[2402.00183] A review of regularised estimation methods and cross-validation in spatiotemporal statistics Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Methodology arXiv:2402.00183 (stat) [Submitted on 31 Jan 2024 ( v1 ), last revised 15 May 2024 (this version, v2)] Title: A review of regularised estimation methods and cross-validation in spatiotemporal statistics Authors: Philipp Otto , Alessandro Fassò , Paolo Maranzano View a PDF of the paper titled A review of regularised estimation methods and cross-validation in spatiotemporal statistics, by Philipp Otto and 2 other authors View PDF HTML (experimental) Abstract: This review article focuses on regularised estimation procedures applicable to geostatistical and spatial econometric models. These methods are particularly relevant in the case of big geospatial data for dimensionality reduction or model selection. To structure the review, we initially consider the most general case of multivariate spatiotemporal processes (i.e., $g > 1$ dimensions of the spatial domain, a one-dimensional temporal domain, and $q \geq 1$ random variables). Then, the idea of regularised/penalised estimation procedures and different choices of shrinkage targets are discussed. Finally, guided by the elements of a mixed-effects model setup, which allows for a variety of spatiotemporal models, we show different regularisation procedures and how they can be used for the analysis of geo-referenced data, e.g. for selection of relevant regressors, dimensionality reduction of the covariance matrices, detection of conditionally independent locations, or the estimation of a full spatial interaction matrix. Subjects: Methodology (stat.ME) ; Computation (stat.CO); Other Statistics (stat.OT) Cite as: arXiv:2402.00183 [stat.ME] (or arXiv:2402.00183v2 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2402.00183 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1214/24-SS150 Focus to learn more DOI(s) linking to related resources Submission history From: Philipp Otto [ view email ] [v1] Wed, 31 Jan 2024 21:24:21 UTC (349 KB) [v2] Wed, 15 May 2024 13:11:10 UTC (608 KB) Full-text links: Access Paper: View a PDF of the paper titled A review of regularised estimation methods and cross-validation in spatiotemporal statistics, by Philipp Otto and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: stat.ME < prev | next > new | recent | 2024-02 Change to browse by: stat stat.CO stat.OT References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from