A unifying modeling framework for highly multivariate disease mapping

2015 ◽  
Vol 34 (9) ◽  
pp. 1548-1559 ◽  
Author(s):  
P. Botella-Rocamora ◽  
M.A. Martinez-Beneito ◽  
S. Banerjee
Keyword(s):  
Disease Mapping ◽  
Modeling Framework ◽  
Multivariate Disease Mapping

scholarly journals On the convenience of heteroscedasticity in highly multivariate disease mapping

Test ◽  
2019 ◽  
Vol 28 (4) ◽  
pp. 1229-1250 ◽  
Author(s):  
F. Corpas-Burgos ◽  
P. Botella-Rocamora ◽  
M. A. Martinez-Beneito
Keyword(s):  
Disease Mapping ◽  
Multivariate Disease Mapping

scholarly journals Bayesian multi-scale modeling for aggregated disease mapping data

2015 ◽  
Vol 26 (6) ◽  
pp. 2726-2742 ◽  
Author(s):  
Mehreteab Aregay ◽  
Andrew B Lawson ◽  
Christel Faes ◽  
Russell S Kirby
Keyword(s):  
Random Effect ◽  
Disease Mapping ◽  
Deviance Information Criterion ◽  
Simulated Data ◽  
Information Criterion ◽  
Modeling Framework ◽  
Multi Scale ◽  
Shared Random Effects ◽  
Convolution Model ◽  
Resolution Level

In disease mapping, a scale effect due to an aggregation of data from a finer resolution level to a coarser level is a common phenomenon. This article addresses this issue using a hierarchical Bayesian modeling framework. We propose four different multiscale models. The first two models use a shared random effect that the finer level inherits from the coarser level. The third model assumes two independent convolution models at the finer and coarser levels. The fourth model applies a convolution model at the finer level, but the relative risk at the coarser level is obtained by aggregating the estimates at the finer level. We compare the models using the deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC) that are applied to real and simulated data. The results indicate that the models with shared random effects outperform the other models on a range of criteria.

On Gaussian Markov random fields and Bayesian disease mapping

2010 ◽  
Vol 20 (1) ◽  
pp. 49-68 ◽  
Author(s):  
Ying C MacNab
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Spatial Interaction ◽  
Spatial Regression ◽  
Disease Mapping ◽  
Model Framework ◽  
Gaussian Markov Random Fields ◽  
Markov Random ◽  
Mapping Models ◽  
Multivariate Disease Mapping

We discuss the nature of Gaussian Markov random fields (GMRFs) as they are typically formulated via full conditionals, also named conditional autoregressive or CAR formulations, to represent small area relative risks ensemble priors within a Bayesian hierarchical model framework for statistical inference in disease mapping and spatial regression. We present a partial review on GMRF/CAR and multivariate GMRF prior formulations in univariate and multivariate disease mapping models and communicate insights into various prior characteristics for representing disease risks variability and ‘spatial interaction.’ We also propose convolution prior modifications to the well known BYM model for attainment of identifiability and Bayesian robustness in univariate and multivariate disease mapping and spatial regression. Several illustrative examples of disease mapping and spatial regression are presented.

Modelling Gas Processing Unit: An Inter-disciplinary Approach Based on Petri Nets

2008 ◽  
Vol 59 (7) ◽  
Author(s):  
Sanda Florentina Mihalache
Keyword(s):  
Industrial Process ◽  
Industrial Processes ◽  
Processing Unit ◽  
Industrial System ◽  
Modeling Framework ◽  
Gas Processing ◽  
Human Interactions ◽  
Modelling Method ◽  
Effective Representation ◽  
Modelling Approach

A modelling approach that will facilitate an in-depth understanding of the interactions of the different phenomena, human interactions and environmental factors constituting �real world� industrial processes is presented. An important industrial system such as Gas Processing Unit (GPU) have inter-related internal process activities coexisting with external events and requires a real time inter-disciplinary approach to model them. This modeling framework is based on identifying as modules, the part of processes that have interactions and can be considered active participants in overall behaviour. The selected initial set of modules are structured as Petri net models and made to interact iteratively to provide process states of the system. The modeling goal is accomplished by identifying the evolution of the process states as a means of effective representation of the �actual running�� of the industrial process. The paper discusses the function and the implementation of the modelling method as applicable to the industrial case of GPU.

Sign in / Sign up

Export Citation Format

Share Document