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Increasing the replicability for linear models via adaptive significance levels

Test(2022)10.1007/s11749-022-00803-4Source: DataRank Database

Increasing the replicability for linear models via adaptive significance levels is a research paper published in Test (2022). On theSindex it has a DataRank of 0. It has been cited 2 times.

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2 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (3 grants)OA: closedTopics (14)IDs (PubMed)

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Authors (3)

María-Eglée Pérez ORCID,Luis R. Pericchi ORCID,Daiver Vélez Ramos ORCID

Live enrichment

PubMed:30945415

National Institutes of Health

Grant: 5R25MD010399-02

Increasing Diversity in Interdisciplinary BD2K (IDI-BD2K)

National Institutes of Health

Grant: 5U54CA096300-02

PRCC/MDACC: Partners for Excellence in Cancer Research

National Institutes of Health

Grant: 5P20GM103475-16

Advancing Competitive Biomedical Research in Puerto Rico

closedLicense: Springer TDM

https://doi.org/10.1007/s11749-022-00803-4

http://arxiv.org/pdf/2003.06938

https://dx.doi.org/10.48550/arxiv.2003.06938

http://arxiv.org/abs/2003.06938

https://zbmath.org/7601203

Fields of Study

01 natural sciences0101 mathematics

Keywords

FOS: Computer and information scienceslikelihood ratioPBICGeneral considerations in statistical decision theory\(p\)-value calibrationBayes factoradaptive \(\alpha\)linear modelMethodology (stat.ME)Diagnostics, and linear inference and regressionBayesian problems; characterization of Bayes proceduresStatistics - Methodology

Increasing the replicability for linear models via adaptive significance levels

FAIR Checklist

Authors (3)

Funding (3 grants)

Open Access

Topics