Research Support

• Principal Investigator, 2018-2021 NSF regular grant, DMS-1811936, Collaborative Research: New Developments in Direct Probabilistic Inference on Interest Parameters
  

• Principle Investigator, 2018, NSF conference grant, DMS-1812088, Third Workshop on Higher-Order Asymptotics and Post-Selection Inference
  

• Principal Investigator, 2017-2020, NSF regular grant, DMS-1712940, Collaborative Research: Higher-Order Asymptotics and Accurate Inference for Post-Selection
  

• Principle Investigator, 2016, NSF conference grant, DMS-1623028, Workshop on Higher-Order Asymptotics and Post-Selection Inference
  

Papers submitted or under revision

• Block bootstrap optimality for density estimation with dependent data, with S.M.S. Lee and G.A. Young. Submitted. [arXiv]

• Bayes factor consistency. Revision requested by Statistical Science.

• On model selection criteria for climate change impact studies, with X. Cui and D. Ghanem.  Submitted. [arXiv]
  

Papers published or accepted

1. T.J. DiCiccio, T.A. Kuffner, and G.A. Young (2012). Objective Bayes, conditional inference and the signed root likelihood ratio statistic. Biometrika. [doi]  [preprint]
2. T.J. DiCiccio, T.A. Kuffner, G.A. Young, and R. Zaretzki (2015). Stability and uniqueness of p-values for likelihood-based inference. Statistica Sinica. [doi]  [preprint]
3. T.J. DiCiccio, T.A. Kuffner, and G.A. Young (2015). Quantifying nuisance parameter effects via decompositions of asymptotic refinements for likelihood-based statistics. Journal of Statistical Planning and Inference. [doi]  [preprint]
4. T.J. DiCiccio, T.A. Kuffner, and G.A. Young (2017). A simple analysis of the exact probability matching prior in the location-scale model. The American Statistician. [doi]  [preprint]
5. T.A. Kuffner and S.G. Walker (2019). Why are p-values controversial? The American Statistician. [doi]  [preprint]
6. T.A. Kuffner, S.M.S. Lee, and G.A. Young (2018). Consistency of a hybrid block bootstrap for distribution and variance estimation for sample quantiles of weakly dependent sequences. Australian & New Zealand Journal of Statistics. Special Issue in Honour of Peter Gavin Hall. [doi] [preprint]
7. T.J. DiCiccio, T.A. Kuffner, and G.A. Young (2017). The formal relationship between analytic and bootstrap approaches to parametric inference. Journal of Statistical Planning and Inference. [doi] [preprint]
8. L. Hong, T.A. Kuffner, and R.G. Martin (2018). On overfitting and post-selection uncertainty assessments. Biometrika. [doi] [preprint]
9. T.A. Kuffner and G.A. Young (2018). Principled statistical inference in data science. In Proceedings of the Statistical Data Science Conference. N. Adams, E. Cohen and Y.K. Guo, editors. World Scientific. [doi] [preprint]
10. L. Hong, T.A. Kuffner and R.G. Martin (2019). On prediction of future insurance claims when the model is uncertain, with L. Hong and R. Martin. Variance.  [journal link] [ssrn]
11. J.E. Kolassa and T.A. Kuffner (2020). On the validity of the formal Edgeworth expansion for posterior densities. Annals of Statistics. [doi] [pdf]
12. D. Ghanem and T.A. Kuffner (2019). Discussion of ``Models as Approximations, Parts I & II" by Andreas Buja and coauthors. Statistical Science, accepted.
13. T.A. Kuffner, S.M.S. Lee and G.A. Young (2020+). Block bootstrap optimality and empirical block selection for sample quantiles with dependent data. Biometrika, accepted. [pdf]
14. Q. Wang, J.E. Figueroa-Lopez and T.A. Kuffner (2020+). Bayesian inference on volatility in the presence of infinite jump activity and microstructure noise. Electronic Journal of Statistics, accepted. [arXiv]

Upcoming Talks

• Awaiting a vaccine :-)

In the Past

• Department Colloquium.
  Harvard University, Department of Statistics
  October 21, 2019.

• Joint Statistical Meetings
  Invited Session (Organizer: Rob Tibshirani).
  Denver, Colorado, July 27 - Aug. 1, 2019.

