Amy Herring
Sara & Charles Ayres Professor
Statistical Science, Global Health, and Biostatistics & Bioinformatics
Appointment:
Topics:
Amy Herring
Sara & Charles Ayres Professor
Statistical Science, Global Health, and Biostatistics & Bioinformatics
Amy H. Herring is Professor of Statistical Science and Research Professor of Global Health at Duke University. Dr. Herring received her doctorate in biostatistics at Harvard University and came to Duke from UNC-Chapel Hill, where she was distinguished professor of biostatistics. Her research interests include development of statistical methodology for longitudinal or clustered data, Bayesian methods, latent class and latent variable models, missing data, complex environmental mixtures, and applications of statistics in population health and medicine. She has received numerous awards for her work, including the Mortimer Spiegelman Award from the American Public Health Association as the best applied public health statistician under age 40. Her research program is funded by NIH, and she holds leadership positions at the national and international level, including as Past Chair of the American Statistical Association's Biometrics Section and as Executive Secretary of the International Society for Bayesian Analysis. She sits on several national committees, including the National Academy of Science Committee on Applied and Theoretical Statistics and the Research Committee of the Health Effects Institute.
Courses
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GLHLTH 758
Case Studies in Data Science
Publications
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Stephenson BJK, Sotres-Alvarez D, Siega-Riz A-M, Mossavar-Rahmani Y, Daviglus ML, Van Horn L, et al. Empirically Derived Dietary Patterns Using Robust Profile Clustering in the Hispanic Community Health Study/Study of Latinos. The Journal of Nutrition. 2020 Oct;150(10):2825–34.
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Tang B, Herring AH. Bayesian statistical methods, Brian J.ReichSujit K.Ghosh, Boca Raton, FL: Chapman and Hall/CRC, 2019, Hard cover. pp. 288. US$ 79.96. Biometrics. 2020 Jun;76(2):671–2.
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Stephenson BJK, Herring AH, Olshan A. Robust Clustering with Subpopulation-specific Deviations. Journal of the American Statistical Association. 2020 Jan;115(530):521–37.
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Kunihama T, Halpern CT, Herring AH. Non-parametric Bayes models for mixed scale longitudinal surveys. Journal of the Royal Statistical Society Series C: Applied Statistics. 2019 Aug 1;68(4):1091–109.
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See more publications at Scholars@Duke