Spatial regression modeling for compositional data with many zeros

DOI10.1007/s13253-013-0145-yzbMath1303.62085OpenAlexW1998120841WikidataQ121717104 ScholiaQ121717104MaRDI QIDQ486044

John A. jun. Silander, Thomas J. Leininger, Jenica M. Allen, Alan E. Gelfand

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/s13253-013-0145-y

zbMATH Keywords

Markov chain Monte Carlohierarchical modelingareal dataconditionally autoregressive modelcontinuous ranked probability score

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to environmental and related topics (62P12)

Related Items (8)

Time use of married couples: Bayesian approach ⋮ Score Matching for Compositional Distributions ⋮ A review of compositional data analysis and recent advances ⋮ Composite likelihood estimation for models of spatial ordinal data and spatial proportional data with zero/one values ⋮ Modelling changes in Arctic sea ice cover: an application of generalized and inflated beta and gamma densities ⋮ Flexible non-parametric regression models for compositional data ⋮ A Dirichlet regression model for compositional data with zeros ⋮ Colours and Cocktails: Compositional Data Analysis 2013 Lancaster Lecture

Uses Software

• compositions
• R
• CODA
• spBayes
• LFT

Cites Work

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