Adaptively robust geographically weighted regression

Shonosuke Sugasawa, Daisuke Murakami

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number100623
JournalSpatial Statistics
Volume48
DOIs
Publication statusPublished - 2022 Apr
Externally publishedYes

Keywords

  • Majorization–Minimization algorithm
  • Outliers
  • Robust divergence

ASJC Scopus subject areas

  • Statistics and Probability
  • Computers in Earth Sciences
  • Management, Monitoring, Policy and Law

Fingerprint

Dive into the research topics of 'Adaptively robust geographically weighted regression'. Together they form a unique fingerprint.

Cite this