{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/modelling-spatial-heteroskedasticity-by","title":"Modelling spatial heteroskedasticity by volatility modulated moving averages","arxiv_id":"1609.04682","date":"2016-09-15","proceeding":null,"authors":["Michele Nguyen","Almut E. D. Veraart"],"abstract":"Spatial heteroskedasticity refers to stochastically changing variances and covariances in space. Such features have been observed in, for example, air pollution and vegetation data. We study how volatility modulated moving averages can model this by developing theory, simulation and statistical inference methods. For illustration, we also apply our procedure to sea surface temperature anomaly data from the International Research Institute for Climate and Society.","url_abs":"http://arxiv.org/abs/1609.04682v1","url_pdf":"http://arxiv.org/pdf/1609.04682v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"modelling-spatial-heteroskedasticity-by","repo_url":"https://bitbucket.org/mntd/vmma-processes","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}