The North Carolina Journal of Mathematics and Statistics

EPA Particulate Matter Data - Analyses using Local Control Strategy

Robert Lincoln Obenchain, Sidney Stanley Young


Statistical Learning methodology for analysis of large collections of observational, cross sectional data can be most effective
when the approach used is both Non-parametric and Unsupervised. We illustrate this using "LocalControl Strategy" on 2016 US environmental epidemiology data that we have contributed to Dryad. We invite researchers to download our CSV file, 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 that have predominantly Biogenic or Anthropogenic origin) within PM2.5 particulate matter on Circulatory and/or Respiratory mortality. Our analyses here focus on the question: "Can life in a region with relatively high air-borne Biogenic particulate matter also be relatively dangerous in terms of Circulatory and/or Respiratory Mortality?"


Nonparametric Unsupervised Learning; Local Control Strategy; Clustering as Matching; Permutation Distributions; Random Forests and Partial Dependence Plots.

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