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Authors: Caramenti, Luca* 
Menafoglio, Alessandra* 
Sgobba, Sara* 
Lanzano, Giovanni* 
Title: Multi-source geographically weighted regression for regionalized ground-motion models
Journal: Spatial Statistics 
Series/Report no.: /47 (2022)
Publisher: Elsevier
Issue Date: 2022
DOI: 10.1016/j.spasta.2022.100610
Abstract: This work proposes a novel approach to the calibration of region- alized regression models, with particular reference to ground- motion models (GMMs), which are key for probabilistic seismic hazard analysis and earthquake engineering applications. A novel methodology, named multi-source geographically-weighted re- gression (MS-GWR), is developed, allowing one to (i) estimate regionalized regression models depending on multiple sources of non-stationarity (such as site- and event-dependent non- stationarities in GMMs), and (ii) make inference on the sig- nificance and stationarity of the regression coefficients. Unlike previous approaches to the problem, the proposed framework is non-parametric – in the sense of the distribution of the errors – the inference being based on a permutation scheme. MS-GWR is here used to calibrate a new regionalized ground-motion model for predicting peak ground acceleration in Italy, based on a large scale database of waveforms and metadata made available by the Italian Institute for Geophysics and Vulcanology (INGV).
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