Adaptively robust geographically weighted regression

We develop a new robust geographically weighted regression method in the presence of outliers. We embed the standard geographically weighted regression in robust objective function based on γ-divergence. A novel feature of the proposed approach is that two tuning parameters that control robustness a...

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Vydáno v:Spatial statistics Ročník 48; s. 100623
Hlavní autoři: Sugasawa, Shonosuke, Murakami, Daisuke
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.04.2022
Témata:
Majorization–Minimization algorithm
Outliers
Robust divergence
Outliers
Robust divergence
Majorization–Minimization algorithm
ISSN:2211-6753, 2211-6753
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Shrnutí:We develop a new robust geographically weighted regression method in the presence of outliers. We embed the standard geographically weighted regression in robust objective function based on γ-divergence. A novel feature of the proposed approach is that two tuning parameters that control robustness and spatial smoothness are automatically tuned in a data-dependent manner. Further, the proposed method can produce robust standard error estimates and give us a reasonable quantity for local outlier detection. We demonstrate that the proposed method is superior to the existing robust geographically weighted regression through simulation and data analysis.
ISSN:2211-6753
2211-6753
DOI:10.1016/j.spasta.2022.100623