David Hunter Professor, Penn State Department of Statistics

I received my Ph.D. in 1999 from the Department of Statistics at the University of Michigan in Ann Arbor, Michigan.

Teaching

In the SP 2022 semester, I am teaching DS 200, Introduction to Data Sciences and STAT 590, Colloquium and Perspectives on Statistics.

In past semesters, I've taught or co-taught:

• DS 200, Introduction to Data Sciences (spring 2020)
• FRNSC 597A, Applied Forensic Science Statistics(fall 2014)
• PSU 016, Statistics first-year seminar (fall 2011 and fall 2010 and fall 2009 and fall 2008 and fall 2006 and fall 2005)
• SC 205N, BS: Identifying Bias and Falsehood (fall 2021 and fall 2018, co-taught with Paula Droege)
• STAT 100, Statistical concepts and reasoning (fall 2015 and fall 2013 and fall 2012 and fall 2008 and spring 2004 and spring 2005 and fall 2005)
• STAT 200, Elementary Statistics (fall 2021 and fall 2016)
• STAT 200H, Honors Elementary Statistics (fall 2002 and fall 1999)
• STAT 220, Basic Statistics for Quantitative Students (spring 2006)
• STAT 250, Introduction to Biostatistics (fall 2017)
• STAT 250H, Honors Biostatistics (spring 2003 and spring 2002)
• STAT 401, Experimental Methods (spring 2004 and spring 2000)
• STAT 414, Introduction to Probability Theory (fall 2010 and fall 2009 and spring 2009)
• STAT 460, Intermediate Applied Statistics (fall 2001 and spring 2000)
• STAT 470, Problem Solving and Communication in Applied Statistics (spring 2020 and fall 2018)
• STAT 514, Theory of Statistics II (fall 2014)
• STAT 515, Stochastic Processes I (spring 2012)
• STAT 525, Survival Analysis I (spring 2011)
• STAT 553, Asymptotic Tools (spring 2020 and fall 2011 and fall 2006 and fall 2004 and fall 2003 and fall 2002 and fall 2001 and fall 2000) Course lecture notes
• STAT 590, Colloquium and Perspectives on Statistics (spring 2017)
• STAT 597D, Statistical genetics and bioinformatics (spring 2001, co-taught with Francesca Chiaromonte)

