Zhang, H. & Wu, L., 2018. A Mechanistic Nonlinear Model for Truncated and Mis-Measured Time-varying Covariates in Survival Models, with Applications in HIV/AIDS. Journal of the Royal Statistical Society, C.

Dinsdale, D.R. & Salibian-Barrera, M., 2018. Methods for preferential sampling in geostatistics. Journal of the Royal Statistical Society Series C.

Högg, T. et al., 2018. Mining healthcare data for markers of the multiple sclerosis prodrome. Multiple Sclerosis and Related Disorders.

Fu, E. & Heckman, N., 2018. Model-based curve registration via stochastic approximation EM algorithm. Computational Statistics and Data Analysis, to appear. Available at: https://arxiv.org/abs/1712.07265.

Zhou, G. & Wu, L., 2018. Modeling semi-continuous longitudinal data with order constraints. Statistics in Medicine.

Zhao, B. et al., 2018. Modular Generative Adversarial Networks. In European Conference on Computer Vision. European Conference on Computer Vision.

Dorri, F. et al., 2018. MuClone: somatic mutation detection and classification through probabilistic integration of clonal population information. Communications Biology, 2, pp.1–10.

Dorri, F. et al., 2018. MuClone: somatic mutation detection and classification through probabilistic integration of clonal population information. Communications Biology , 2.

Chang, B. et al., 2018. Multi-level Residual Networks from Dynamical Systems View. In International Conference on Learning Representations. International Conference on Learning Representations. Available at: https://openreview.net/forum?id=SyJS-OgR-.

Zhu, G. & Chen, J., 2018. Multi-Parameter One-Sided Monitoring Tests. Technometrics, 60, pp.398–407.

Lee, D. & Joe, H., 2018. Multivariate extreme value copulas with factor and tree dependence structures. Extremes, 21, pp.147-176.

Joe, H., 2018. Parsimonious graphical dependence models constructed from vines. Canadian Journal of Statistics, 46, pp.532-555.

Bierkens, J. et al., 2018. Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains. Statistics and Probability Letters, 136, pp.148–154.

Campbell, K., Bouchard-Côté, A. & Shah, S., 2018. Probabilistic inference of clonal gene expression through integration of RNA & DNA-seq at single-cell resolution. In HitSeq. HitSeq.

Sadatsafavi, M. et al., 2018. Relative impact characteristic curve: a graphical tool to visualize and quantify the clinical utility and population-level consequences of implementing markers. Annals of epidemiology.

Chang, B. et al., 2018. Reversible Architectures for Arbitrarily Deep Residual Neural Networks. In AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence.

Maronna, R.A. et al., 2018. Robust Statistics: Theory and Methods (with R) 2nd ed., New York: John Wiley & Sons Ltd .

Zhuang, W., Hu, B. & Chen, J., 2018. Semiparametric inference for the dominance index under the density ratio model. Biometrika.

Zidek, J.V. & LUM, C.O.N.R.O.Y., 2018. Statistical challenges in assessing the engineering properties of forest products. Annual review of statistics and its application - invitation only, 5, pp.237-264.

Kondo, Y. et al., 2018. Subset selection procedures with an application to lumber strength properties. Sanhkya Ser B, 80, pp.146-172.

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.