Handle URI:
http://hdl.handle.net/10754/598411
Title:
Geostatistics for Large Datasets
Authors:
Sun, Ying; Li, Bo; Genton, Marc G.
Abstract:
Each chapter should be preceded by an abstract (10–15 lines long) that summarizes the content. The abstract will appear onlineat www.SpringerLink.com and be available with unrestricted access. This allows unregistered users to read the abstract as a teaser for the complete chapter. As a general rule the abstracts will not appear in the printed version of your book unless it is the style of your particular book or that of the series to which your book belongs. Please use the ’starred’ version of the new Springer abstractcommand for typesetting the text of the online abstracts (cf. source file of this chapter template abstract) and include them with the source files of your manuscript. Use the plain abstractcommand if the abstract is also to appear in the printed version of the book.
Citation:
Sun Y, Li B, Genton MG (2011) Geostatistics for Large Datasets. Advances and Challenges in Space-time Modelling of Natural Events: 55–77. Available: http://dx.doi.org/10.1007/978-3-642-17086-7_3.
Publisher:
Springer Science + Business Media
Journal:
Advances and Challenges in Space-time Modelling of Natural Events
KAUST Grant Number:
KUSC1-016-04
Issue Date:
31-Oct-2011
DOI:
10.1007/978-3-642-17086-7_3
Type:
Book Chapter
ISSN:
0930-0325
Sponsors:
The authors thank Reinhard Furrer for valuable comments on themanuscript. Li’s research was partially supported by NSF grant DMS-1007686. Genton’s researchwas partially supported by NSF grants DMS-1007504 and DMS-1100492, and by Award No.KUSC1-016-04, made by King Abdullah University of Science and Technology (KAUST).
Appears in Collections:
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Full metadata record

DC FieldValue Language
dc.contributor.authorSun, Yingen
dc.contributor.authorLi, Boen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2016-02-25T13:20:16Zen
dc.date.available2016-02-25T13:20:16Zen
dc.date.issued2011-10-31en
dc.identifier.citationSun Y, Li B, Genton MG (2011) Geostatistics for Large Datasets. Advances and Challenges in Space-time Modelling of Natural Events: 55–77. Available: http://dx.doi.org/10.1007/978-3-642-17086-7_3.en
dc.identifier.issn0930-0325en
dc.identifier.doi10.1007/978-3-642-17086-7_3en
dc.identifier.urihttp://hdl.handle.net/10754/598411en
dc.description.abstractEach chapter should be preceded by an abstract (10–15 lines long) that summarizes the content. The abstract will appear onlineat www.SpringerLink.com and be available with unrestricted access. This allows unregistered users to read the abstract as a teaser for the complete chapter. As a general rule the abstracts will not appear in the printed version of your book unless it is the style of your particular book or that of the series to which your book belongs. Please use the ’starred’ version of the new Springer abstractcommand for typesetting the text of the online abstracts (cf. source file of this chapter template abstract) and include them with the source files of your manuscript. Use the plain abstractcommand if the abstract is also to appear in the printed version of the book.en
dc.description.sponsorshipThe authors thank Reinhard Furrer for valuable comments on themanuscript. Li’s research was partially supported by NSF grant DMS-1007686. Genton’s researchwas partially supported by NSF grants DMS-1007504 and DMS-1100492, and by Award No.KUSC1-016-04, made by King Abdullah University of Science and Technology (KAUST).en
dc.publisherSpringer Science + Business Mediaen
dc.titleGeostatistics for Large Datasetsen
dc.typeBook Chapteren
dc.identifier.journalAdvances and Challenges in Space-time Modelling of Natural Eventsen
dc.contributor.institutionDepartment of Statistics, Texas A&M University, College Station, TX, 77843-3143, USAen
dc.contributor.institutionDepartment of Statistics, Purdue University, West Lafayette, IN, 47907, USAen
kaust.grant.numberKUSC1-016-04en
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