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A framework for Bayesian nonparametric inference for causal effects of mediation.
Kim, Chanmin; Daniels, Michael J; Marcus, Bess H; Roy, Jason A.
  • Kim C; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, U.S.A.
  • Daniels MJ; Department of Statistics and Data Sciences and Department of Integrative Biology, University of Texas at Austin, Austin, Texas 78712, U.S.A.
  • Marcus BH; Department of Family Medicine and Public Health, University of California, San Diego, California 92093, U.S.A.
  • Roy JA; Department of Biostatistics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, U.S.A.
Biometrics ; 73(2): 401-409, 2017 06.
Article en En | MEDLINE | ID: mdl-27479682
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Teorema de Bayes Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2017 Tipo del documento: Article
Texto completo
Imprimir
XML
PubMed Links
Search on Google

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Teorema de Bayes Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2017 Tipo del documento: Article