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David Hunter

Professor of Statistics
Dave Hunter


David R. Hunter is a Professor of Statistics at Penn State.

He earned his Ph.D. in statistics from the University of Michigan in 1999, following a math degree from Princeton university in 1992 and two years teaching mathematics at a public high school in New Hampshire. He has been at Penn State University since 1999, where he is professor of statistics and served as head of the Department of Statistics from 2012 to 2018.

Hunter has published widely on statistical models for networks and is a co-creator of the "statnet" suite of packages for network analysis in R. He co-coined the term "MM algorithms" and has written extensively on this and other EM-like algorithms. He has also extended the theory and computational practice of unsupervised clustering using nonparametric finite mixture models.


Honors and Awards

  • Fellow of American Statistical Association
  • Elected Member of International Statistical Institute
  • Co-recipient of Richards Software Award from International Network for Social Networks Analysis
  • Faculty Fellow of Penn State’s Teaching and Learning with Technology
  • Research Fellow of Le Studium, CNRS, France



  • Hunter DR and Lange K (2004), A Tutorial on MM Algorithms, The American Statistician, 58: 30-37.
  • Hunter DR (2004), MM algorithms for generalized Bradley-Terry models, Annals of Statistics, 32: 386-408.
  • Hunter DR and Li R (2005), Variable selection using MM algorithms, Annals of Statistics, 33: 1617-1642.
  • Hunter DR and Handcock MS (2006), Inference in curved exponential family models for networks, Journal of Computational and Graphical Statistics, 15: 565-583.
  • Hunter DR, Wang S, and Hettmansperger TP (2007), Inference for mixtures of symmetric distributions, Annals of Statistics, 35: 224-251.
  • Handcock MS, Hunter DR, Butts CT, Goodreau SM, and Morris M (2008), statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data, Journal of Statistical Software, 24(1).
  • Hunter DR, Goodreau SM, and Handcock MS (2008), Goodness of fit of social network models, Journal of the American Statistical Association, 103: 248-258.
  • Levine M, Hunter DR, Chauveau D (2011), Maximum Smoothed Likelihood for Multivariate Mixtures, Biometrika, 98 (2): 403-416.
  • Vu DQ, Hunter DR, and Schweinberger M (2013), Model-Based Clustering of Large Networks, Annals of Applied Statistics, 7(2): 1010-1039.
  • Hunter DR, Bao L, and Poss M (2017), Assignment of Endogenous Retrovirus Integration Sites Using a Mixture Model, Annals of Applied Statistics, 11 (2): 751-770.



DS 200 - Introduction to Data Sciences

FRNSC 597A - Applied Forensic Science Statistics

PSU 016 - Statistics first-year seminar

SC 205N - Identifying Bias and Falsehood (co-taught with Paula Droege)

STAT 100 - Statistical concepts and reasoning

STAT 200 - Elementary Statistics

STAT 200H - Honors Elementary Statistics

STAT 220 - Basic Statistics for Quantitative Students

STAT 250 - Introduction to Biostatistics

STAT 250H - Honors Biostatistics

STAT 401 - Experimental Methods

STAT 414 - Introduction to Probability Theory

STAT 460 - Intermediate Applied Statistics

STAT 470 - Problem Solving and Communication in Applied Statistics

STAT 514 - Theory of Statistics II

STAT 515 - Stochastic Processes I

STAT 525 - Survival Analysis I

STAT 553 - Asymptotic Tools

STAT 590 - Colloquium and Perspectives on Statistics

STAT 597D - Statistical genetics and bioinformatics (co-taught with Francesca Chiaromonte)