A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide

A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide

Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary linear regression. However, different algorithms have rarely been compared in terms of their predictiv...

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Bibliographic Details
Main Authors: Jie Chen, Kees de Hoogh, John Gulliver, Barbara Hoffmann, Ole Hertel, Matthias Ketzel, Mariska Bauwelinck, Aaron van Donkelaar, Ulla A. Hvidtfeldt, Klea Katsouyanni, Nicole A.H. Janssen, Randall V. Martin, Evangelia Samoli, Per E. Schwartz, Massimo Stafoggia, Tom Bellander, Maciek Strak, Kathrin Wolf, Danielle Vienneau, Roel Vermeulen, Bert Brunekreef, Gerard Hoek
Format: Article
Language:English
Published: Elsevier 2019-09-01
Series:Environment International
Online Access:http://www.sciencedirect.com/science/article/pii/S0160412019304404

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http://www.sciencedirect.com/science/article/pii/S0160412019304404

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