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2017
Straub, J. et al., 2017. Efficient global point cloud alignment using Bayesian nonparametric mixtures. In IEEE Conference on Computer Vision and Pattern Recognition. IEEE Conference on Computer Vision and Pattern Recognition.
Nolde, N. & Ziegel, J., 2017. Elicitability and backtesting: Perspectives for banking regulation. The Annals of Applied Statistics, 11, p.1833--1874.
Deligiannidis, G., Bouchard-Côté, A. & Doucet, A., 2017. Exponential ergodicity of the Bouncy Particle Sampler. arXiv, 1705.04579.
Krupskii, P. & Genton, M., 2017. Factor copula models for data with spatio-temporal dependence. Spatial Statistics, 22(1), pp.180-195.
Chen, J., 2017. On finite mixture models. Statistical Theory and Related Fields, 1, pp.15–27.
Chen, H., Loeppky, J.L. & Welch, W.J., 2017. Flexible Correlation Structure for Accurate Prediction and Uncertainty Quantification in Bayesian Gaussian Process Emulation of a Computer Model. SIAM/ASA Journal on Uncertainty Quantification, 5, pp.598–620. Available at: https://doi.org/10.1137/15M1008774.
Islam, N. et al., 2017. Hepatitis C cross-genotype immunity and implications for vaccine development. Scientific reports, 7, p.12326.
Cai, S., Chen, J. & Zidek, J.V., 2017. Hypothesis testing in the presence of multiple samples under density ratio models. Statistica Sinica, 27, pp.716–783.
Islam, N. et al., 2017. Incidence, risk factors, and prevention of hepatitis C reinfection: a population-based cohort study. The Lancet Gastroenterology & Hepatology, 2, pp.200–210.
McDonald, D.J., 2017. Minimax Density Estimation for Growing Dimension A. Singh & Zhu, J. , eds. Proceedings of the Twentieth International Conference on Artificial Intelligence and Statistics (AISTATS), 54, pp.194–203. Available at: http://proceedings.mlr.press/v54/mcdonald17a.html.
Panagiotelis, A. et al., 2017. Model selection for discrete regular vine copulas. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 106, pp.138-152.
Hua, L. & Joe, H., 2017. Multivariate dependence modeling based on comonotonic factors. Journal of Multivariate Analysis, 155, pp.317-333.
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.

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