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Factor copula models for data with spatio-temporal dependence. Spatial Statistics, 22(1), pp.180-195.
, 2017. On finite mixture models. Statistical Theory and Related Fields, 1, pp.15–27.
, 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(1), p.620. Available at: http://epubs.siam.org/doi/abs/10.1137/15M1008774.
, 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.
, 2017. Health-care use before a first demyelinating event suggestive of a multiple sclerosis prodrome: a matched cohort study. The Lancet Neurology, 16, pp.445–451.
, 2017. Hepatitis C cross-genotype immunity and implications for vaccine development. Scientific reports, 7, p.12326.
, 2017. Hypothesis testing in the presence of multiple samples under density ratio models. Statistica Sinica, 27, pp.716–783.
, 2017. Identification of treatment responders based on multiple longitudinal outcomes with applications to multiple sclerosis patients. Statistics in Medicine, 36, pp.1862-1883.
, 2017. Incidence, risk factors, and prevention of hepatitis C reinfection: a population-based cohort study. The Lancet Gastroenterology & Hepatology, 2, pp.200–210.
, 2017. , 2017.
Model selection for discrete regular vine copulas. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 106, pp.138-152.
, 2017. Multi-tissue polygenic models for transcriptome-wide association studies. bioRxiv, p.107623.
, 2017. Multivariate dependence modeling based on comonotonic factors. Journal of Multivariate Analysis, 155, pp.317-333.
, 2017. , 2017.
Network meta-analysis of disconnected networks: How dangerous are random baseline treatment effects?. Research synthesis methods, 8, pp.465–474.
, 2017. Parametric copula families for statistical models. In 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.
, 2017. Particle Gibbs split-merge sampling for Bayesian inference in mixture models. Journal of Machine Learning Research, 18, pp.1–39.
, 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.
, 2017. Piecewise Deterministic Markov Chain Monte Carlo. arXiv, 1707.05296.
, 2017. A Poissonian model of indel rate variation for phylogenetic tree inference. Systematic Biology, 66, pp.698–714.
, 2017. A Poissonian model of indel rate variation for phylogenetic tree inference. Systematic Biology, (Accepted).
, 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).
, 2017. Regularization in regime-switching Gaussian autoregressive models. The Canadian Journal of Statistics, 45, p.374.
, 2017. ReMixT: clone-specific genomic structure estimation in cancer. Genome Biology, 18.
, 2017.