ArticleThe annals of applied statistics2024
A PARTIALLY FUNCTIONAL LINEAR REGRESSION FRAMEWORK FOR INTEGRATING GENETIC, IMAGING, AND CLINICAL DATA.
Article in The annals of applied statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- S-GMAS: Genome-Wide Mediation Analysis With Brain Subcortical Shape Mediators.Human brain mapping · 2025Article
- High-Dimensional Multiresponse Partially Functional Linear Regression.Statistics in medicine · 2025Article
- A PARTIALLY FUNCTIONAL LINEAR REGRESSION FRAMEWORK FOR INTEGRATING GENETIC, IMAGING, AND CLINICAL DATA.The annals of applied statistics · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This paper is motivated by the joint analysis of genetic, imaging, and clinical (GIC) data collected in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We propose a partially functional linear regression (PFLR) framework to map high-dimensional GIC-related pathways for Alzheimer's disease (AD). We develop a joint model selection and estimation procedure by embedding imaging data in the reproducing kernel Hilbert space and imposing the
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Registered trials
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