Linked-Tucker Factorized Individualized Regression for Paired Multivariate Categorical Outcomes
Arkaprava Roy, Jeremy T. Gaskins, Steven Levy, Somnath Datta

TL;DR
This paper introduces a joint individualized hurdle-ordinal regression model with tensor factorization for analyzing complex, zero-inflated, multilevel dental health data, revealing spatially heterogeneous effects of fluoride and diet.
Contribution
It develops a novel linked Tucker tensor factorization approach with sparsity priors for high-dimensional, spatially varying effects in paired ordinal outcomes.
Findings
Fluoride increases odds and severity of fluorosis.
Soda intake raises caries risk.
Associations vary across locations, ages, and subpopulations.
Abstract
We propose a joint individualized hurdle-ordinal regression model for paired zero-inflated ordinal outcomes with subject-specific, spatially varying, and time-varying covariate effects, motivated by the Iowa Fluoride Study (IFS). The two outcomes, dental caries and dental fluorosis, are measured repeatedly across ages at fine spatial resolution, yielding nested longitudinal data with substantial zero inflation, ordinality, and heterogeneity across individuals and locations. For each outcome, a hurdle component models disease presence, while a proportional-odds component models severity among positive observations. To parsimoniously represent the high-dimensional coefficient arrays, we introduce a linked Tucker tensor factorization. Shared subject-mode factors induce dependence between the caries and fluorosis coefficient tensors, while separate spatial factors accommodate the distinct…
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