A smoothed ANOVA model for multivariate ecological regression   总被引：3，自引：3，他引：0  

Marc Marí-Dell’Olmo  Miguel A. Martinez-Beneito  Mercè Gotsens  Laia Palència 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(3):695-706

Smoothed analysis of variance (SANOVA) has recently been proposed for carrying out disease mapping. The main advantage of this approach is its conceptual simplicity and ease of interpretation. Moreover, it allows us to fix the combination of diseases of particular interest in advance and to make specific inferences about them. In this paper we propose a reformulation of SANOVA in the context of ecological regression studies. This proposal considers the introduction in a non-parametric way of one (or several) covariate(s) into the model, explaining some pre-specified combinations of the outcome variables. In addition, random effects are also incorporated in order to model geographical variation in the combinations of outcome variables not explained by the covariate. Lastly, the model permits the decomposition of the variance in the set of outcome variables into different orthogonal components, quantifying the contribution of every one of them. The proposed model is applied to the geographical analysis of mortality due to malignant stomach neoplasm among women resident in the city of Barcelona (Spain). The available outcome variables are deaths grouped into two time periods, and a socioeconomic deprivation index is included as a covariate. The model has been implemented through INLA, a novel inference tool for Bayesian statistics.  相似文献   

On the use of adaptive spatial weight matrices from disease mapping multivariate analyses     

Corpas-Burgos  Francisca  Martinez-Beneito  Miguel A. 《Stochastic Environmental Research and Risk Assessment (SERRA)》2020,34(3):531-544

Stochastic Environmental Research and Risk Assessment - Conditional autoregressive distributions are commonly used to model spatial dependence between nearby geographic units in disease mapping...  相似文献   

STANOVA: a smoothed-ANOVA-based model for spatio-temporal disease mapping     

Francisco Torres-Avilés  Miguel A. Martinez-Beneito 《Stochastic Environmental Research and Risk Assessment (SERRA)》2015,29(1):131-141

Spatio-temporal disease mapping can be viewed as a multivariate disease mapping problem with a given order of the geographic patterns to be studied. As a consequence, some of the techniques in multivariate literature could also be used to build spatio-temporal models. In this paper we propose using the smoothed ANOVA multivariate model for spatio-temporal problems. Under our approach the time trend for each geographic unit is modeled parametrically, projecting it on a preset orthogonal basis of functions (the contrasts in the smoothed ANOVA nomenclature), while the coefficients of these projections are considered to be spatially dependent random effects. Despite the parametric temporal nature of our proposal, we show with both simulated and real datasets that it may be as flexible as other spatio-temporal smoothing models proposed in the literature and may model spatio-temporal data with several sources of variability.  相似文献   

A spatio-temporal hierarchical Markov switching model for the early detection of influenza outbreaks     

Amor&#;s  Rub&#;n  Conesa  David  L&#;pez-Qu&#;lez  Antonio  Martinez-Beneito  Miguel-Angel 《Stochastic Environmental Research and Risk Assessment (SERRA)》2020,34(2):275-292

Stochastic Environmental Research and Risk Assessment - Rapidly detecting the beginning of influenza outbreaks helps health authorities to reduce their impact. Accounting for the spatial...  相似文献