Todd Kuffner

Research Support

Papers submitted or under revision

Papers published or accepted

  1. On the validity of the formal Edgeworth expansion for posterior densities, with J.E. Kolassa. Annals of Statistics, accepted. [pdf]

  2. On prediction of future insurance claims when the model is uncertain, with L. Hong and R. Martin. Variance, accepted.  [ssrn]

  3. 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, to appear. [doi] [preprint]

  4. L. Hong, T.A. Kuffner, and R.G. Martin (2018). On overfitting and post-selection uncertainty assessments. Biometrika. [doi] [preprint]

  5. 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]

  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.A. Kuffner and S.G. Walker (2017+). Why are p-values controversial?. The American Statistician, to appear.  [preprint]

  8. 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]

  9. 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]

  10. 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]

  11. 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]

Selected working papers and in preparation

Upcoming Talks

In the Past