Local Variable and Neighborhood Selection for Firearm Fatality in the Southeast USA

TL;DR

This paper introduces a novel two-step localized variable selection framework for analyzing spatially correlated firearm fatality data, enabling identification of influential socio-economic factors and neighborhood structures specific to each location.

Contribution

It develops a new method combining local variable selection with inference on neighborhood structures, addressing a gap in spatially correlated, over-dispersed data analysis.

Findings

The method effectively identifies local socio-economic predictors of firearm fatalities.

It provides inference on directional variation in spatial neighborhood structures.

The approach improves modeling accuracy by tailoring neighborhood assumptions to each location.

Abstract

A major public health concern in the United States (US) is gun-related deaths. The number of gun injuries largely varies spatially because of county-wise heterogeneity of race, sex, age, and income distributions. But still, a major challenge is to locally identify the influential socio-economic factors behind these firearm fatality incidents. For a diverging number of predictors, a rich literature exists regarding SCAD under the independence framework; however, a vacuum remains when discussing local variable selection for spatially correlated, over-dispersed data. This research presents a two-step localized variable selection and inference framework for spatially indexed gunshot fatality data. In the first step, we select variables locally using the SCAD penalty for specific locations where the number of gunshot incidents exceeds a threshold. For these locations, after selecting the predictors, we proceed to the next step, which involves examining the directional variation in the latent spatial neighborhood structure. We further discuss the theoretical properties of this county-specific local variable selection under infill asymptotics. This method has threefold advantages: (i) this method selects the variables locally, (ii) this method provides inference about directional variation of a selected predictor, and (iii) instead of assuming the spatial neighborhood structure in an ad hoc manner, this method identifies the specific type of spatial neighborhood structure that is most appropriate for modeling the random effects.