A spatial logistic regression model based on a valid skew-Gaussian latent field

DOI10.1007/s13253-022-00512-3WikidataQ114220107 ScholiaQ114220107MaRDI QIDQ6045978

Vahid Tadayon, Mohammad Mehdi Saber

Publication date: 15 May 2023

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

zbMATH Keywords

MCEM algorithmspatial modelingbinary spatial datanon-Gaussian random field

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Cites Work

• Approximate Bayesian inference in spatial GLMM with skew normal latent variables
• Computational techniques for spatial logistic regression with large data sets
• On the existence of some skew-Gaussian random field models
• Inference from iterative simulation using multiple sequences
• Non-Gaussian covariate-dependent spatial measurement error model for analyzing big spatial data
• Autologistic regression analysis of spatial-temporal binary data via Monte Carlo maximum likelihood
• Analysis of binary spatial data by quasi-likelihood estimating equations
• Maximum likelihood estimation of models for residual covariance in spatial regression
• A new class of multivariate skew distributions with applications to bayesian regression models