TY - JOUR
T1 - Generalized sparse precision matrix selection for fitting multivariate Gaussian random fields to large data sets
AU - Tajbakhsh, Sam Davanloo
AU - Aybat, Necdet Serhat
AU - Del Castillo, Enrique
N1 - Publisher Copyright:
© 2018 Institute of Statistical Science. All rights reserved.
PY - 2018/4
Y1 - 2018/4
N2 - 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.
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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U2 - 10.5705/ss.202017.0091
DO - 10.5705/ss.202017.0091
M3 - Editorial
AN - SCOPUS:85045672610
SN - 1017-0405
VL - 28
SP - 941
EP - 962
JO - Statistica Sinica
JF - Statistica Sinica
IS - 2
ER -