Generalized sparse precision matrix selection for fitting multivariate Gaussian random fields to large data sets

Sam Davanloo Tajbakhsh, Necdet Serhat Aybat, Enrique Del Castillo

Research output: Contribution to journalEditorialpeer-review

3 Scopus citations

Abstract

We present a new method for estimating multivariate, second-order stationary Gaussian Random Field (GRF) models based on the Sparse Precision matrix Selection (SPS) algorithm, proposed by Davanloo Tajbakhsh, Aybat and Del Castillo (2015) for estimating scalar GRF models. Theoretical convergence rates for the estimated between-response covariance matrix and for the estimated parameters of the underlying spatial correlation function are established. Numerical tests using simulations and datasets validate our theoretical findings. Data segmentation is used to handle large data sets.

Original languageEnglish (US)
Pages (from-to)941-962
Number of pages22
JournalStatistica Sinica
Volume28
Issue number2
DOIs
StatePublished - Apr 2018

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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