Modelling Spatial Intensity for Replicated Inhomogeneous Point Patterns in Brain Imaging

DOI10.1046/j.1369-7412.2003.05285.xzbMath1062.62221OpenAlexW2139180752MaRDI QIDQ4665856

No author found.

Publication date: 11 April 2005

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1046/j.1369-7412.2003.05285.x

zbMATH Keywords

krigingsingular value decompositionoverdispersionsimulationsanalysis of varianceVoronoi tessellationgeneralized linear mixed modelinhomogeneous Poisson likelihoodpoint warpingpseudosplinethalamic activation

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Generalized linear models (logistic models) (62J12) Biomedical imaging and signal processing (92C55)

Related Items (5)

Semiparametric regression during 2003--2007 ⋮ A Geostatistical Approach to Large‐Scale Disease Mapping with Temporal Misalignment ⋮ Unnamed Item ⋮ Bias correction for parameter estimates of spatial point process models ⋮ Computational techniques for spatial logistic regression with large data sets

Uses Software

• TRIPACK
• SemiPar
• Splancs

Cites Work

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• GLIM and normalising constant models in spatial and directional data analysis
• Parametric Procedures in the Analysis of Replicated Pairwise Interaction Point Patterns
• Simple Incorporation of Interactions into Additive Models
• Geoadditive Models
• Model-Based Geostatistics
• Log Gaussian Cox Processes
• Algorithm 751: TRIPACK
• Semiparametric Regression
• A comparison between parametric and non-parametric approaches to the analysis of replicated spatial point patterns
• Practical Maximum Pseudolikelihood for Spatial Point Patterns
• What can modern statistics offer imaging neuroscience?
• Automated identification of Fos expression

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