Bias correction methods for spatially lagged covariates measured with errors

IF 2.5 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics Pub Date : 2025-05-29 DOI:10.1016/j.spasta.2025.100909

Mohammad Masjkur , Asep Saefuddin , I. Wayan Mangku , Henk Folmer , Arno J. Van der Vlist , Marco Grzegorczyk

引用次数: 0

Abstract

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.

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带有误差测量的空间滞后协变量的偏差校正方法

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。

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来源期刊

Spatial Statistics GEOSCIENCES, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

4.00

自引率

21.70%

发文量

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.