Panagiotelis, A. et al., 2017. Model selection for discrete regular vine copulas. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 106, pp.138-152.

Park, Y. et al., 2017. Multi-tissue polygenic models for transcriptome-wide association studies. bioRxiv, p.107623.

Hua, L. & Joe, H., 2017. Multivariate dependence modeling based on comonotonic factors. Journal of Multivariate Analysis, 155, pp.317-333.

Leung, A., Yohai, V. & Zamar, R., 2017. Multivariate Location and Scatter Matrix Estimation Under Cellwise and Casewise Contamination. CSDA.

Béliveau, A. et al., 2017. Network meta-analysis of disconnected networks: How dangerous are random baseline treatment effects?. Research synthesis methods, 8, pp.465–474.

McDonald, D.J., Shalizi, C.Rohilla & Schervish, M., 2017. Nonparametric risk bounds for time-series forecasting. Journal of Machine Learning Research, 18, pp.1–40. Available at: http://www.jmlr.org/papers/v18/13-336.html.

Joe, H., 2017. Parametric copula families for statistical models. In M. Ubeda-Flores et al., eds. Copulas and Dependence Models with Applications: Contributions in Honor of Roger B. Nelsen. Copulas and Dependence Models with Applications: Contributions in Honor of Roger B. Nelsen. Berlin: Springer, pp. 119–134. Available at: https://link.springer.com/book/10.1007/978-3-319-64221-5.

Bouchard-Côté, A., Doucet, A. & Roth, A., 2017. Particle Gibbs split-merge sampling for Bayesian inference in mixture models. Journal of Machine Learning Research, 18, pp.1–39.

Kepplinger, D. et al., 2017. PGCA: An algorithm to link protein groups created from MS/MS data. PLOS ONE, 12, pp.1-19. Available at: https://doi.org/10.1371/journal.pone.0177569.

Vanetti, P. et al., 2017. Piecewise Deterministic Markov Chain Monte Carlo. arXiv, 1707.05296.

Zhai, Y. & Bouchard-Côté, A., 2017. A Poissonian model of indel rate variation for phylogenetic tree inference. Systematic Biology, 66, pp.698–714.

Zhai, Y. & Bouchard-Côté, A., 2017. A Poissonian model of indel rate variation for phylogenetic tree inference. Systematic Biology, (Accepted).

Lee, W. et al., 2017. Pre-averaged kernel estimators for the drift function of a diffusion process in the presence of microstructure noise. Statistical Inference for Stochastic Processes, 20(2).

Ding, L. & McDonald, D.J., 2017. Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression. Bioinformatics, 33, pp.i350–i358. Available at: http://dx.doi.org/10.1093/bioinformatics/btx265.

Khalili, A., Chen, J. & , , 2017. Regularization in regime-switching Gaussian autoregressive models. The Canadian Journal of Statistics, 45, p.374.

McPherson, A. et al., 2017. ReMixT: clone-specific genomic structure estimation in cancer. Genome Biology, 18.

Homrighausen, D. & McDonald, D.J., 2017. Risk consistency of cross-validation for lasso-type procedures. Statistica Sinica, 27, pp.1017–1036. Available at: http://dx.doi.org/10.5705/ss.202015.0355.

Boente, G., Martínez, A. & Salibian-Barrera, M., 2017. Robust estimators for additive models using backfitting. Journal of Nonparametric Statistics, 29, pp.744-767. Available at: https://doi.org/10.1080/10485252.2017.1369077.

Islam, N. et al., 2017. Role of primary T-cell immunodeficiency and hepatitis B coinfection on spontaneous clearance of hepatitis C: The BC Hepatitis Testers Cohort. Journal of viral hepatitis, 24, pp.421–429.

Jun, S.-H. et al., 2017. Sequential decision model for inference and prediction on non-uniform hypergraphs with application to knot matching from computational forestry. arXiv, 1708.07592.

Jun, S.-H. et al., 2017. Sequential Graph Matching with Sequential Monte Carlo. In AISTATS. AISTATS. pp. 1075–1084.

Jun, S.-H. & Bouchare-Cote, A., 2017. Sequential graph matching with sequential monte carlo S. W. K. Wonlg, ed. 20th International Conference on Artificial Intelligence and Statistics.

Ding, X., Qiu, Z. & Chen, X., 2017. Sparse transition matrix estimation for high-dimensional and locally stationary vector autoregressive models. Electronic Journal of Statistics, 11, pp.3871–3902.

Casquilho-Resende, C.M., Le, N.D. & Zidek, J.V., 2017. Spatio-temporal modelling of temperature fields in the Pacific Northwest. Environmetrics, p.Resubmitted.

de Souza, C.P.E., Heckman, N.E. & Xu, F., 2017. Switching nonparametric regression models for multi-curve data. Canadian Journal of Statistics, 45, pp.442–460. Available at: http://dx.doi.org/10.1002/cjs.11331.