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A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms

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  • Tao Wang
  • Hongyu Zhao

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  • Tao Wang & Hongyu Zhao, 2017. "A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms," Biometrics, The International Biometric Society, vol. 73(3), pages 792-801, September.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:3:p:792-801
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    File URL: http://hdl.handle.net/10.1111/biom.12654
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    References listed on IDEAS

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    1. Fan Xia & Jun Chen & Wing Kam Fung & Hongzhe Li, 2013. "A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis," Biometrics, The International Biometric Society, vol. 69(4), pages 1053-1063, December.
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    Cited by:

    1. Yaru Song & Hongyu Zhao & Tao Wang, 2020. "An adaptive independence test for microbiome community data," Biometrics, The International Biometric Society, vol. 76(2), pages 414-426, June.
    2. Matthew D. Koslovsky, 2023. "A Bayesian zero‐inflated Dirichlet‐multinomial regression model for multivariate compositional count data," Biometrics, The International Biometric Society, vol. 79(4), pages 3239-3251, December.
    3. Patrick LeBlanc & Li Ma, 2023. "Microbiome subcommunity learning with logistic‐tree normal latent Dirichlet allocation," Biometrics, The International Biometric Society, vol. 79(3), pages 2321-2332, September.
    4. Zahra Rezaei Ghahroodi, 2023. "Statistical matching of sample survey data: application to integrate Iranian time use and labour force surveys," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 1023-1051, September.
    5. Tu, Wangshu & Browne, Ryan & Subedi, Sanjeena, 2024. "A mixture of logistic skew-normal multinomial models," Computational Statistics & Data Analysis, Elsevier, vol. 196(C).

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