ArticleThe annals of applied statistics2021
PERTURBED FACTOR ANALYSIS: ACCOUNTING FOR GROUP DIFFERENCES IN EXPOSURE PROFILES.
Article in The annals of applied statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Tutorial on Bayesian Multi-Study Factor Analysis With Applications in Nutrition and Genomics.Statistics in medicine · 2026Article
- Inferring Covariance Structure from Multiple Data Sources via Subspace Factor Analysis.Journal of the American Statistical Association · 2025Article
- Exposome Burden Scores to Summarize Environmental Chemical Mixtures: Creating a Fair and Common Scale for Cross-study Harmonization, Report-back and Precision Environmental Health.Current environmental health reports · 2025Review
- Fast Variational Inference for Bayesian Factor Analysis in Single and Multi-Study Settings.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- A Bayesian multivariate factor analysis model for causal inference using time-series observational data on mixed outcomes.Biostatistics (Oxford, England) · 2024Article
- A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures.Frontiers in public health · 2024Review
- Bayesian combinatorial MultiStudy factor analysis.The annals of applied statistics · 2023Article
- Principal Component Pursuit for Pattern Identification in Environmental Mixtures.Environmental health perspectives · 2022Article
- Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods.International journal of environmental research and public health · 2022Review
- PERTURBED FACTOR ANALYSIS: ACCOUNTING FOR GROUP DIFFERENCES IN EXPOSURE PROFILES.The annals of applied statistics · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
In this article we investigate group differences in phthalate exposure profiles using NHANES data. Phthalates are a family of industrial chemicals used in plastics and as solvents. There is increasing evidence of adverse health effects of exposure to phthalates on reproduction and neurodevelopment and concern about racial disparities in exposure. We would like to identify a single set of low-dimensional factors summarizing exposure to different chemicals, while allowing differences across groups. Improving on current multigroup additive factor models, we propose a class of Perturbed Factor Analysis (PFA) models that assume a common factor structure after perturbing the data via multiplication by a group-specific matrix. Bayesian inference algorithms are defined using a matrix normal hierarchical model for the perturbation matrices. The resulting model is just as flexible as current approaches in allowing arbitrarily large differences across groups but has substantial advantages that we illustrate in simulation studies. Applying PFA to NHANES data, we learn common factors summarizing exposures to phthalates, while showing clear differences across groups.
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