Sparse principal component based high-dimensional mediation analysis

DOI10.1016/j.csda.2019.106835OpenAlexW2807747040WikidataQ99629449 ScholiaQ99629449MaRDI QIDQ2008127

Yanyan Li

Publication date: 22 November 2019

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1806.06118

zbMATH Keywords

structural equation modelregularized regressionfunctional magnetic resonance imagingmediation analysis

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to biology and medical sciences; meta analysis (62P10) Biomedical imaging and signal processing (92C55)

Related Items (7)

Nonlinear mediation analysis with high‐dimensional mediators whose causal structure is unknown ⋮ On optimal two‐stage testing of multiple mediators ⋮ Multimodal neuroimaging data integration and pathway analysis ⋮ Mediation analysis method review of high throughput data ⋮ Mediation analysis for high-dimensional mediators and outcomes with an application to multimodal imaging data ⋮ Estimations and Tests for Generalized Mediation Models with High-Dimensional Potential Mediators ⋮ Principal regression for high dimensional covariance matrices

Uses Software

• medflex
• PMA
• HIMA
• MultiMed

Cites Work

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• Causal Inference Using Potential Outcomes

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