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Tensor quantile regression with low-rank tensor train estimation

The Annals of Applied Statistics(2024)10.1214/23-aoas1835Source: DataRank Database

Tensor quantile regression with low-rank tensor train estimation is a research paper published in The Annals of Applied Statistics (2024). On theSindex it has a DataRank of 0. It has been cited 7 times.

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Open Access7 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (4 grants)Impact (FWCI 1.54)OA: greenTopics (20)IDs (PubMed, PMC, OpenAlex)Annotations (1)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (3)

Cheuk Yin Lee ORCID,Heping Zhang ORCID,Zihuan Liu ORCID

Live enrichment

PubMed:38682044 PMC:PMC11046526 OpenAlex:https://openalex.org/W4394006146

NHGRI NIH HHS

Grant: R01 HG010171

NIMH NIH HHS

Grant: R01 MH116527

NCATS NIH HHS

Grant: UL1 TR001863

NIMH NIH HHS

Grant: U54 MH091657

FWCI

1.54

Citation Percentile

0.8%

Citation Trend

2024

2025

2026

green

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC11046526/pdf/nihms-1938886.pdf

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/11046526

journalhttps://doi.org/10.1214/23-aoas1835

repositoryhttps://pubmed.ncbi.nlm.nih.gov/38682044

Fields of Study

Tensor decomposition and applicationsSparse and Compressive Sensing TechniquesAdvanced Neuroimaging Techniques and Applications

Keywords

Tensor (intrinsic definition)MathematicsEstimatorQuantile regressionCurse of dimensionalityQuantileInterpretabilityMathematical optimizationComputer sciencePattern recognition (psychology)Artificial intelligenceApplied mathematicsStatisticsTotal VariationTensor RegressionConditional QuantileTensor Train (Tt) Decomposition

Sustainable Development Goals

SDG 0: Peace, Justice and strong institutions

Tensor quantile regression with low-rank tensor train estimation

FAIR Checklist

Authors (3)

Funding (4 grants)

Impact

Open Access

Topics

Annotations (Europe PMC)