Todd Kuffner
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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


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