Todd Kuffner
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Papers submitted or under revision

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]

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