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• W2146454060 abstract "Random fields play a central role in the analysis of spatially correlated data and, as a result, have a significant impact on a broad array of scientific applications. This paper studies the cepstral random field model, providing recursive formulas that connect the spatial cepstral coefficients to an equivalent moving-average random field, which facilitates easy computation of the autocovariance matrix. We also provide a comprehensive treatment of the asymptotic theory for two-dimensional random field models: we establish asymptotic results for Bayesian, maximum likelihood and quasi-maximum likelihood estimation of random field parameters and regression parameters. The theoretical results are presented generally and are of independent interest, pertaining to a wide class of random field models. The results for the cepstral model facilitate model-building: because the cepstral coefficients are unconstrained in practice, numerical optimization is greatly simplified, and we are always guaranteed a positive definite covariance matrix. We show that inference for individual coefficients is possible, and one can refine models in a disciplined manner. Our results are illustrated through simulation and the analysis of straw yield data in an agricultural field experiment." @default.
• W2146454060 created "2016-06-24" @default.
• W2146454060 creator A5032943807 @default.
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• W2146454060 date "2014-02-01" @default.
• W2146454060 modified "2023-09-26" @default.
• W2146454060 title "Asymptotic theory of cepstral random fields" @default.
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• W2146454060 doi "https://doi.org/10.1214/13-aos1180" @default.
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