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Computer Science > Computers and Society

arXiv:2209.05461 (cs)
[Submitted on 2 Sep 2022 (v1), last revised 20 Dec 2022 (this version, v3)]

Title:EPA Particulate Matter Data -- Analyses using Local Control Strategy

Authors:Robert L. Obenchain, S. Stanley Young
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Abstract:Statistical Learning methodology for analysis of large collections of cross-sectional observational data can be most effective when the approach used is both Nonparametric and Unsupervised. We illustrate use of our NU Learning approach on 2016 US environmental epidemiology data that we have made freely available. We encourage other researchers to download these data, apply whatever methodology they wish, and contribute to development of a broad-based ``consensus view'' of potential effects of Secondary Organic Aerosols (volatile organic compounds of predominantly biogenic or anthropogenic origin) within PM2.5 particulate matter on circulatory and/or respiratory mortality. Our analyses here focus on the question: ``Are regions with relatively high air-borne biogenic particulate matter also expected to have relatively high circulatory and/or respiratory mortality?''
Comments: 30 pages, 22 figures, 6 tables
Subjects: Computers and Society (cs.CY); Applications (stat.AP)
MSC classes: 62K10, 62H20, 62G09, 62G30, 62-04, 62P12
Report number: ISSN 2380-7539
Cite as: arXiv:2209.05461 [cs.CY]
  (or arXiv:2209.05461v3 [cs.CY] for this version)
  https://doi.org/10.48550/arXiv.2209.05461
Journal reference: North Carolina Journal of Mathematics and Statistics, Vol. 9, pp. 1-24, 01/02/2023

Submission history

From: Robert L Obenchain PhD [view email]
[v1] Fri, 2 Sep 2022 02:12:37 UTC (2,509 KB)
[v2] Sat, 12 Nov 2022 22:41:03 UTC (463 KB)
[v3] Tue, 20 Dec 2022 03:31:46 UTC (2,537 KB)
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