• Department Colloquium.
  Indiana University, April 15, 2019.

• Workshop in Honor of Lawrence D. Brown
  Invited Speaker.
  University of Pennsylvania, Nov. 30 - Dec. 1, 2018.

• Workshop on Statistical Inference, Learning and Models for Data Science.
  Invited Speaker (Organizer: Nancy Reid).
  Fields Institute, Toronto, Ontario, September 24-26, 2018.

• The Conference on Statistical Learning and Data Science / Nonparametric Statistics.
  Invited Session (Organizer: Ryan Tibshirani).
  Columbia University, June 4-6, 2018.

• Symposium on Data Science & Statistics.
  Invited Session (Organizer: Larry Wasserman).
  Reston, Virginia, May 16-19, 2018.

• BFF5: Foundations of Data Science
  Invited Speaker.
  University of Michigan, May 7-9, 2018.

• Department Colloquium.
  "Philosophy of Science, Principled Statistical Inference, and Data Science."
  Michigan State University, March 13, 2018.
  

• Department Colloquium.
  "A Tour of Modern Higher-Order Asymptotics."
  NC State University, February 12th, 2018.
  

• Department Colloquium.
  "Philosophy of Science, Principled Statistical Inference, and Data Science."
  UNC Chapel Hill, November 15th, 2017.
  

• Wharton Statistics Seminar.
  "Philosophy of Science, Principled Statistical Inference, and Data Science."
  University of Pennsylvania, November 1st, 2017.

• Stochastics Seminar.
  "Optimal block bootstrap estimation for nonsmooth functionals for weakly dependent sequences."
  Georgia Tech, October 26th, 2017.

• Statistics Seminar.
  "Philosophy of Science, Statistics, and Data Science."
  Washington University in St. Louis, October 23rd, 2017.

• Statistical Machine Learning Group.
  "Philosophy of Science, Principled Statistical Inference, and Data Science."
  Carnegie Mellon University, October 5th, 2017.

• ICSA 2017, Invited Session (Organizer: Min-ge Xie).
  "Higher-Order BFF (Bayes Fiducial Frequentist)."
  Chicago, IL, June 26th, 2017.

• Department of Statistics, A Celebration of Peter McCullagh.
  "Some Aspects of Bayesian Higher-Order Asymptotics."
  University of Chicago, June 7th, 2017.

• Department of Business Analytics and Statistics Seminar.
  "Principled Statistical Inference in the Age of Data Science."
  University of Tennessee Knoxville, April 28th, 2017.

• Department of Mathematics, Statistics Seminar.
  "Optimal Block Bootstrap Estimation for Sample Quantiles Under Weak Dependence."
  Washington University in St. Louis, March 3rd, 2017.

• Department of Statistics Seminar.
  "Quantifying Nuisance Parameter Effects in Likelihood and Bayesian Inference."
  Northwestern University, November 9th, 2016.

• Department of Statistics Seminar.
  "Conditional Probability Matching Priors."
  University of Missouri Columbia, October 21st, 2016.

• Workshop on Higher-Order Asymptotics & Post-Selection Inference.
  "Tutorials on Post-Selection Inference and Higher-Order Asymptotics."
  Washington University in St. Louis, September 30th, 2016.

• PhyStat-nu Workshop on Statistical Issues in Experimental Neutrino Physics.
  "Post-Selection Inference."
  Fermilab, September 19th, 2016.

• JSM 2016, IMS Contributed Session.
  "A Unified Framework for Bayes Factor Asymptotics."
  Chicago, IL, August 1st, 2016.

• Department of Mathematics, Statistics Seminar.
  "Bayes Factors and Asymptotics."
  Imperial College London, May 27th, 2016.

• Department of Statistics Seminar.
  University of Illinois Urbana-Champaign, April 28th, 2016.

• Department of Statistical Sciences Colloquium.
  University of Toronto, November 12th, 2015.

• Department of Mathematics, Statistics Seminar.
  Washington University in St. Louis, September 25th, 2015

• Department of Statistics & Biostatistics Colloquium.
  Rutgers University, September 23rd, 2015.

• Department of Statistics Colloquium.
  Rice University, April 20th, 2015.

• Department of Statistics and Data Sciences Colloquium.
  University of Texas Austin, April 17th, 2015