Estimation for single-index spatial autoregressive model with covariate measurement errors

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics Pub Date : 2023-12-21 DOI:10.1016/j.spasta.2023.100805

Ke Wang , Dehui Wang

引用次数: 0

Abstract

This paper explores the estimators of parameters for a spatial data single-index model which has measurement errors of covariates in the nonparametric part. The related estimations are considered to combine a local-linear smoother based simulation-extrapolation (SIMEX) algorithm, the estimation equation and the estimation method for profile maximum likelihood. Under regular conditions, asymptotic properties of the link function and uncertain estimators are derived. As verified in simulations, the performance of the estimators is satisfactory. Finally, an application to a real dataset is illustrated.

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具有协变量测量误差的单指数空间自回归模型的估计

本文探讨了空间数据单指数模型的参数估计方法，该模型的非参数部分存在协变量的测量误差。相关估计结合了基于局部线性平滑器的模拟外推法（SIMEX）算法、估计方程和轮廓最大似然估计方法。在常规条件下，得出了链接函数和不确定估计器的渐近特性。通过模拟验证，估计器的性能令人满意。最后，对实际数据集的应用进行了说明。

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.