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Reducing the complexity of high-dimensional environmental data: An analytical framework using LASSO with considerations of confounding for statistical inference.
Frndak, Seth; Yu, Guan; Oulhote, Youssef; Queirolo, Elena I; Barg, Gabriel; Vahter, Marie; Mañay, Nelly; Peregalli, Fabiana; Olson, James R; Ahmed, Zia; Kordas, Katarzyna.
Afiliação
  • Frndak S; Department of Epidemiology and Environmental Health: University at Buffalo, The State University of New York, USA. Electronic address: sethfrnd@buffalo.edu.
  • Yu G; Department of Biostatistics: University of Pittsburgh, USA.
  • Oulhote Y; Department of Epidemiology, University of Massachusetts Amherst, USA.
  • Queirolo EI; Department of Neuroscience and Learning, Catholic University of Uruguay, Montevideo, Uruguay.
  • Barg G; Department of Neuroscience and Learning, Catholic University of Uruguay, Montevideo, Uruguay.
  • Vahter M; Department of Environmental Medicine: Karolinska Institute, Sweden.
  • Mañay N; Faculty of Chemistry, University of the Republic of Uruguay (UDELAR), Montevideo, Uruguay.
  • Peregalli F; Department of Neuroscience and Learning, Catholic University of Uruguay, Montevideo, Uruguay.
  • Olson JR; Department of Epidemiology and Environmental Health: University at Buffalo, The State University of New York, USA.
  • Ahmed Z; Research and Education in eNergy, Environment and Water (RENEW) Institute University at Buffalo, The State University of New York, USA.
  • Kordas K; Department of Epidemiology and Environmental Health: University at Buffalo, The State University of New York, USA.
Int J Hyg Environ Health ; 249: 114116, 2023 04.
Article em En | MEDLINE | ID: mdl-36805184
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Modelos Estatísticos Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Child / Humans Idioma: En Revista: Int J Hyg Environ Health Assunto da revista: SAUDE AMBIENTAL / SAUDE PUBLICA Ano de publicação: 2023 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Modelos Estatísticos Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Child / Humans Idioma: En Revista: Int J Hyg Environ Health Assunto da revista: SAUDE AMBIENTAL / SAUDE PUBLICA Ano de publicação: 2023 Tipo de documento: Article