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Krupskii, P. & Genton, M., 2018. Linear Factor Copula Models and Their Properties. Scandinavian Journal of Statistics, to appear.
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.
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.