Export 1667 results:
2018
Lee, D., Joe, H. & Krupskii, P., 2018. Tail-weighted dependence measures with limit being the tail dependence coefficient. Journal of Nonparametric Statistics, 30, pp.262-290.
Fernandez, M. et al., 2018. Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images. Journal of Chemical Information and Modeling, (in press).
Liu, Y. et al., 2018. Using Artificial Censoring to Improve Extreme Tail Quantile Estimates. Journal of the Royal Statistical Society Series C, 67(4), pp.791-812. Available at: https://doi.org/10.1111/rssc.12262.
Liu, Y. et al., 2018. Using artificial censoring to improve extreme tail quantile estimates. Applied Statistics, p.Accepted Dec 4, 2017.
Gomulkiewicz, R. et al., 2018. Variation and evolution of function-valued traits. Annual Review of Ecology, Evolution, and Systematics, 49(1).
Gustafson, P. & McCandless, L.C., 2018. When Is a Sensitivity Parameter Exactly That?. Statistical Science, 33, pp.86–95.
2017
Högg, T. et al., 2017. Bayesian analysis of pair-matched case-control studies subject to outcome misclassification. Statistics in Medicine, 36, pp.4196-4213.
Högg, T. et al., 2017. Bayesian analysis of pair-matched case-control studies subject to outcome misclassification. Statistics in medicine, 36, pp.4196–4213.
Bouchard-Côté, A., Vollmer, S.J. & Doucet, A., 2017. The Bouncy Particle Sampler: A non-reversible rejection-free Markov chain Monte Carlo method. Journal of the American Statistical Association, (Accepted).
McCandless, L.C. & Gustafson, P., 2017. A comparison of Bayesian and Monte Carlo sensitivity analysis for unmeasured confounding. Statistics in medicine, 36, pp.2887–2901.
Campbell, H. & Gustafson, P., 2017. Conditional Equivalence Testing: an alternative remedy for publication bias. arXiv preprint arXiv:1710.01771.
Chen, J., 2017. Consistency of the MLE under mixture models. Statistical Science, 32, pp.47–63.
Krupskii, P., 2017. Copula-based measures of reflection and permutation asymmetry and statistical tests. Statistical Papers, 58(4), pp.1165-1187.
Tomal, J.H., Welch, W.J. & Zamar, R.H., 2017. Discussion of Random-Projection Ensemble Classification by T. I. Cannings and R. J. Samworth. Journal of the Royal Statistical Society B, 79, pp.1024-1025. Available at: http://dx.doi.org/10.1111/rssb.12228.
Lindsten, F. et al., 2017. Divide-and-conquer with sequential Monte Carlo. Journal of Computational Statistics and Graphics, 26, pp.445–458.
Lindsten, F. et al., 2017. Divide-and-conquer with sequential Monte Carlo. Journal of Computational Statistics and Graphics, 26, pp.445–458.

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