ArticleBiostatistics (Oxford, England)2016
Canonical variate regression.
Article in Biostatistics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A Bayesian Integrative Mixed Modeling Framework for Analysis of the Multi-Site Adolescent Brain and Cognitive Development Study.Data science in science · 2026Article
- Scalable randomized kernel methods for multiview data integration and prediction with application to Coronavirus disease.Biostatistics (Oxford, England) · 2025Article
- Extensions of Heterogeneity in Integration and Prediction (HIP) With R Shiny Application.Statistics in medicine · 2025Article
- The need for a cancer exposome atlas: a scoping review.JNCI cancer spectrum · 2025Article
- HIP: a method for high-dimensional multi-view data integration and prediction accounting for subgroup heterogeneity.Briefings in bioinformatics · 2024Article
- NetMIM: network-based multi-omics integration with block missingness for biomarker selection and disease outcome prediction.Briefings in bioinformatics · 2024Article
- DeepIDA-GRU: a deep learning pipeline for integrative discriminant analysis of cross-sectional and longitudinal multiview data with applications to inflammatory bowel disease classification.Briefings in bioinformatics · 2024Article
- HETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES.Statistica Sinica · 2023Article
- Bayesian integrative analysis and prediction with application to atherosclerosis cardiovascular disease.Biostatistics (Oxford, England) · 2022Article
- sJIVE: Supervised Joint and Individual Variation Explained.Computational statistics & data analysis · 2022Article
- Sparse linear discriminant analysis for multiview structured data.Biometrics · 2022Article
- Tangent functional canonical correlation analysis for densities and shapes, with applications to multimodal imaging data.Journal of multivariate analysis · 2022Article
- Article
- Vertical integration methods for gene expression data analysis.Briefings in bioinformatics · 2021Review
- Integration of Proteomics and Other Omics Data.Methods in molecular biology (Clifton, N.J.) · 2021Article
- A Selective Review of Multi-Level Omics Data Integration Using Variable Selection.High-throughput · 2019Review
- Assisted gene expression-based clustering with AWNCut.Statistics in medicine · 2018Article
- Adaptive Sparse Multiple Canonical Correlation Analysis With Application to Imaging (Epi)Genomics Study of Schizophrenia.IEEE transactions on bio-medical engineering · 2018Article
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Authors and funding
4 authors.
Funding
Abstract
In many fields, multi-view datasets, measuring multiple distinct but interrelated sets of characteristics on the same set of subjects, together with data on certain outcomes or phenotypes, are routinely collected. The objective in such a problem is often two-fold: both to explore the association structures of multiple sets of measurements and to develop a parsimonious model for predicting the future outcomes. We study a unified canonical variate regression framework to tackle the two problems simultaneously. The proposed criterion integrates multiple canonical correlation analysis with predictive modeling, balancing between the association strength of the canonical variates and their joint predictive power on the outcomes. Moreover, the proposed criterion seeks multiple sets of canonical variates simultaneously to enable the examination of their joint effects on the outcomes, and is able to handle multivariate and non-Gaussian outcomes. An efficient algorithm based on variable splitting and Lagrangian multipliers is proposed. Simulation studies show the superior performance of the proposed approach. We demonstrate the effectiveness of the proposed approach in an [Formula: see text] intercross mice study and an alcohol dependence study.
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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.