Multi-level restricted maximum likelihood covariance estimation and kriging for large non-gridded spatial datasets

Julio Castrillon, Marc G. Genton, Rio Yokota

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

We develop a multi-level restricted Gaussian maximum likelihood method for estimating the covariance function parameters and computing the best unbiased predictor. Our approach produces a new set of multi-level contrasts where the deterministic parameters of the model are filtered out thus enabling the estimation of the covariance parameters to be decoupled from the deterministic component. Moreover, the multi-level covariance matrix of the contrasts exhibit fast decay that is dependent on the smoothness of the covariance function. Due to the fast decay of the multi-level covariance matrix coefficients only a small set is computed with a level dependent criterion. We demonstrate our approach on problems of up to 512,000 observations with a Matérn covariance function and highly irregular placements of the observations. In addition, these problems are numerically unstable and hard to solve with traditional methods.
Original languageEnglish (US)
Pages (from-to)105-124
Number of pages20
JournalSpatial Statistics
Volume18
DOIs
StatePublished - Nov 10 2015

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Statistics and Probability
  • Management, Monitoring, Policy and Law

Fingerprint

Dive into the research topics of 'Multi-level restricted maximum likelihood covariance estimation and kriging for large non-gridded spatial datasets'. Together they form a unique fingerprint.

Cite this