Estimation and prediction for spatial generalized linear mixed models using high order Laplace approximation

DOI10.1016/j.jspi.2011.05.008zbMath1219.62112OpenAlexW1986684220WikidataQ60521602 ScholiaQ60521602MaRDI QIDQ2276187

Zhengyuan Zhu, Richard L. Smith, Evangelos Evangelou

Publication date: 1 August 2011

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: http://opus.bath.ac.uk/24192/1/Evangelou.pdf

zbMATH Keywords

maximum likelihood estimationspatial statisticspredictive inference

Mathematics Subject Classification ID

Related Items (15)

Approximate composite marginal likelihood inference in spatial generalized linear mixed models ⋮ Reduced-Dimensional Monte Carlo Maximum Likelihood for Latent Gaussian Random Field Models ⋮ The value of information for correlated GLMs ⋮ Approximate likelihood inference in spatial generalized linear mixed models with closed skew normal latent variables ⋮ Asymptotic properties of adaptive maximum likelihood estimators in latent variable models ⋮ Adaptive spatial designs minimizing the integrated Bernoulli variance in spatial logistic regression models -- with an application to benthic habitat mapping ⋮ Selection of fixed effects in high-dimensional generalized linear mixed models ⋮ Maximum Likelihood Algorithm for Spatial Generalized Linear Mixed Models without Numerical Evaluations of Intractable Integrals ⋮ Selection of Proposal Distributions for Multiple Importance Sampling ⋮ Improving the INLA approach for approximate Bayesian inference for latent Gaussian models ⋮ Practical likelihood analysis for spatial generalized linear mixed models ⋮ Approximate Bayesian inference for large spatial datasets using predictive process models ⋮ Optimal predictive design augmentation for spatial generalised linear mixed models ⋮ Efficient estimation and prediction for the Bayesian binary spatial model with flexible link functions ⋮ The dimension-wise quadrature estimation of dynamic latent variable models for count data

Uses Software

• R
• LBFGS-B
• geoRglm
• bootstrap

Cites Work

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