Export 1625 results:
2017
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.
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.
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.
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, (Accepted).
Zhai, Y. & Bouchard-Côté, A., 2017. A Poissonian model of indel rate variation for phylogenetic tree inference. Systematic Biology, 66, pp.698–714.
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.

Pages