On the public citation graph
Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping
0 signed · 54 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/1097-0258(20000915/30)19:17/18<2555::aid-sim587>3.0.co;2-#openalex-snapshot
Bayesian modelling of inseparable space‐time variation in disease risk
inferredcites →https://doi.org/10.1002/bimj.201500248openalex-snapshot
Hierarchical multivariate mixture generalized linear models for the analysis of spatial data: An application to disease mapping
inferredcites →https://doi.org/10.1002/env.2410openalex-snapshot
Spatio‐temporal Bayesian model selection for disease mapping
inferredcites →https://doi.org/10.1002/sim.4780142112openalex-snapshot
Bayesian analysis of space—time variation in disease risk
inferredcites →https://doi.org/10.1002/sim.6955openalex-snapshot
Linear models of coregionalization for multivariate lattice data: a general framework for coregionalized multivariate CAR models
inferredcites →https://doi.org/10.1007/bf00116466openalex-snapshot
Bayesian image restoration, with two applications in spatial statistics
inferredcites →https://doi.org/10.1007/s10552-004-1661-4openalex-snapshot
The epidemiology of vitamin D and cancer incidence and mortality: A review (United States)
inferredcites →https://doi.org/10.1007/s11222-013-9416-2openalex-snapshot
Understanding predictive information criteria for Bayesian models
inferredcites →https://doi.org/10.1007/s13253-012-0100-3openalex-snapshot
Bayesian 2-Stage Space-Time Mixture Modeling With Spatial Misalignment of the Exposure in Small Area Health Data
inferredcites →https://doi.org/10.1007/s40300-014-0047-yopenalex-snapshot
Negotiating multicollinearity with spike-and-slab priors
inferredcites →https://doi.org/10.1016/j.annepidem.2015.10.011openalex-snapshot
Bayesian model selection methods in modeling small area colon cancer incidence
inferredcites →https://doi.org/10.1016/j.annepidem.2016.08.014openalex-snapshot
Space-time variation of respiratory cancers in South Carolina: a flexible multivariate mixture modeling approach to risk estimation
inferredcites →https://doi.org/10.1016/j.sste.2015.08.001openalex-snapshot
Comparing INLA and OpenBUGS for hierarchical Poisson modeling in disease mapping
inferredcites →https://doi.org/10.1080/01621459.1997.10474012openalex-snapshot
Hierarchical Spatio-Temporal Mapping of Disease Rates
inferredcites →https://doi.org/10.1080/09332480.2013.868759openalex-snapshot
The BUGS Book: A Practical Introduction to Bayesian Analysis
inferredcites →https://doi.org/10.1093/biostatistics/4.1.11openalex-snapshot
Proper multivariate conditional autoregressive models for spatial data analysis
inferredcites →https://doi.org/10.1093/biostatistics/kxs005openalex-snapshot
BaySTDetect: detecting unusual temporal patterns in small area data via Bayesian model choice
inferredcites →https://doi.org/10.1111/1467-985x.00187openalex-snapshot
A Shared Component Model for Detecting Joint and Selective Clustering of Two Diseases
inferredcites →https://doi.org/10.1111/1467-9868.00353openalex-snapshot
Bayesian Measures of Model Complexity and Fit
inferredcites →https://doi.org/10.1111/1467-9876.00229openalex-snapshot
Bayesian Inference for Generalized Additive Mixed Models Based on Markov Random Field Priors
inferredcites →https://doi.org/10.1111/j.1467-9876.2012.01039.xopenalex-snapshot
A Bayesian Hierarchical Model with Spatial Variable Selection: The Effect of Weather on Insurance Claims
inferredcites →https://doi.org/10.1111/j.1541-0420.2006.00617.xopenalex-snapshot
Effects of Residual Smoothing on the Posterior of the Fixed Effects in Disease‐Mapping Models
inferredcites →https://doi.org/10.1111/j.1541-0420.2010.01391.xopenalex-snapshot
Joint Variable Selection for Fixed and Random Effects in Linear Mixed‐Effects Models
inferredcites →https://doi.org/10.1111/j.2517-6161.1993.tb01467.xopenalex-snapshot
