Composite likelihood estimation for a Gaussian process under fixed domain asymptotics
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Publication:2008225
DOI10.1016/j.jmva.2019.104534zbMath1428.62419arXiv1807.08988OpenAlexW2963135460MaRDI QIDQ2008225
Moreno Bevilacqua, Daira Velandia, François Bachoc
Publication date: 22 November 2019
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1807.08988
consistencyasymptotic normalityGaussian processesexponential modellarge data setsfixed-domain asymptoticsmicroergodic parameterspairwise composite likelihood
Asymptotic properties of parametric estimators (62F12) Inference from spatial processes (62M30) Random fields; image analysis (62M40) Gaussian processes (60G15) Point estimation (62F10)
Related Items (5)
Fixed-domain asymptotic properties of maximum composite likelihood estimators for Gaussian processes ⋮ Assessing the effective sample size for large spatial datasets: a block likelihood approach ⋮ Statistical test for anomalous diffusion based on empirical anomaly measure for Gaussian processes ⋮ Asymptotic Analysis of Maximum Likelihood Estimation of Covariance Parameters for Gaussian Processes: An Introduction with Proofs ⋮ Bayesian fixed-domain asymptotics for covariance parameters in a Gaussian process model
Uses Software
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