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2018
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.
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 .
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.
Homrighausen, D. & McDonald, D.J., 2018. A study on tuning parameter selection for the high-dimensional lasso. Journal of Statistical Computation and Simulation, 88, pp.2865–2892. Available at: http://dx.doi.org/10.1080/00949655.2018.1491575.
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. Applied Statistics, p.Accepted Dec 4, 2017.
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.
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.

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