Parallel inference for massive distributed spatial data using low-rank models

DOI10.1007/S11222-016-9627-4zbMath1505.62217arXiv1402.1472OpenAlexW3122231250MaRDI QIDQ518241

Dorit Hammerling, Matthias Katzfuss

Publication date: 28 March 2017

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1402.1472

zbMATH Keywords

Gaussian processdistributed computingparticle filterpredictive processspatial random effects modelspatio-temporal statistics

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Computational methods for problems pertaining to statistics (62-08) Inference from stochastic processes and prediction (62M20) Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12)

Related Items (7)

An area-specific stick breaking process for spatial data ⋮ Areal prediction of survey data using Bayesian spatial generalised linear models ⋮ Distributed nearest-neighbor Gaussian processes ⋮ Multi-scale process modelling and distributed computation for spatial data ⋮ A case study competition among methods for analyzing large spatial data ⋮ Kryging: geostatistical analysis of large-scale datasets using Krylov subspace methods ⋮ Hierarchical sparse Cholesky decomposition with applications to high-dimensional spatio-temporal filtering

Uses Software

• FRK

Cites Work

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