Multilevel latent Gaussian process model for mixed discrete and continuous multivariate response data

DOI10.1007/s13253-013-0136-zzbMath1303.62095arXiv1205.4163OpenAlexW1999784573MaRDI QIDQ486069

Erin M. Schliep, Jennifer A. Hoeting

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://arxiv.org/abs/1205.4163

zbMATH Keywords

Bayesianlatent variablemixed response datawetland condition

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15)

Related Items (7)

Extending ordinal regression with a latent zero-augmented beta distribution ⋮ Modeling skewed spatial data using a convolution of Gaussian and log-Gaussian processes ⋮ Data augmentation and parameter expansion for independent or spatially correlated ordinal data ⋮ Model-based clustering of trends and cycles of nitrate concentrations in rivers across France ⋮ Latent multivariate log-gamma models for high-dimensional multitype responses with application to daily fine particulate matter and mortality counts ⋮ Joint Bayesian analysis of multiple response-types using the hierarchical generalized transformation model ⋮ Large-scale inference of correlation among mixed-type biological traits with phylogenetic multivariate probit models

Uses Software

• R
• BayesDA

Cites Work

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• Generalized spatial structural equation models
• Investigating the sensitivity of Gaussian processes to the choice of their correlation function and prior specifications
• Bayesian Tests and Model Diagnostics in Conditionally Independent Hierarchical Models
• Latent Variable Analysis of Multivariate Spatial Data
• Bayesian Analysis of Binary and Polychotomous Response Data
• Spatially Balanced Sampling of Natural Resources

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