Evidence map›Paper›PMID 38283734›Full record

ArticleJournal of the American Statistical Association2023

Sparse Reduced Rank Huber Regression in High Dimensions.

Kean Ming Tan, Qiang Sun, Daniela Witten

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Statistical Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Robust convex biclustering with a tuning-free method.Journal of applied statistics · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Kean Ming TanDepartment of Statistics, University of Michigan, Ann Arbor, MI.
Qiang SunDepartment of Statistical Sciences, University of Toronto, Toronto, ON, Canada.
Daniela WittenDepartments of Statistics and Biostatistics, University of Washington, Seattle, WA.

Funding

A Modeling Framework for Multi-View Data, with Applications to the Pioneer 100 Study and Protein Interaction NetworksR01GM123993 · NIGMS · UNIVERSITY OF WASHINGTON · PI BIEN, JACOB, WITTEN, DANIELA · 2017 to 2020
$1.3M
Statistical Methods for Stimulus-Locked Brain Connectivity Networks with Unmeasured ConfoundersR21MH122833 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI TAN, KEAN MING · 2020 to 2020
$149k
NIGMS NIH HHS R01 GM123993NIMH NIH HHS R21 MH122833
6 · The paper itself

Abstract

We propose a sparse reduced rank Huber regression for analyzing large and complex high-dimensional data with heavy-tailed random noise. The proposed method is based on a convex relaxation of a rank- and sparsity-constrained nonconvex optimization problem, which is then solved using a block coordinate descent and an alternating direction method of multipliers algorithm. We establish nonasymptotic estimation error bounds under both Frobenius and nuclear norms in the high-dimensional setting. This is a major contribution over existing results in reduced rank regression, which mainly focus on rank selection and prediction consistency. Our theoretical results quantify the tradeoff between heavy-tailedness of the random noise and statistical bias. For random noise with bounded

Indexed as

approximationConvex relaxationHuberlossLow rankSparsity

Identifiers

PMID38283734
PMCPMC10812838

What OpenQuestion holds

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Registered trials

None linked

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.