Export 1667 results:
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.