Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/46402
Title: A low-rank Bayesian approach for geoadditive modeling
Authors: SUMALINAB, Bryan 
GRESSANI, Oswaldo 
HENS, Niel 
FAES, Christel 
Issue Date: 2025
Publisher: ELSEVIER SCI LTD
Source: Spatial statistics, 68 (Art N° 100907)
Abstract: Kriging is an established methodology for predicting spatial data in geostatistics. Current kriging techniques can handle linear dependencies on spatially referenced covariates. Although splines have shown promise in capturing nonlinear dependencies of covariates, their combination with kriging, especially in handling count data, remains underexplored. This paper proposes a new Bayesian approach to the low-rank representation of geoadditive models, which integrates splines and kriging to account for both spatial correlations and nonlinear dependencies of covariates. The proposed method accommodates Gaussian and count data inherent in many geospatial datasets. Additionally, Laplace approximations to selected posterior distributions enhances computational efficiency, resulting in faster computation times compared to Markov chain Monte Carlo techniques commonly used for Bayesian inference. Method performance is assessed through a simulation study, demonstrating the effectiveness of the proposed approach. The methodology is applied to the analysis of heavy metal concentrations in the Meuse river and vulnerability to the coronavirus disease 2019 (COVID-19) in Belgium.
Notes: Sumalinab, B (corresponding author), Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat I BioSta, Data Sci Inst DSI, Agoralaan Gebouw D, B-3590 Diepenbeek, Belgium.
bryan.sumalinab@uhasselt.be
Keywords: Kriging;Geoadditive models;Bayesian P-splines;Laplace approximations;Low-rank model
Document URI: http://hdl.handle.net/1942/46402
ISSN: 2211-6753
DOI: 10.1016/j.spasta.2025.100907
ISI #: 001507005700001
Rights: 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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