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• W2942141331 abstract "This article is motivated by the problem of inference on interactions among chemical exposures impacting human health outcomes. Chemicals often co-occur in the environment or in synthetic mixtures and as a result exposure levels can be highly correlated. We propose a latent factor joint model, which includes shared factors in both the predictor and response components while assuming conditional independence. By including a quadratic regression in the latent variables in the response component, we induce flexible dimension reduction in characterizing main effects and interactions. We propose a Bayesian approach to inference under this Factor analysis for INteractions (FIN) framework. Through appropriate modifications of the factor modeling structure, FIN can accommodate higher order interactions and multivariate outcomes. We provide theory on posterior consistency and the impact of misspecifying the number of factors. We evaluate the performance using a simulation study and data from the National Health and Nutrition Examination Survey (NHANES). Code is available on GitHub." @default.
• W2942141331 created "2019-05-03" @default.
• W2942141331 creator A5002768909 @default.
• W2942141331 creator A5015766904 @default.
• W2942141331 date "2019-04-25" @default.
• W2942141331 modified "2023-09-27" @default.
• W2942141331 title "Bayesian Factor Analysis for Inference on Interactions" @default.
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• W2942141331 hasPublicationYear "2019" @default.
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