Reduced-Dimensional Monte Carlo Maximum Likelihood for Latent Gaussian Random Field Models

DOI10.1080/10618600.2020.1811106OpenAlexW3080285932MaRDI QIDQ5066383

Jaewoo Park, Murali Haran

Publication date: 29 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.09711

zbMATH Keywords

importance samplingMarkov chain Monte Carlodimension reductionnon-Gaussian spatial dataMonte Carlo maximum likelihood

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Log-Gaussian Cox process modeling of large spatial lightning data using spectral and Laplace approximations

Uses Software

• GMRFLib
• Rcpp
• spBayes
• PMTK
• bernor

Cites Work

• An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
• MCMC maximum likelihood for latent state models
• Markov chain Monte Carlo: can we trust the third significant figure?
• Convergence of the Monte Carlo expectation maximization for curved exponential families.
• Inference from iterative simulation using multiple sequences
• Estimation and prediction for spatial generalized linear mixed models using high order Laplace approximation
• Monte Carlo likelihood inference for missing data models
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• On Estimation and Prediction for Spatial Generalized Linear Mixed Models
• Bayesian Prediction of Spatial Count Data Using Generalized Linear Mixed Models
• A Computationally Efficient Projection-Based Approach for Spatial Generalized Linear Mixed Models
• Gaussian Predictive Process Models for Large Spatial Data Sets
• Model-Based Geostatistics
• On Block Updating in Markov Random Field Models for Disease Mapping
• Statistical-mechanical approach to image processing
• Hidden Markov Models and Disease Mapping
• Gaussian Markov Random Fields
• Multivariate output analysis for Markov chain Monte Carlo
• Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models
• Handbook of Spatial Epidemiology
• Effects of Residual Smoothing on the Posterior of the Fixed Effects in Disease‐Mapping Models
• Efficient Gaussian process regression for large datasets
• Inconsistent Estimation and Asymptotically Equal Interpolations in Model-Based Geostatistics
• Asymptotics of Maximum Likelihood without the LLN or CLT or Sample Size Going to Infinity
• Restricted spatial regression in practice: geostatistical models, confounding, and robustness under model misspecification
• Practical likelihood analysis for spatial generalized linear mixed models
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