Spatial Statistics and Bayesian Computation
inferredcites →https://doi.org/10.1177/0962280212446319openalex-snapshot
Prospective surveillance of multivariate spatial disease data
inferredcites →https://doi.org/10.1177/0962280215627298openalex-snapshot
Spatially-dependent Bayesian model selection for disease mapping
inferredcites →https://doi.org/10.1198/016214501753168109openalex-snapshot
Spatiotemporal Hierarchical Bayesian Modeling Tropical Ocean Surface Winds
inferredcites →https://doi.org/10.1198/106186002501openalex-snapshot
Bayesian Variable and Transformation Selection in Linear Regression
inferredcites →https://doi.org/10.1198/tast.2010.10052openalex-snapshot
Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love
inferredcites →https://doi.org/10.1214/06-ba117aopenalex-snapshot
Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper)
inferredcites →https://doi.org/10.1214/14-ba873openalex-snapshot
Spatial Bayesian Variable Selection Models on Functional Magnetic Resonance Imaging Time-Series Data
inferredcites →https://doi.org/10.1214/16-ba995openalex-snapshot
Towards a Multidimensional Approach to Bayesian Disease Mapping
inferredcites →https://doi.org/10.1214/ss/1177011136openalex-snapshot
Inference from Iterative Simulation Using Multiple Sequences
inferredcites →https://doi.org/10.17615/b1pe-2b05openalex-snapshot
VARIABLE SELECTION FOR REGRESSION MODELS WITH MISSING DATA
inferredcites →https://doi.org/10.48550/arxiv.1004.2316openalex-snapshot
Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/env.2681openalex-snapshot
Spatiotemporal clustering using Gaussian processes embedded in a mixture model
inferred← citeshttps://doi.org/10.1007/s00038-020-01384-5openalex-snapshot
A systematic review of Bayesian spatial–temporal models on cancer incidence and mortality
inferred← citeshttps://doi.org/10.1007/s00477-023-02466-5openalex-snapshot
Adaptive LASSO estimation for functional hidden dynamic geostatistical models
inferred← citeshttps://doi.org/10.1016/j.clcc.2018.12.001openalex-snapshot
Trends in Colorectal Cancer Incidence and Survival in Iowa SEER Data: The Timing of It All
inferred← citeshttps://doi.org/10.1016/j.spasta.2021.100508openalex-snapshot
Quantifying the small-area spatio-temporal dynamics of the Covid-19 pandemic in Scotland during a period with limited testing capacity
inferred← citeshttps://doi.org/10.1016/j.sste.2023.100588openalex-snapshot
A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba
inferred← citeshttps://doi.org/10.1093/biostatistics/kxy023openalex-snapshot
Temporally dependent accelerated failure time model for capturing the impact of events that alter survival in disease mapping
inferred← citeshttps://doi.org/10.1111/biom.13295openalex-snapshot
Estimating the burden of the opioid epidemic for adults and adolescents in Ohio counties
inferred← citeshttps://doi.org/10.1145/3451393openalex-snapshot
A Joint Passenger Flow Inference and Path Recommender System for Deploying New Routes and Stations of Mass Transit Transportation
inferred← citeshttps://doi.org/10.1155/tbed/4994209openalex-snapshot
Spatial Patterns and Epidemiological Drivers of Foot‐and‐Mouth Disease Outbreaks in Uganda
inferred← citeshttps://doi.org/10.1186/s12982-026-02040-8openalex-snapshot
Spatio temporal projection of cancer incidence in India using artificial neural networks
inferred← citeshttps://doi.org/10.1214/24-ss150openalex-snapshot
A review of regularised estimation methods and cross-validation in spatiotemporal statistics
inferred← citeshttps://doi.org/10.3390/ijerph14050503openalex-snapshot
Extensions to Multivariate Space Time Mixture Modeling of Small Area Cancer Data
inferred← citeshttps://doi.org/10.3390/ijerph19063467openalex-snapshot
Spatial Analysis of Shared Risk Factors between Pleural and Ovarian Cancer Mortality in Lombardy (Italy)
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.