Statistical Properties of Covariance Tapers

71 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Likelihood approximation with hierarchical matrices for large spatial datasets (2017, background): “For instance, covariance tapering sometimes performs even worse than assuming independent blocks in the covariance [62]; low-rank approximations have their own limitations [63]; and Markov models depend on the observation locations, requiring irregular locations to be realigned on a much finer grid ”
A Case Study Competition Among Methods for Analyzing Large Spatial Data (2017, methodology): “…(1) into a series of conditional distributions (Vecchia 1988; Stein et al. 2004), the use of pseudo-likelihoods (Varin et al. 2011; Eidsvik et al. 2014), modeling in the spectral domain (Fuentes 2007) or using tapered covariance functions (Furrer et al. 2006; Kaufman et al. 2008; Stein 2013).”
Estimation and prediction using generalized Wendland covariance functions under fixed domain asymptotics (2016, unknown): “Extensions have been discussed by, e.g., Stein (2013) and Hirano and Yajima (2013).”