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Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide

The Annals of Applied Statistics(2021)10.1214/20-aoas1422Source: DataRank Database

Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide is a research paper published in The Annals of Applied Statistics (2021). On theSindex it has a DataRank of 0.

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (2 grants)Impact (FWCI 0.00)OA: bronzeTopics (25)IDs (PubMed, PMC, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

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Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (2)

Matthias Katzfuß ORCID,Kyle P. Messier ORCID

Live enrichment

PubMed:35069963 PMC:PMC8774268 OpenAlex:https://openalex.org/W3026390070

Intramural NIH HHS

Grant: Z99 ES999999

NIEHS NIH HHS

Grant: K99 ES029523

FWCI

0.00

Citation Percentile

0.0%

bronze

journalhttps://projecteuclid.org/journals/annals-of-applied-statistics/volume-15/issue-2/Scalable-penalized-spatiotemporal-land-use-regression-for-ground-level-nitrogen/10.1214/20-AOAS1422.pdf

publisherhttps://projecteuclid.org/journals/annals-of-applied-statistics/volume-15/issue-2/Scalable-penalized-spatiotemporal-land-use-regression-for-ground-level-nitrogen/10.1214/20-AOAS1422.pdf

journalhttps://doi.org/10.1214/20-aoas1422

repositoryhttps://pubmed.ncbi.nlm.nih.gov/35069963

repositoryhttp://arxiv.org/abs/2005.09210

Fields of Study

Air Quality and Health ImpactsUrban Transport and AccessibilityClimate Change and Health Impacts

Keywords

CovariateEnvironmental scienceEconometricsAir pollutionStatisticsScale (ratio)Regression analysisRegressionLinear regressionKrigingRange (aeronautics)CalibrationComputer scienceMathematicsGeographyEcologyCartographyEngineeringSpatial statisticsVariable selectionGaussian ProcessGeneral Vecchia Approximation

Sustainable Development Goals

SDG 0: Sustainable cities and communities