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My bibliography Save this articleA machine learning-based approach for estimating and testing associations with multivariate outcomes
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DOI: 10.1515/ijb-2019-0061
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- van der Laan Mark J. & Dudoit Sandrine & Keles Sunduz, 2004. "Asymptotic Optimality of Likelihood-Based Cross-Validation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-25, March.
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Keywords
canonical correlation; epidemiology; machine learning; multivariate outcomes; variable importance;All these keywords.
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