A Bayesian mixture model to quantify parameters of spatial clustering

DOI10.1016/J.CSDA.2015.07.004zbMath1468.62172OpenAlexW2169370587MaRDI QIDQ1663157

Y. Aharonov

Publication date: 21 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2015.07.004

zbMATH Keywords

spatial statisticspoint processescluster analysisnearest neighbor distancesgamma mixtureMatérn cluster process

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Inference from spatial processes (62M30) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15)

Uses Software

• Matlab
• R
• Rapidstorm
• clusfind

Cites Work

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• On a class of Bayesian nonparametric estimates: I. Density estimates
• Bayesian regularization for normal mixture estimation and model-based clustering
• Spatial variation. 2nd ed
• Hierarchical clustering schemes
• Finite mixture and Markov switching models.
• Clustering in the Presence of Scatter
• Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering
• Finding Groups in Data
• Bayesian cluster analysis
• Nearest-Neighbor Clutter Removal for Estimating Features in Spatial Point Processes
• Non‐ and semi‐parametric estimation of interaction in inhomogeneous point patterns
• Finite mixture models
• Bayesian Inference for Gene Expression and Proteomics
• Improved criteria for clustering based on the posterior similarity matrix

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