Lee, D., Joe, H. & Krupskii, P., 2018. Tail-weighted dependence measures with limit being tail dependence coefficient. Journal of Nonparametric Statistics, to appear.

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.

Campbell, H. & Gustafson, P., 2018. The Validity and Efficiency of Hypothesis Testing in Observational Studies with Time-Varying Exposures. Observational Studies. Available at: https://obsstudies.org/validity-efficiency-hypothesis-testing-observational-studies-time-varying-exposures/.

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.

Lenzi, A. et al., 2017. Analysis of Aggregated Functional Data from Mixed Populations with Application to Energy Consumption. Environmetrics, 28(2).

Karim, M.E. et al., 2017. On the application of statistical learning approaches to construct inverse probability weights in marginal structural Cox models: Hedging against weight-model misspecification. Communications in Statistics - Simulation and Computation, 46, pp.7668-7697.

Karim, M.Ehsanul et al., 2017. On the application of statistical learning approaches to construct inverse probability weights in marginal structural cox models: hedging against weight-model misspecification. Communications in Statistics-Simulation and Computation, 46, pp.7668–7697.

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(26).

Högg, T. et al., 2017. Bayesian analysis of pair-matched case-control studies subject to outcome misclassification. Statistics in medicine, 36, pp.4196–4213.

Park, Y. et al., 2017. A Bayesian approach to mediation analysis predicts 206 causal target genes in Alzheimer's disease. bioRxiv, p.219428.

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.

Burstyn, I. et al., 2017. Correction of odds ratios in case-control studies for exposure misclassification with partial knowledge of the degree of agreement among experts who assessed exposures. Occup Environ Med, p.oemed–2017.

Salehi, S. et al., 2017. ddClone: joint statistical inference of clonal populations from single-cell and bulk tumor sequencing data. Genome Biology, 18.

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.