@article{ref1,
title="From regression analysis to deep learning: development of improved proxy measures of state-level household gun ownership",
journal="Patterns (New York, N.Y.)",
year="2020",
author="Gomez, David Benjamin and Xu, Zhaoyi and Saleh, Joseph Homer",
volume="1",
number="9",
pages="e100154-e100154",
abstract="In the absence of direct measurements of state-level household gun ownership (GO), the quality and accuracy of proxy measures for this variable are essential for firearm-related research and policy development. In this work, we develop two highly accurate proxy measures of GO using traditional regression analysis and deep learning, the former accounting for non-linearities in the covariates (portion of suicides committed with a firearm [FS/S] and hunting license rates) and their statistical interactions. We subject the proxies to extensive model diagnostics and validation. Both our regression-based and deep-learning proxy measures provide highly accurate models of GO with training R2 of 96% and 98%, respectively, along with other desirable qualities-stark improvements over the prevalent FS/S proxy (R2 = 0.68). Model diagnostics reveal this widely used FS/S proxy is highly biased and inadequate; we recommend that it no longer be used to represent state-level household gun ownership in firearm-related studies.<p /> <p>Language: en</p>",
language="en",
issn="2666-3899",
doi="10.1016/j.patter.2020.100154",
url="http://dx.doi.org/10.1016/j.patter.2020.100154"
}