Journal ArticleDOI
A spatially adaptive conditional autoregressive prior for area health data
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TLDR
Halloran et al. as discussed by the authors proposed a spatially adaptive extension of Leroux et al.'s prior to reflect the fact that the appropriate mix between local and global smoothing may not be constant across the region being studied.About:
This article is published in Statistical Methodology.The article was published on 2008-11-01. It has received 14 citations till now. The article focuses on the topics: Spatial dependence & Strong prior.read more
Citations
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Journal ArticleDOI
Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach.
TL;DR: Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences and suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences.
Journal ArticleDOI
A multilevel model for cardiovascular disease prevalence in the US and its application to micro area prevalence estimates
TL;DR: The present study demonstrates by formal modelling methods that improved explanation is obtained by allowing for distinct geographic effects (for counties and states) and for interaction between geographic and person variables.
Journal ArticleDOI
Comparison of Bayesian Random-Effects and Traditional Life Expectancy Estimations in Small-Area Applications
Marcel F. Jonker,Frank J. van Lenthe,Peter Congdon,Bas Donkers,Alex Burdorf,Johan P. Mackenbach +5 more
TL;DR: In the presented Monte Carlo simulations, the Bayesian random-effects approach outperforms the traditional approach in terms of bias, root mean square error, and coverage of the 95% confidence intervals and is well-suited for estimation of life expectancies in small areas.
Journal ArticleDOI
Towards activity-based exposure measures in spatial analysis of pedestrian-motor vehicle crashes.
TL;DR: A Bayesian spatially varying coefficients model was developed and found that road density, intersection density, bus stop density, and the number of parking lots were found to be positively associated with PMV crash frequency, whereas the percentage of motorways and median monthly income had negative associations with the risk of PMV crashes.
Journal ArticleDOI
Some recent work on multivariate Gaussian Markov random fields
TL;DR: Some recent work on conditional formulation of multivariate Gaussian Markov random fields is presented, with a focus on model constructions by compatible conditionals and coregionalization.
References
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Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Journal ArticleDOI
Bayesian measures of model complexity and fit
TL;DR: In this paper, the authors consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined and derive a measure pD for the effective number in a model as the difference between the posterior mean of the deviances and the deviance at the posterior means of the parameters of interest, which is related to other information criteria and has an approximate decision theoretic justification.
BookDOI
Markov Chain Monte Carlo in Practice
TL;DR: The Markov Chain Monte Carlo Implementation Results Summary and Discussion MEDICAL MONITORING Introduction Modelling Medical Monitoring Computing Posterior Distributions Forecasting Model Criticism Illustrative Application Discussion MCMC for NONLINEAR HIERARCHICAL MODELS.
Journal ArticleDOI
Sampling-Based Approaches to Calculating Marginal Densities
TL;DR: In this paper, three sampling-based approaches, namely stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm, are compared and contrasted in relation to various joint probability structures frequently encountered in applications.
Journal Article
Sampling-based approaches to calculating marginal densities
TL;DR: Stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm can be viewed as three alternative sampling- (or Monte Carlo-) based approaches to the calculation of numerical estimates of marginal probability distributions.