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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).
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.
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.
Jun, S.-H. et al., 2017. Sequential Graph Matching with Sequential Monte Carlo. In AISTATS. AISTATS. pp. 1075–1084.
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.
Park, Y. et al., 2017. Temporal expression divergence of network modules. bioRxiv, p.167734.
Ye, Q. & Wu, L., 2017. Two-Step and Likelihood Methods for Joint Models of Longitudinal and Survival Data. Communication in Statistics, 46(8).
Riddell, C.A., Zhao, Y. & Petkau, J., 2016. An adaptive clinical trials procedure for a sensitive subgroup examined in the multiple sclerosis context. Statistical Methods in Medical Research, 25, pp.1330-1345. Available at: http://smm.sagepub.com/content/early/2013/04/01/0962280213480576.
Chen, H. et al., 2016. Analysis Methods for Computer Experiments: How to Assess and What Counts?. Statistical Science, 31, pp.40–60. Available at: https://dx.doi.org/10.14288/1.0302078.
Zhang, T. et al., 2016. Association between the use of selective serotonin reuptake inhibitors and multiple sclerosis disability progression. Pharmacoepidemiology and Drug Safety, 25, pp.1150-1159.