Show simple item record

dc.contributor.authorHuang, Huang
dc.contributor.authorSun, Ying
dc.date.accessioned2020-07-28T13:38:43Z
dc.date.available2020-07-28T13:38:43Z
dc.date.issued2019
dc.identifier.citationHuang, H., & Sun, Y. (2019). Hierarchical Low Rank Approximation of Likelihoods for Large Spatial Datasets [Data set]. Taylor & Francis. https://doi.org/10.6084/M9.FIGSHARE.5427271.V2
dc.identifier.doi10.6084/m9.figshare.5427271.v2
dc.identifier.urihttp://hdl.handle.net/10754/664470
dc.description.abstractDatasets in the fields of climate and environment are often very large and irregularly spaced. To model such datasets, the widely used Gaussian process models in spatial statistics face tremendous challenges due to the prohibitive computational burden. Various approximation methods have been introduced to reduce the computational cost. However, most of them rely on unrealistic assumptions for the underlying process and retaining statistical efficiency remains an issue. We develop a new approximation scheme for maximum likelihood estimation. We show how the composite likelihood method can be adapted to provide different types of hierarchical low rank approximations that are both computationally and statistically efficient. The improvement of the proposed method is explored theoretically; the performance is investigated by numerical and simulation studies; and the practicality is illustrated through applying our methods to two million measurements of soil moisture in the area of the Mississippi River basin, which facilitates a better understanding of the climate variability. Supplementary material for this article is available online.
dc.publisherfigshare
dc.subject59999 Environmental Sciences not elsewhere classified
dc.subject69999 Biological Sciences not elsewhere classified
dc.subject80699 Information Systems not elsewhere classified
dc.subject19999 Mathematical Sciences not elsewhere classified
dc.titleHierarchical Low Rank Approximation of Likelihoods for Large Spatial Datasets
dc.typeDataset
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEnvironmental Statistics Group
dc.contributor.departmentStatistics Program
kaust.personHuang, Huang
kaust.personSun, Ying
dc.relation.issupplementtoDOI:10.1080/10618600.2017.1356324
display.relations<b> Is Supplement To:</b><br/> <ul> <li><i>[Article]</i> <br/> Huang H, Sun Y (2017) Hierarchical Low Rank Approximation of Likelihoods for Large Spatial Datasets. Journal of Computational and Graphical Statistics 27: 110–118. Available: http://dx.doi.org/10.1080/10618600.2017.1356324.. DOI: <a href="https://doi.org/10.1080/10618600.2017.1356324" >10.1080/10618600.2017.1356324</a> HANDLE: <a href="http://hdl.handle.net/10754/631117">10754/631117</a></li></ul>


This item appears in the following Collection(s)

Show simple item record