Hartung, H.-P. et al., 2015. Predictors of disease activity in 857 patients with MS treated with interferon beta-1b. Journal of neurology, 262, pp.2466–2471.

Joe, H. et al., 2015. Preface to special issue on high-dimensional dependence and copulas. Journal of Multivariate Analysis, 138, pp.1-3.

Wong, S.W.K. et al., 2015. Quantifying uncertainty in lumber grading and strength prediction: a Bayesian approach. Technometrics, 58, pp.236-243.

Hall, W.A. et al., 2015. A randomized controlled trial of an intervention for infants' behavioral sleep problems. BMC Pediatr, 15, p.181.

Lavoie, P.M. et al., 2015. A Randomized Controlled Trial of the Use of Oral Glucose with or without Gentle Facilitated Tucking of Infants during Neonatal Echocardiography. PLoS ONE, 10, p.e0141015.

Agostinelli, C. et al., 2015. Rejoinder on: Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination. TEST, 24, pp.484-488.

Agostinelli, C. et al., 2015. Rejoinder on: Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination. TEST, 24, pp.484-488.

Wang, L., Chen, J. & Pu, X., 2015. Resampling calibrated adjusted empirical likelihood. Canadian Journal of Statistics, 43, pp.42–59.

Etminan, M., Luo, H. & Gustafson, P., 2015. Risk of intracranial hypertension with intrauterine levonorgestrel. Therapeutic advances in drug safety, 6, pp.110–113.

Agostinelli, C. et al., 2015. Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination. TEST, 24, pp.441-461.

Agostinelli, C. et al., 2015. Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination. TEST, 24, pp.441-461.

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.

Reich, D.S. et al., 2015. Sample-size calculations for short-term proof-of-concept studies of tissue protection and repair in multiple sclerosis lesions via conventional clinical imaging. Multiple Sclerosis Journal, 21, pp.1693-1704.

Reich, D.S. et al., 2015. Sample-size calculations for short-term proof-of-concept studies of tissue protection and repair in multiple sclerosis lesions via conventional clinical imaging. Multiple Sclerosis Journal, p.1352458515569098.

Guarna, M.M. et al., 2015. A search for protein biomarkers links olfactory signal transduction to social immunity. BMC genomics, 16, p.63.

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.