Shrinkage estimation in geographically weighted regression with applications to digital platform pricing

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dc.contributor.authorYuzbasi, Bahadir
dc.contributor.authorAhmed, S. Ejaz
dc.contributor.authorLiu, Shuangzhe
dc.date.accessioned2026-04-04T13:34:50Z
dc.date.available2026-04-04T13:34:50Z
dc.date.issued2026
dc.departmentİnönü Üniversitesi
dc.description.abstractGeographically Weighted Regression (GWR) is a spatial statistical technique used to examine how the effects of predictor variables on a response variable vary across different regions. This paper introduces a novel shrinkage estimation approach to improve parameter estimation in spatial regression models, particularly in data-rich environments such as digital platforms and fintech applications. The method enhances predictive accuracy by decomposing the regression coefficient vector into main and nuisance components, with the latter assumed to be close to zero. A shrinkage factor is then applied to adjust the full model estimates towards a more parsimonious submodel, enabling more robust and interpretable results. We provide theoretical justification for the proposed estimators, establishing their superiority over traditional GWR estimators, and demonstrate their effectiveness through extensive geostatistical simulations. Additionally, we apply the method to a real-world dataset from Airbnb pricing in Toronto, showing how the shrinkage approach outperforms conventional models in predicting regional price dynamics and financial outcomes in digital platform pricing settings.
dc.description.sponsorshipScientific Research Projects (BAP) Unit of Idot;noenue University, Turkiye [SBA-2025-4340]; Natural Sciences and Engineering Research Council of Canada (NSERC); University of Canberra
dc.description.sponsorshipThe authors would like to thank the Editor and the anonymous reviewers for their careful reading of the manuscript and for their constructive comments and suggestions, which helped to improve the clarity, presentation, and overall quality of this work. The authors also gratefully acknowledge Paul Harris for kindly sharing simulation materials and for his helpful guidance on the design of the simulation study. This research was supported by the Scientific Research Projects (BAP) Unit of & Idot;noenue University, Turkiye, under Project Number SBA-2025-4340. S. Ejaz Ahmed is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) . Shuangzhe Liu acknowledges the University of Canberra for its support during his research leave.
dc.identifier.doi10.1016/j.spasta.2026.100969
dc.identifier.issn2211-6753
dc.identifier.orcid0000-0002-6196-3201
dc.identifier.scopus2-s2.0-105030880264
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.spasta.2026.100969
dc.identifier.urihttps://hdl.handle.net/11616/109423
dc.identifier.volume73
dc.identifier.wosWOS:001704433100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofSpatial Statistics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectShrinkage estimation
dc.subjectGeographically Weighted Regression
dc.subjectDigital platform pricing
dc.subjectSpatial financial data
dc.subjectFinancial technology applications
dc.titleShrinkage estimation in geographically weighted regression with applications to digital platform pricing
dc.typeArticle

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