ArticleJournal of the American Statistical Association2022
Integrative Factor Regression and Its Inference for Multimodal Data Analysis.
Article in Journal of the American Statistical Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- Bayesian joint and individual component regression for multigroup physiological data.Scientific reports · 2026Article
- Integrative Analysis of Multimodal Omics Data.Annual review of statistics and its application · 2026Article
- MULTI-OBJECT DATA INTEGRATION IN THE STUDY OF PRIMARY PROGRESSIVE APHASIA.The annals of applied statistics · 2025Article
- Fault diagnosis in electric motors using multi-mode time series and ensemble transformers network.Scientific reports · 2025Article
- Are Latent Factor Regression and Sparse Regression Adequate?Journal of the American Statistical Association · 2024Article
- Joint and Individual Component Regression.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Aligned deep neural network for integrative analysis with high-dimensional input.Journal of biomedical informatics · 2023Article
- Orthogonalized Kernel Debiased Machine Learning for Multimodal Data Analysis.Journal of the American Statistical Association · 2023Article
- Uncovering Diverse Mechanistic Spreading Pathways in Disease Progression of Alzheimer's Disease.Journal of Alzheimer's disease reports · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Multimodal data, where different types of data are collected from the same subjects, are fast emerging in a large variety of scientific applications. Factor analysis is commonly used in integrative analysis of multimodal data, and is particularly useful to overcome the curse of high dimensionality and high correlations. However, there is little work on statistical inference for factor analysis based supervised modeling of multimodal data. In this article, we consider an integrative linear regression model that is built upon the latent factors extracted from multimodal data. We address three important questions: how to infer the significance of one data modality given the other modalities in the model; how to infer the significance of a combination of variables from one modality or across different modalities; and how to quantify the contribution, measured by the goodness-of-fit, of one data modality given the others. When answering each question, we explicitly characterize both the benefit and the extra cost of factor analysis. Those questions, to our knowledge, have not yet been addressed despite wide use of factor analysis in integrative multimodal analysis, and our proposal bridges an important gap. We study the empirical performance of our methods through simulations, and further illustrate with a multimodal neuroimaging analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.