NDLI: Covariance Tapering for Interpolation of Large Spatial Datasets

Please wait, while we are loading the content...

Covariance Tapering for Interpolation of Large Spatial Datasets

Content Provider	Scilit
Author	Furrer, Reinhard Genton, Marc G. Nychka, Douglas
Copyright Year	2006
Description	Journal: Journal of Computational and Graphical Statistics Interpolation of a spatially correlated random process is used in many scientific areas. The best unbiased linear predictor, often called a kriging predictor in geostatistical science, requires the solution of a (possibly large) linear system based on the covariance matrix of the observations. In this article, we show that tapering the correct covariance matrix with an appropriate compactly supported positive definite function reduces the computational burden significantly and still leads to an asymptotically optimal mean squared error. The eect of tapering is to create a sparse approximate linear system that can then be solved using sparse matrix algorithms. Monte Carlo simulations support the theoretical results. An application to a large climatological precipitation dataset is presented as a concrete and practical illustration.
Related Links	https://www.zora.uzh.ch/id/eprint/21612/2/ZORA-21612V.pdf http://www.zora.uzh.ch/id/eprint/21612/2/ZORA-21612V.pdf
Ending Page	523
Page Count	22
Starting Page	502
ISSN	10618600
e-ISSN	15372715
DOI	10.1198/106186006x132178
Journal	Journal of Computational and Graphical Statistics
Issue Number	3
Volume Number	15
Language	English
Publisher	Informa UK Limited
Publisher Date	2006-09-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Computational and Graphical Statistics Statistics and Probability Sparse Matrix Large Linear Systems Compactly Supported Covariance. Asymptotic Optimality Linear System Random Process Sparse Approximation Mean Square Error Monte Carlo Simulation Positive Definite Function Covariance Matrix Spatial Correlation
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Discrete Mathematics and Combinatorics Statistics, Probability and Uncertainty

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Covariance tapering for interpolation of large spatial datasets (2006)

Covariance tapering for interpolation of large spatial datasets (2006)

Covariance Tapering for Likelihood Based Estimation in Large Spatial Datasets

Hierarchical Low Rank Approximation of Likelihoods for Large Spatial Datasets

spam: A Sparse Matrix R Package with Emphasis on MCMC Methods for Gaussian Markov Random Fields

spam: A Sparse Matrix R Package with Emphasis on MCMC Methods for Gaussian Markov Random Fields

Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets

A Multiresolution Gaussian Process Model for the Analysis of Large Spatial Datasets

Covariance tapering for likelihoodbased estimation in large spatial data sets (2008)

Covariance Tapering for Interpolation of Large Spatial Datasets

Similar Documents

Covariance tapering for interpolation of large spatial datasets (2006)

Covariance tapering for interpolation of large spatial datasets (2006)

Covariance Tapering for Likelihood Based Estimation in Large Spatial Datasets

Hierarchical Low Rank Approximation of Likelihoods for Large Spatial Datasets

spam: A Sparse Matrix R Package with Emphasis on MCMC Methods for Gaussian Markov Random Fields

spam: A Sparse Matrix R Package with Emphasis on MCMC Methods for Gaussian Markov Random Fields

Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets

A Multiresolution Gaussian Process Model for the Analysis of Large Spatial Datasets

Covariance tapering for likelihoodbased estimation in large spatial data sets (2008)

Covariance Tapering for Interpolation of Large Spatial Datasets