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A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensions

Statistics and Computing(2022)10.1007/s11222-022-10102-5Source: DataRank Database

A generalized likelihood-based Bayesian approach for scalable joint regression and covariance selection in high dimensions is a research paper published in Statistics and Computing (2022). On theSindex it has a DataRank of 0.312. It has been cited 7 times.

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0.312DataRank · unranked

Scored on demand from live citation data

Open Access7 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (4 grants)Impact (FWCI 1.10)OA: greenTopics (32)IDs (PubMed, PMC, OpenAlex)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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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

0.312

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

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Authors (3)

Kshitij Khare ORCID,George Michailidis ORCID,Srijata Samanta

Live enrichment

PubMed:36713060 PMC:PMC9881595 OpenAlex:https://openalex.org/W4281773409

National Institutes of Health

Grant: 1U01CA235487-01

Methods and Tools for Integrative Functional Enrichment Analysis of Metabolomics Data

National Institutes of Health

Grant: 1R01GM114029-01

NCI NIH HHS

Grant: U01 CA235487

National Institutes of Health

Grant: 1R01GM114029-01A1

Machine Learning Tools for Discovery and Analysis of Active Metabolic Pathways

FWCI

1.10

Citation Percentile

0.8%

Citation Trend

2023

2024

2025

2026

greenLicense: Springer TDM

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/9881595

publisherhttps://link.springer.com/content/pdf/10.1007/s11222-022-10102-5.pdf

publisherhttps://link.springer.com/article/10.1007/s11222-022-10102-5/fulltext.html

journalhttps://doi.org/10.1007/s11222-022-10102-5

Fields of Study

Statistical Methods and InferenceStatistical Methods and Bayesian InferenceAdvanced Statistical Methods and Models02 engineering and technology01 natural sciences0202 electrical engineering, electronic engineering, information engineering0101 mathematics

Keywords

Bayesian linear regressionPrior probabilityBayesian probabilityPosterior probabilityComputer scienceCovarianceMathematicsMarginal likelihoodBayesian inferenceArtificial intelligenceStatisticsPattern recognition (psychology)High-dimensional DataPosterior ConsistencyGeneralized LikelihoodSparse Multivariate Regression And Covariance SelectionFOS: Computer and information sciencesEstimation in multivariate analysisStatistics - ComputationApplications of statistics to biology and medical sciences; meta analysisMethodology (stat.ME)Computational methods for problems pertaining to statisticsAsymptotic properties of parametric estimatorsStatistics - MethodologyComputation (stat.CO)

Sustainable Development Goals

SDG 0: Reduced inequalities