Alleviating confounding in spatio-temporal areal models with an application on crimes against women in India

Abstract

Assessing associations between a response of interest and a set of covariates in spatial areal models is the leitmotiv of ecological regression. However, the presence of spatially correlated random effects can mask or even bias estimates of such associations due to confounding effects if they are not carefully handled. Though potentially harmful, confounding issues have often been ignored in practice leading to wrong conclusions about the underlying associations between the response and the covariates. In spatio-temporal areal models, the temporal dimension may emerge as a new source of confounding, and the problem may be even worse. In this work, we propose two approaches to deal with confounding of fixed effects by spatial and temporal random effects. In particular, restricted regression and an apparently -though in fact not -equivalent procedure using constraints are proposed within both fully Bayes and empirical Bayes approaches. The methods are compared in terms of fixed-effect estimates and model selection criteria. The techniques are used to assess the association between dowry deaths and certain socio-demographic covariates in the districts of Uttar Pradesh, India

Comparative assessment of satellite‐ and drone‐based vegetation indices to predict arthropod biomass in shrub‐steppes

“…problem, one potential solution is to constrain the spatial random effect to be orthogonal to those fixed effects with a spatial pattern (Adin et al, 2020;Hodges & Reich, 2010). We tested for spatial confounding in our models by fitting linear regression models incorporating the spatial random intercepts for each observation (response variable) and the VIs as predictors (Hanks et al, 2015).…”