Levinson, D.F. et al., 2014. Genetic Studies of Major Depressive Disorder: Why Are There No Genome-wide Association Study Findings and What Can We Do About It?. BIOLOGICAL PSYCHIATRY, 76, pp.510-512.

Nolde, N., 2014. Geometric interpretation of the residual dependence coefficient. JOURNAL OF MULTIVARIATE ANALYSIS, 123, pp.85-95.

Sharma, A.A. et al., 2014. Hierarchical maturation of innate immune defences in very preterm neonates. Neonatology, 106, pp.1–9.

Wang, D. & Gustafson, P., 2014. On the Impact of Misclassification in an Ordinal Exposure Variable. Epidemiologic Methods, 3, pp.97–106.

Vinall, J. et al., 2014. Invasive procedures in preterm children: brain and cognitive development at school age. Pediatrics, 133, pp.412–421.

Shirani, A. et al., 2014. Investigation of heterogeneity in the association between interferon beta and disability progression in multiple sclerosis: an observational study. European Journal of Neurology, 21, pp.835–844.

Shirani, A. et al., 2014. Investigation of heterogeneity in the association between interferon beta and disability progression in multiple sclerosis: an observational study. European Journal of Neurology, 21, pp.835–844. Available at: http://onlinelibrary.wiley.com/doi/10.1111/ene.12324/abstract.

Zhang, H. & Zamar, R.H., 2014. Least angle regression for model selection. Wiley Interdisciplinary Reviews: Computational Statistics, 6, pp.116–123.

Homrighausen, D. & McDonald, D.J., 2014. Leave-one-out cross-validation is risk consistent for lasso. Machine Learning, 97, pp.65–78. Available at: http://dx.doi.org/10.1007/s10994-014-5438-z.

Liu, Y., Chen, J. & Li, T., 2014. Level-specific correction for nonparametric likelihoods. Journal of Nonparametric Statistics, 26, pp.433–449.

Li, S. et al., 2014. Likelihood Ratio Test for Multi-Sample Mixture Model and its Application to Genetic Imprinting. Journal of the American Statistical Association, pp.00–00.

Karim, M.Ehsanul et al., 2014. Marginal Structural Cox Models for Estimating the Association Between β-Interferon Exposure and Disease Progression in a Multiple Sclerosis Cohort. American journal of epidemiology, 180, pp.160–171.

Karim, M.E. et al., 2014. Marginal structural Cox models for estimating the association between beta-interferon exposure and disease progression in a multiple sclerosis cohort. American Journal of Epidemiology, 180, pp.160-171.

Barr, R.G. et al., 2014. Maternal frustration, emotional and behavioural responses to prolonged infant crying. Infant Behav Dev, 37, pp.652–664.

Jun, S.-H. & Bouchard-Côté, A., 2014. Memory (and time) efficient sequential Monte Carlo. In International Conference on Machine Learning (ICML). International Conference on Machine Learning (ICML). pp. 514–522.

Gustafson, P. & Greenland, S., 2014. Misclassification. In Handbook of Epidemiology. Handbook of Epidemiology. Springer, pp. 639–658.

Ng, C.T. & Joe, H., 2014. Model comparison with composite likelihood information criteria. Bernoulli, 20, pp.1738-1764.

Bonner, S.J., Newlands, N.K. & Heckman, N.E., 2014. Modeling regional impacts of climate teleconnections using functional data analysis. ENVIRONMENTAL AND ECOLOGICAL STATISTICS, 21, pp.1-26.

Ayad, M., Coia, V. & Kihel, O., 2014. The Number of Relatively Prime Subsets of a Finite Union of Sets of Consecutive Integers. Journal of Integer Sequences, 17, p.3.

Le, N. et al., 2014. Occupational exposure and ovarian cancer risk. Cancer causes Control. Cancer causes Control, 7, pp.829-841.

Cubranic, D., Dunham, B. & Kim, D., 2014. On-line homework in probability and statistics: WeBWorK incorporating R. In 9th International Conference on Teaching Statistics. 9th International Conference on Teaching Statistics.

Brechmann, E.C. & Joe, H., 2014. Parsimonious parameterization of correlation matrices using truncated vines and factor analysis. Computational Statistics & Data Analysis, 77, pp.233-251.

Raj, T. et al., 2014. Polarization of the Effects of Autoimmune and Neurodegenerative Risk Alleles in Leukocytes. SCIENCE, 344, pp.519-523.

Dean, C.B., Heckman, N. & Reid, N., 2014. Practical Suggestions for Developing as an Academic Leader. In Leadership and Women in Statistics. Leadership and Women in Statistics. Chapman and Hall.

Falasinnu, T. et al., 2014. Predictors identifying those at increased risk for STDs: a theory-guided review of empirical literature and clinical guidelines. International journal of STD & AIDS, p.0956462414555930.