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2014
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.
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.
Zhang, J. et al., 2014. Prinsimp. R JOURNAL, 6, pp.27-42.
Zhang, J. et al., 2014. Prinsimp. The R Journal, 6(2), pp.27–42. Available at: http://journal.r-project.org/archive/2014-2/zhang-etal.pdf.
Hollander, Z. et al., 2014. Proteomic biomarkers of recovered heart function. European journal of heart failure, 16, pp.551–559.
Roth, A. et al., 2014. PyClone: statistical inference of clonal population structure in cancer. Nature Methods, 11, pp.396–398.
Zidek, J.V., Shaddick, G. & Taylor, C.G., 2014. Reducing estimation bias in adaptively changing monitoring networks with preferential site selection. The Annals of Applied Statistics, 8, pp.1640–1670.
Zidek, J.V. et al., 2014. Reducing estimation bias in adaptively changing monitoring networks with preferential site selection. The Annals of Applied Statistics, 8, pp.1640–1670.
Hua, L., Joe, H. & Li, H., 2014. Relations between hidden regular variation and the tail order of copulas. Journal of Applied Probability, 51, pp.37-57.
Reich, D.S. et al., 2014. Sample-size calculations for short-term proof-of-concept studies of tissue protection and repair in MS lesions via conventional clinical imaging. In MULTIPLE SCLEROSIS JOURNAL. MULTIPLE SCLEROSIS JOURNAL. SAGE PUBLICATIONS LTD 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND, pp. 284–284.
Bouchard-Côté, A., 2014. Sequential Monte Carlo (SMC) for Bayesian phylogenetics. In M. - H. Chen, Kuo, L. , & Lewis, P. O. (eds.), eds. Bayesian phylogenetics: methods, algorithms, and applications. Bayesian phylogenetics: methods, algorithms, and applications. pp. 163–186.
Coia, V. & Huang, M.Ling, 2014. A Sieve model for extreme values. Journal of Statistical Computation and Simulation, 84, pp.1692–1710.
Xu, C. & Chen, J., 2014. The Sparse MLE for Ultrahigh-Dimensional Feature Screening. Journal of the American Statistical Association, 109, pp.1257–1269.
Cai, S. et al., 2014. Statistical modeling and forecasting of fruit crop phenology under climate change. Environmetrics, 25, pp.621–629.
Nolde, N. & Parker, G., 2014. Stochastic analysis of life insurance surplus. INSURANCE MATHEMATICS & ECONOMICS, 56, pp.1-13.
Hua, L. & Joe, H., 2014. Strength of tail dependence based on conditional tail expectation. Journal of Multivariate Analysis, 123, pp.143-159.
de Souza, C.P.E. & Heckman, N.E., 2014. Switching nonparametric regression models. JOURNAL OF NONPARAMETRIC STATISTICS, 26, pp.617-637.
Mostafavi, S. et al., 2014. Variation and Genetic Control of Gene Expression in Primary Immunocytes across Inbred Mouse Strains. JOURNAL OF IMMUNOLOGY, 193, pp.4485-4496.
Ascherio, A. et al., 2014. Vitamin D as an early predictor of multiple sclerosis activity and progression. JAMA neurology, 71, pp.306–314.

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