Modeling Longitudinal Spatial Periodontal Data: A Spatially Adaptive Model with Tools for Specifying Priors and Checking Fit

DOI10.1111/j.1541-0420.2007.00956.xzbMath1170.62399OpenAlexW2065714299WikidataQ31142122 ScholiaQ31142122MaRDI QIDQ3530096

Brian J. Reich, James S. Hodges

Publication date: 15 October 2008

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2007.00956.x

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50)

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Cites Work

• Bayesian image restoration, with two applications in spatial statistics (with discussion)
• Generalized autoregressive conditional heteroscedasticity
• Spatial stochastic volatility for lattice data
• Counting degrees of freedom in hierarchical and other richly-parameterised models
• On the Precision of the Conditionally Autoregressive Prior in Spatial Models
• A smoothness priors time-varying AR coefficient modeling of nonstationary covariance time series
• Generalized Hierarchical Multivariate CAR Models for Areal Data
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Bayesian Measures of Model Complexity and Fit
• Effects of Residual Smoothing on the Posterior of the Fixed Effects in Disease‐Mapping Models
• Spatial Analyses of Periodontal Data Using Conditionally Autoregressive Priors Having Two Classes of Neighbor Relations
• Proper multivariate conditional autoregressive models for spatial data analysis

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