6533b834fe1ef96bd129e0be
RESEARCH PRODUCT
On the convenience of heteroscedasticity in highly multivariate disease mapping
Miguel A. Martinez-beneitoPaloma Botella-rocamoraFrancisca Corpas-burgossubject
Statistics and ProbabilityHeteroscedasticityMultivariate statisticsComputer scienceDiseaseJoint analysisMachine learningcomputer.software_genreBayesian statistics01 natural sciencesGaussian Markov random fields010104 statistics & probability03 medical and health sciences0302 clinical medicineHomoscedasticity0101 mathematicsMultivariate disease mappingSpatial analysisMortality studiesInterpretation (logic)Spatial statisticsbusiness.industryBayesian statisticsEstadística bayesianaMalalties030211 gastroenterology & hepatologyArtificial intelligenceStatistics Probability and Uncertaintybusinesscomputerdescription
Highly multivariate disease mapping has recently been proposed as an enhancement of traditional multivariate studies, making it possible to perform the joint analysis of a large number of diseases. This line of research has an important potential since it integrates the information of many diseases into a single model yielding richer and more accurate risk maps. In this paper we show how some of the proposals already put forward in this area display some particular problems when applied to small regions of study. Specifically, the homoscedasticity of these proposals may produce evident misfits and distorted risk maps. In this paper we propose two new models to deal with the variance-adaptivity problem in multivariate disease mapping studies and give some theoretical insights on their interpretation.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-02-06 |