ArticleBiometrika2013
Reduced rank regression via adaptive nuclear norm penalization.
Article in Biometrika, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Fast autoregressive model for multivariate dependent outcomes with application to lipidomics analysis for Alzheimer's disease and APOE-Computational statistics & data analysis · 2026Article
- Reduced varying coefficient models for regional quantile regression with multiple responses.Biometrics · 2026Article
- Smooth optimization using global and local low-rank regularizers.SIAM journal on imaging sciences · 2026Article
- Multiple augmented reduced rank regression for pan-cancer analysis.Biometrics · 2024Article
- A tensor based varying-coefficient model for multi-modal neuroimaging data analysis.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2024Article
- Low-rank regression models for multiple binary responses and their applications to cancer cell-line encyclopedia data.Journal of the American Statistical Association · 2024Article
- LOCUS: A REGULARIZED BLIND SOURCE SEPARATION METHOD WITH LOW-RANK STRUCTURE FOR INVESTIGATING BRAIN CONNECTIVITY.The annals of applied statistics · 2023Article
- Sparse Reduced Rank Huber Regression in High Dimensions.Journal of the American Statistical Association · 2023Article
- Minimizing the distortions in electrophysiological source imaging of cortical oscillatory activityFrontiers in neuroscience · 2023Article
- Stability Approach to Regularization Selection for Reduced-Rank Regression.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Sparse Single Index Models for Multivariate Responses.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2021Article
- Sparse reduced-rank regression for integrating omics data.BMC bioinformatics · 2020Article
- Bayesian sparse multiple regression for simultaneous rank reduction and variable selection.Biometrika · 2020Article
- Matrix Linear Discriminant Analysis.Technometrics : a journal of statistics for the physical, chemical, and engineering sciences · 2020Article
- SOFAR: Large-Scale Association Network Learning.IEEE transactions on information theory · 2019Article
- Article
- Nuclear penalized multinomial regression with an application to predicting at bat outcomes in baseball.Statistical modelling · 2018Article
- Robust reduced-rank regression.Biometrika · 2017Article
- Bayesian sparse reduced rank multivariate regression.Journal of multivariate analysis · 2017Article
- Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications.NeuroImage · 2017Article
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3 authors.
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Abstract
We propose an adaptive nuclear norm penalization approach for low-rank matrix approximation, and use it to develop a new reduced rank estimation method for high-dimensional multivariate regression. The adaptive nuclear norm is defined as the weighted sum of the singular values of the matrix, and it is generally non-convex under the natural restriction that the weight decreases with the singular value. However, we show that the proposed non-convex penalized regression method has a global optimal solution obtained from an adaptively soft-thresholded singular value decomposition. The method is computationally efficient, and the resulting solution path is continuous. The rank consistency of and prediction/estimation performance bounds for the estimator are established for a high-dimensional asymptotic regime. Simulation studies and an application in genetics demonstrate its efficacy.
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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.