On estimation and selection of autologistic regression models via penalized pseudolikelihood

DOI10.1007/s13253-013-0144-zzbMath1303.62068OpenAlexW1968302188MaRDI QIDQ486061

Michelle M. Steen-Adams, Tingjin Chu, Rao Fu, Andrew L. Thurman, Jun Zhu

Publication date: 14 January 2015

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

Full work available at URL: https://doi.org/10.1007/s13253-013-0144-z

zbMATH Keywords

model selectionspatial statisticsrandom fieldvariable selectionbinary datamaximum pseudolikelihood estimation

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Applications of statistics to environmental and related topics (62P12)

Related Items (2)

High dimensional classification for spatially dependent data with application to neuroimaging ⋮ Composite likelihood estimation for models of spatial ordinal data and spatial proportional data with zero/one values

Uses Software

• R
• spBayes

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Autologistic models with interpretable parameters
• The Adaptive Lasso and Its Oracle Properties
• Nonconcave penalized composite conditional likelihood estimation of sparse Ising models
• Model-based geostatistics.
• The importance of scale for spatial-confounding bias and precision of spatial regression estimators
• One-step sparse estimates in nonconcave penalized likelihood models
• Spatial statistics and modeling. Translated from the French by Kevin Bleakley.
• Least angle regression. (With discussion)
• Autologistic regression analysis of spatial-temporal binary data via Monte Carlo maximum likelihood
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• Estimating spatial covariance using penalised likelihood with weightedL₁penalty
• A central limit theorem for conditionally centred random fields with an application to Markov fields
• On Selection of Spatial Linear Models for Lattice Data
• Markov Chain Monte Carlo for Autologistic Regression Models with Application to the Distribution of Plant Species
• Dimension Reduction and Alleviation of Confounding for Spatial Generalized Linear Mixed Models
• Statistics for Spatial Data
• Regularization Parameter Selections via Generalized Information Criterion
• Bayesian Analysis of Crossclassified Spatial Data with Autocorrelation
• An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants
• Spatial multinomial regression models for nominal categorical data: a study of land cover in Northern Wisconsin, USA

This page was built for publication: On estimation and selection of autologistic regression models via penalized pseudolikelihood