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 journalEditorial

1 Citation (Scopus)

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

Fingerprint

Gaussian Random Field
Large Data Sets
del operator
Spatial Correlation
Covariance matrix
Convergence Rate
Correlation Function
Segmentation
Scalar
Model-based
Simulation
Random field
Model
Spatial correlation
Convergence rate

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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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.",
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Generalized sparse precision matrix selection for fitting multivariate Gaussian random fields to large data sets. / Tajbakhsh, Sam Davanloo; Aybat, Necdet Serhat; Del Castillo, Enrique.

In: Statistica Sinica, Vol. 28, No. 2, 04.2018, p. 941-962.

Research output: Contribution to journalEditorial

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AU - Del Castillo, Enrique

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AB - 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.

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