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Large spatial data modeling and analysis: A Krylov subspace approach

  • Published:2021-10-11 Issue: Volume: Page:
  • ISSN:0303-6898
  • Container-title:Scandinavian Journal of Statistics
  • language:en
  • Short-container-title:Scand J Statist

Author:

Liu Jialuo1,Chu Tingjin2ORCID,Zhu Jun3,Wang Haonan1

Affiliation:

1. Department of Statistics Colorado State University Fort Collins Colorado USA

2. School of Mathematics and Statistics University of Melbourne Melbourne Victoria Australia

3. Department of Statistics University of Wisconsin‐Madison Madison Wisconsin USA

Funder

National Science Foundation of Sri Lanka

U.S. Geological Survey

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference59 articles.

1. A matrix‐free approach for solving the parametric Gaussian process maximum likelihood problem;Anitescu M.;SIAM Journal on Scientific Computing,2012

2. Gaussian predictive process models for large spatial data sets;Banerjee S.;Journal of the Royal Statistical Society: Series B,2008

3. The rational SPDE approach for Gaussian random fields with general smoothness;Bolin D.;Journal of Computational and Graphical Statistics,2020

4. Spatially adaptive covariance tapering;Bolin D.;Spatial Statistics,2016

5. A randomized algorithm for approximating the log determinant of a symmetric positive definite matrix;Boutsidis C.;Linear Algebra and Its Applications,2017

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. On maximizing the likelihood function of general geostatistical models;Scandinavian Journal of Statistics;2024-05-06
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