Papers

Papers about networks

• Krivitsky PN, Hunter DR, Morris M, Klumb C (2022), ergm 4: New features, ArXiv Preprint, arXiv:2106.04997.
• Krivitsky PN, Hunter DR, Morris M, Klumb C (2022), ergm 4: Computational Improvements, ArXiv Preprint, arXiv:2203.08198.
• Krivitsky PN, Kuvelkar AR, Hunter DR (2022), Likelihood-based Inference for Exponential-Family Random Graph Models via Linear Programming, ArXiv Preprint, arXiv:2202.03572.
• Schmid C, Hunter DR (2020), Improving ERGM Starting Values Using Simulated Annealing, ArXiv Preprint, arXiv:2009.01202.
• Lee KH, Xue L, and Hunter DR (2020), Model-based clustering of time-evolving networks through temporal exponential-family random graph models, Journal of Multivariate Analysis, 175 (104540): 1-17.
• Carnegie NB, Krivitsky PN, Hunter DR, Goodreau SM (2015), An approximation method for improving dynamic network model fitting, Journal of Computational and Graphical Statistics, 24 (2): 502-519.
• Vu DQ, Hunter DR, and Schweinberger M (2013), Model-Based Clustering of Large Networks, Annals of Applied Statistics, 7 (2): 1010-1039. (code)
• Salathe M, Vu DQ, Khandelwal S, Hunter DR (2013), The Dynamics of Health Behavior Sentiments on a Large Online Social Network, EPJ Data Science, 2 (1): 1-12.
• Hunter DR, Goodreau SM, Handcock MS (2013), ergm.userterms: A Template Package for Extending statnet, Journal of Statistical Software, 52 (2): 1-25.
• Hunter DR, Krivitsky PN, and Schweinberger M (2012), Computational Statistical Methods for Social Network Analysis, Journal of Computational and Graphical Statistics, 21 (4): 856-882.
• Hummel RM, Hunter DR, Handcock MS (2012), Improving Simulation-Based Algorithms for Fitting ERGMs, Journal of Computational and Graphical Statistics, 21 (4): 920-939. Here is the supplemental code.
• Groendyke C, Welch D, Hunter DR (2012), A Network-Based Analysis of the 1861 Hagelloch Measles Data, Biometrics, 68 (3): 755-765.
• Vu DQ, Asuncion AU, Hunter DR, Smyth P (2011), Continuous-Time Regression Models for Longitudinal Networks, Advances in Neural Information Processing Systems 24 (NIPS 2011), 2492-2500.
• Groendyke C, Welch D, Hunter DR (2011), Bayesian Inference for Contact Networks Given Epidemic Data, Scandanavian Journal of Statistics, 38 (3): 600-616.
• Vu DQ, Asuncion AU, Hunter DR, Smyth P (2011), Dynamic Egocentric Models for Citation Networks, Proceedings of the 28th International Conference on Machine Learning (ICML 2011), 857-864. (code)
• Welch D, Bansal S, Hunter DR (2011), Statistical inference to advance network models in epidemiology, Epidemics, 3 (1): 38-45.
• Hunter DR, Goodreau SM, and Handcock MS (2008), Goodness of fit of social network models, Journal of the American Statistical Association, 103 (481): 248-258.
• 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): 1-11. URL http://www.jstatsoft.org/v24/i01
• Hunter DR, Handcock MS, Butts CT, Goodreau SM, and Morris M (2008), ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks, Journal of Statistical Software, 24 (3): 1-29. URL http://www.jstatsoft.org/v24/i03
• Morris M, Handcock MS, and Hunter DR (2008), Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects, Journal of Statistical Software, 24 (4): 1-24. URL http://www.jstatsoft.org/v24/i04
• Goodreau SM, Handcock MS, Hunter DR, Butts CT, and Morris M (2008), A statnet Tutorial, Journal of Statistical Software, 24 (9): 1-26. URL http://www.jstatsoft.org/v24/i09
• Hunter DR (2007), Comment on "Model-Based Clustering for Social Networks" by Handcock MS, Raftery AE, and Tantrum JM, Journal of the Royal Statistical Society, Series A, 170 (2): 339-340.
• Hunter DR (2007), Curved exponential family models for social networks, Social Networks, 29 (2): 216-230.
• Hunter DR and Handcock MS (2006), Inference in curved exponential family models for networks, Journal of Computational and Graphical Statistics, 15 (3): 565-583.

Papers about mixture models

• Ranalli M, Lindsay BG, and Hunter DR (2020), A Classical Invariance Approach to the Normal Mixture Problem, Statistica Sinica, 30 (3): 1354-1254.
• Lee KH, Xue L, and Hunter DR (2020), Model-based clustering of time-evolving networks through temporal exponential-family random graph models, Journal of Multivariate Analysis, 175 (104540): 1-17.
• Zhu X and Hunter DR (2019), Clustering Via Finite Nonparametric ICA Mixture Models, Advances in Data Analysis and Classification, 13 (1): 65-87.
• 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.
• Zhu X and Hunter DR (2016), Theoretical Grounding for Estimation in Conditional Independence Multivariate Finite Mixture Models, Journal of Nonparametric Statistics, 28 (4): 683-701.
• Young DS and Hunter DR (2015), Random Effects Regression Mixtures for Analyzing Infant Habituation, Journal of Applied Statistics, 42 (7): 1421-1441.
• Chauveau D, Hunter DR, Levine M (2015), Semi-Parametric Estimation for Conditional Independence Multivariate Finite Mixture Models, Statistics Surveys, 9: 1-31.
• Vu DQ, Hunter DR, and Schweinberger M (2013), Model-Based Clustering of Large Networks, Annals of Applied Statistics, 7 (2): 1010-1039. (code)
• Hunter DR and Young DS (2012), Semiparametric Mixtures of Regressions, Journal of Nonparametric Statistics, 24 (1): 19-38.
• Levine M, Hunter DR, Chauveau D (2011), Maximum Smoothed Likelihood for Multivariate Mixtures, Biometrika, 98 (2): 403-416.
• Benaglia T, Chauveau D, Hunter DR (2011), Bandwidth Selection in an EM-like Algorithm for Nonparametric Multivariate Mixtures, in DR Hunter, DSP Richards, and JL Rosenberger (eds.), Nonparametric Statistics and Mixture Models: A Festschrift in Honor of Thomas P. Hettmansperger, (World Scientific, Singapore), pp. 15-27.
• Young DS, Hunter DR (2010), Mixtures of Regressions with Predictor-Dependent Mixing Proportions, Computational Statistics and Data Analysis, 54 (10): 2253-2266.
• Benaglia T, Chauveau D, Hunter DR, and Young DS (2009), mixtools: An R Package for Analyzing Finite Mixture Models, Journal of Statistical Software, 32 (6): 1-29. URL http://www.jstatsoft.org/v32/i06.
• Benaglia T, Chauveau D, and Hunter DR (2009), An EM-like algorithm for semi- and non-parametric estimation in multivariate mixtures, Journal of Computational and Graphical Statistics, 18 (2): 505-526.
• Hunter DR, Wang S, and Hettmansperger TP (2007), Inference for mixtures of symmetric distributions, Annals of Statistics, 35 (1): 224-251. (code)

