Show simple item record

dc.contributor.authorBopp, Gregory P.
dc.contributor.authorShaby, Benjamin A.
dc.contributor.authorHuser, Raphaël
dc.date.accessioned2020-09-03T13:59:19Z
dc.date.available2020-09-03T13:59:19Z
dc.date.issued2020
dc.identifier.citationBopp, G. P., Shaby, B. A., & Huser, R. (2020). A Hierarchical Max-Infinitely Divisible Spatial Model for Extreme Precipitation [Data set]. Taylor & Francis. https://doi.org/10.6084/M9.FIGSHARE.12071430
dc.identifier.doi10.6084/m9.figshare.12071430
dc.identifier.urihttp://hdl.handle.net/10754/664927
dc.description.abstractUnderstanding the spatial extent of extreme precipitation is necessary for determining flood risk and adequately designing infrastructure (e.g., stormwater pipes) to withstand such hazards. While environmental phenomena typically exhibit weakening spatial dependence at increasingly extreme levels, limiting max-stable process models for block maxima have a rigid dependence structure that does not capture this type of behavior. We propose a flexible Bayesian model from a broader family of (conditionally) max-infinitely divisible processes that allows for weakening spatial dependence at increasingly extreme levels, and due to a hierarchical representation of the likelihood in terms of random effects, our inference approach scales to large datasets. Therefore, our model not only has a flexible dependence structure, but it also allows for fast, fully Bayesian inference, prediction and conditional simulation in high dimensions. The proposed model is constructed using flexible random basis functions that are estimated from the data, allowing for straightforward inspection of the predominant spatial patterns of extremes. In addition, the described process possesses (conditional) max-stability as a special case, making inference on the tail dependence class possible. We apply our model to extreme precipitation in North-Eastern America, and show that the proposed model adequately captures the extremal behavior of the data. Interestingly, we find that the principal modes of spatial variation estimated from our model resemble observed patterns in extreme precipitation events occurring along the coast (e.g., with localized tropical cyclones and convective storms) and mountain range borders. Our model, which can easily be adapted to other types of environmental datasets, is therefore useful to identify extreme weather patterns and regions at risk.
dc.publisherTaylor & Francis
dc.subjectBiotechnology
dc.subject59999 Environmental Sciences not elsewhere classified
dc.subjectEcology
dc.subject69999 Biological Sciences not elsewhere classified
dc.subject80699 Information Systems not elsewhere classified
dc.subject19999 Mathematical Sciences not elsewhere classified
dc.titleA Hierarchical Max-Infinitely Divisible Spatial Model for Extreme Precipitation
dc.typeDataset
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.institutionDepartment of Statistics, Pennsylvania State University, University Park, PA,;
dc.contributor.institutionDepartment of Statistics, Colorado State University, Fort Collins, CO,;
kaust.personHuser, Raphaël
dc.relation.issupplementtoDOI:10.1080/01621459.2020.1750414
display.relations<b> Is Supplement To:</b><br/> <ul> <li><i>[Article]</i> <br/> Bopp, G. P., Shaby, B. A., & Huser, R. (2020). A Hierarchical Max-Infinitely Divisible Spatial Model for Extreme Precipitation. Journal of the American Statistical Association, 1–14. doi:10.1080/01621459.2020.1750414. DOI: <a href="https://doi.org/10.1080/01621459.2020.1750414" >10.1080/01621459.2020.1750414</a> HANDLE: <a href="http://hdl.handle.net/10754/632525">10754/632525</a></li></ul>


This item appears in the following Collection(s)

Show simple item record