Scholars@Duke

• Background
• 

  Education, Training, & Certifications

  • Ph.D., University of Wisconsin - Madison 2000
  • M.S., University of Wisconsin - Madison 1994
  • B.S., Indiana University at Bloomington 1993
• 

  Previous Appointments & Affiliations

  • Director of Graduate Studies in Statistical Science, Statistical Science, Trinity College of Arts & Sciences 2018 - 2021
• Recognition
• 

  In the News

  • MAR 22, 2021

    Machine Learning Meets the Maestros

  • OCT 2, 2016

    Peter Hoff: Reckoning With Big Data to Tease Out Network Patterns

  • SEP 30, 2016

    Peter Hoff: Reckoning with Big Data
• Publications & Artistic Works
• 

  Selected Publications

  • Academic Articles

    • He, Y., and P. D. Hoff. “Multiplicative coevolution regression models for longitudinal networks and nodal attributes.” Social Networks 57 (May 1, 2019): 54–62. https://doi.org/10.1016/j.socnet.2018.12.002.
       Full Text
    • Hoff, P. D. “Limitations on detecting row covariance in the presence of column covariance.” Journal of Multivariate Analysis 152 (December 1, 2016): 249–58. https://doi.org/10.1016/j.jmva.2016.09.003.
       Full Text
    • Minhas, S., P. D. Hoff, and M. D. Ward. “A new approach to analyzing coevolving longitudinal networks in international relations.” Journal of Peace Research 53, no. 3 (May 1, 2016): 491–505. https://doi.org/10.1177/0022343316630783.
       Full Text
    • Kessler, David C., Peter D. Hoff, and David B. Dunson. “Marginally specified priors for non-parametric Bayesian estimation.” Journal of the Royal Statistical Society. Series B, Statistical Methodology 77, no. 1 (January 2015): 35–58. https://doi.org/10.1111/rssb.12059.
       Full Text
    • Volfovsky, Alexander, and Peter D. Hoff. “HIERARCHICAL ARRAY PRIORS FOR ANOVA DECOMPOSITIONS OF CROSS-CLASSIFIED DATA.” The Annals of Applied Statistics 8, no. 1 (March 2014): 19–47. https://doi.org/10.1214/13-aoas685.
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• Teaching & Mentoring
• 

  Recent Courses

  • MATH 743L: Linear Models 2021
  • STA 693: Research Independent Study 2021
  • STA 721L: Linear Models 2021
  • STA 532: Theory of Statistical Inference 2020
  • STA 693: Research Independent Study 2020
  • STA 701S: Readings in Statistical Science 2020
  • STA 832: Multivariate Statistical Analysis 2020

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