量偏差校正方法。对于单一滞后协变量的特殊情况,我们在模拟数据上对这些校正方法进行了交叉比较。我们使用均方根误差(RMSE)作为评价标准。结果表明,BA是最好的偏置校正方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bias correction methods for spatially lagged covariates measured with errors
This paper compares three widely applied bias correction methods for spatially lagged covariates measured with error, namely, Monte Carlo expectation-maximization (MCEM), instrumental variables (IV), and Bayesian analysis (BA). We cross-compare these correction methods on simulated data for the special case of one single lagged covariate. We use the root mean squared error (RMSE) as evaluation criterion. The findings indicate that BA is the best bias correction method.
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
Spatial Statistics
Spatial Statistics GEOSCIENCES, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
CiteScore
4.00
自引率
21.70%
发文量
89
审稿时长
55 days
期刊介绍: Spatial Statistics publishes articles on the theory and application of spatial and spatio-temporal statistics. It favours manuscripts that present theory generated by new applications, or in which new theory is applied to an important practical case. A purely theoretical study will only rarely be accepted. Pure case studies without methodological development are not acceptable for publication. Spatial statistics concerns the quantitative analysis of spatial and spatio-temporal data, including their statistical dependencies, accuracy and uncertainties. Methodology for spatial statistics is typically found in probability theory, stochastic modelling and mathematical statistics as well as in information science. Spatial statistics is used in mapping, assessing spatial data quality, sampling design optimisation, modelling of dependence structures, and drawing of valid inference from a limited set of spatio-temporal data.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信