Papers about or involving MM algorithms

• Hunter DR, Kuruppumullage Don P, and Lindsay BG (2019), An Expansive View of EM Algorithms, in Handbook of Mixture Analysis, S Fruehwirth-Schnatter, G Celeux, and CP Robert, editors.
• Hunter DR (2014), Invited discussion of "A Brief History of Modern Optimization for Statisticians" by Lange, Chi, and Zhou, International Statistical Review, 82 (1): 76-79.
• Chauveau D and Hunter DR (2013), ECM and MM algorithms for normal mixtures with constrained parameters, hal-00625285, version 2.
• Vu DQ, Hunter DR, and Schweinberger M (2013), Model-Based Clustering of Large Networks, Annals of Applied Statistics, 7 (2): 1010-1039. (code)
• Levine M, Hunter DR, Chauveau D (2011), Maximum Smoothed Likelihood for Multivariate Mixtures, Biometrika, 98 (2): 403-416.
• Hunter DR and Li R (2005), Variable selection using MM algorithms, Annals of Statistics, 33 (4): 1617-1642.
• Hunter DR (2004), MM algorithms for generalized Bradley-Terry models, Annals of Statistics, 32 (1): 386-408. (code)
• Hunter DR and Lange K (2004), A Tutorial on MM Algorithms, The American Statistician, 58 (1): 30-37.
• Hunter DR (2003), On the geometry of EM algorithms, Penn State Department of Statistics Technical Report 0303.
• Hunter DR and Lange K (2002), Computing estimates in the proportional odds model, Annals of the Institute of Statistical Mathematics, 54 (1): 155-168. (code)
• Lange K, Hunter DR, Yang I (2000), Optimization transfer using surrogate objective functions, Journal of Computational and Graphical Statistics, 9 (1): 1-20.
• Hunter DR and Lange K (2000), Rejoinder to discussion of "Optimization transfer using surrogate objective functions", Journal of Computational and Graphical Statistics, 9 (1): 52-59.
• Hunter DR and Lange K (2000), Quantile regression via an MM algorithm, Journal of Computational and Graphical Statistics, 9 (1): 60-77. (code)

Other papers

• Malhotra DE, Bao L, Hunter DR, Poss M, and Acharya R (2016), A Pipeline for Identifying Integration Sites of Mobile Elements in the Genome Using Next-Generation Sequencing, Proceedings of the 8th International Conference on Bioinformatics and Computational Biology (BICOB 2016), 63-68.
• Bao L, Elleder D, Malhotra R, DeGiorgio M, Maravegias T, Horvath L, Carrel L, Gillin C, Hron T, Fabryova H, Hunter DR, and Poss M (2014), Computational and statistical analyses of insertional polymorphic endogenous retroviruses in a non-model organism, Computation, 2 (4), 221-245.
• Hunter DR (2005), Teaching computing in statistical theory courses, The American Statistician, 59: 327-333.
• Shandera S, Matsick, JL, Hunter DR, and Leblond L (2021), RASE: Modeling Cumulative Disadvantage Due to Marginalized Group Status in Academia, PLOS One, 16 (12), e0260567.

Miscellaneous links

• I'm part of a team that created the statnet suite of packages for R.
• Here is the most up-to-date version of the lecture notes I created for STAT 553, Asymptotic Tools.
• Links to R tutorials I have used during weeklong astrostatistics summer school workshops at Penn State (and once in Kavalur, India) are available here for these years: 2005   |   2006   |   2007   |   2010   |   2011
• During the 2007-8 academic year, I was on sabbatical at the University of Orléans, France, partially supported by le Studium, an agency of the CNRS (basically the French version of NSF) that supports researchers visiting the Centre region of France from abroad. We kept a blog of the year.