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Small area estimation using spatio-temporal M-quantile models
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AI
DOI：10.1093/jrsssa/qnag055.png)
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Abstract
En 中文
The paper introduces a novel framework for small area estimation based on spatio-temporal M-quantile regression. The proposed approach extends the Geographically Weighted Regression by incorporating both spatial and temporal weighting schemes, and integrates them with the M-quantile modelling to effectively capture local distributional features across space and time. The resulting predictors are specifically designed for out-of-sample prediction in small domains and are accompanied by analytical estimators of their mean squared error. The methodology is evaluated through extensive simulation studies, demonstrating strong robustness to spatio-temporal dependence and the presence of outliers at both unit and area levels. An application to county-level air quality data in the United States (2016-2023) highlights the predictive performance and practical relevance of the proposed methods.
Keywords:
geographically weighted regression
M-quantile regression
mean squared error estimation
out-of-sample prediction
robust prediction
spatio-temporal modelling
Journal
J
IF:
1.6
Papers:
183
Citations:
0