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2015
Troncoso, P., 2015. The SAGE handbook of multilevel modeling. International Journal of Research & Method in Education, 38, pp.100–101. Available at: http://dx.doi.org/10.1080/1743727X.2014.986027.
Tremlett, H. et al., 2015. Serum proteomics in multiple sclerosis disease progression. Journal of proteomics, 118, pp.2–11*Senior Author.
Boente, G. & Salibian-Barrera, M., 2015. S-Estimators for Functional Principal Component Analysis. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 110, pp.1100-1111.
Pierson, E. et al., 2015. Sharing and Specificity of Co-expression Networks across 35 Human Tissues. PLOS COMPUTATIONAL BIOLOGY, 11, p.e1004220.
Straub, J. et al., 2015. Small-variance nonparametric clustering on the hypersphere. In IEEE Conference on Computer Vision and Pattern Recognition. IEEE Conference on Computer Vision and Pattern Recognition.
Shaddick, G. & Zidek, J.V., 2015. Spatio-Temporal Methods in Environmental Epidemiology, CRC Press.
Campbell, T. et al., 2015. Streaming, distributed variational inference for Bayesian nonparametrics. In Advances in Neural Information Processing Systems. Advances in Neural Information Processing Systems.
Krupskii, P. & Joe, H., 2015. Structured factor copula models: Theory, inference and computation. Journal of Multivariate Analysis, 138, pp.53-73.
Krupskii, P. & Joe, H., 2015. Structured factor copula models: theory, inference and computation. Journal of Multivariate Analysis, 138, pp.53–73.
Krupskii, P. & Joe, H., 2015. Tail-weighted measures of dependence. Journal of Applied Statistics, 42, pp.614-629.
Krupskii, P. & Joe, H., 2015. Tail-weighted measures of dependence. Journal of Applied Statistics, 42, pp.614–629.
Xu, C. & Chen, J., 2015. A Thresholding Algorithm for Order Selection in Finite Mixture Models. Communications in Statistics-Simulation and Computation, 44, pp.433–453.
Brechmann, E.C. & Joe, H., 2015. Truncation of vine copulas using fit indices. Journal of Multivariate Analysis, 138, pp.19-33.
2014
Shi, T., Steyn, D. & Welch, W.J., 2014. Air Quality Model Evaluation Using Gaussian Process Modelling and Empirical Orthogonal Function Decomposition. In Air Pollution Modeling and its Application XXIII. Air Pollution Modeling and its Application XXIII. Springer International Publishing, pp. 457–462. Available at: http://link.springer.com/chapter/10.1007/978-3-319-04379-1_75.
Campbell, T. & How, J.P., 2014. Approximate decentralized Bayesian inference. In Uncertainty in Artificial Intelligence. Uncertainty in Artificial Intelligence.
Maydeu-Olivares, A. & Joe, H., 2014. Assessing approximate fit in categorical data analysis. Multivariate Behavioral Research, 49, pp.305-328.
Evans, C. et al., 2014. Association between beta-interferon exposure and hospital events in multiple sclerosis. Pharmacoepidemiology and drug safety, 23, pp.1213–1222.
Evans, C. et al., 2014. Association between beta-interferon exposure and hospital events in multiple sclerosis. Pharmacoepidemiology and Drug Safety, 23, pp.1213-1222.
Gustafson, P., 2014. Bayesian inference in partially identified models: Is the shape of the posterior distribution useful?. Electronic Journal of Statistics, 8, pp.476-496.

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