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ESTIMATION AND INFERENCE IN METABOLOMICS WITH NON-RANDOM MISSING DATA AND LATENT FACTORS
High throughput metabolomics data are fraught with both non-ignorable missing observations and unobserved factors that influence a metabolite’s measured concentration, and it is well known that ignoring either of these complications can compromise estimators. However, current methods to analyze thes...
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| Publicado no: | Ann Appl Stat |
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| Main Authors: | , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8248477/ https://ncbi.nlm.nih.gov/pubmed/34221212 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1214/20-aoas1328 |
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