Liu, J. et al., 2015. A flexible approach for multivariate mixed-effects models in the presence of non-ignorable missingness and measurement error. Journal of Statistical Computation and Simulation.

Kondo, Y., Zhao, Y. & Petkau, J., 2015. A flexible mixed-effect negative binomial regression model for detecting unusual increases in MRI lesion counts in individual multiple sclerosis patients. Statistics in Medicine, 34, pp.2165-2180.

Lei, E. et al., 2015. Functional Data Model for Genetically Related Individuals With Application to Cow Growth. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 24, pp.756-770.

Dong, J. et al., 2015. Further investigation of safety monitoring guidelines based on magnetic resonance imaging lesion activity in multiple sclerosis clinical trials. Multiple Sclerosis Journal, 21, pp.101-104. Available at: http://msj.sagepub.com/content/early/2014/05/20/1352458514533846.

Luo, H., Burstyn, I. & Gustafson, P., 2015. Gene-enviroment Independence in case-control studies: Issues of parameterization and Bayesian inference. Statistics in Biosciences.

Kingsolver, J.G. et al., 2015. Genetic Variation, Simplicity, and Evolutionary Constraints for Function-Valued Traits. AMERICAN NATURALIST, 185, pp.E166-E181.

Ardlie, K.G. et al., 2015. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. SCIENCE, 348, pp.648-660.

Leung, A. et al., 2015. GSE: Robust Estimation in the Presence of Cellwise and Casewise Contamination and Missing Data. , p.R package.

Cai, S., Chen, J. & Zidek, J.V., 2015. Hypothesis testing in the presence of multiple samples under a density ratio model. Statistica Sinica, p.Accepted.

McPherson, A. et al., 2015. The Importance of Mutation Loss in Modelling Evolution and Metastasis in Genomically Unstable Cancers. In HitSeq. HitSeq.

Liu, Y., Shaddick, G. & Zidek, J.V., 2015. incorporating high-dimensional exposure modelling into studies of air pollution and health. Statistics in Biosciences, p.Accepted.

Roth, A. et al., 2015. Inference of clonal genotypes from single cell sequencing data. In HitSeq. HitSeq.

Chen, J., 2015. The joy of proofs in statistical research. Canadian Journal of Statistics, 43, pp.481–497.

Li, S. et al., 2015. Likelihood Ratio Test for Multi-Sample Mixture Model and Its Application to Genetic Imprinting. Journal of the American Statistical Association, 110, pp.867–877.

Joe, H., 2015. Markov count time series models with covariates. In R. A. Davis et al., eds. Handbook of Discrete-Valued Time Series. Handbook of Discrete-Valued Time Series. Boca Raton, FL: Chapman & Hall/CRC, pp. 29–49. Available at: http://www.crcpress.com/product/isbn/9781466577732.

Crane, J.M. et al., 2015. Maternal and perinatal outcomes of pregnancies delivered at 23 weeks' gestation. J Obstet Gynaecol Can, 37, pp.214–224.

Fairbrother, N. et al., 2015. Maternal thoughts of harm in response to infant crying: an experimental analysis. Arch Womens Ment Health, 18, pp.447–455.

Qiu, J. & WU, L.A.N.G., 2015. A moving blocks empirical likelihood method for longitudinal data. Biometrics, 71, pp.616–624. Available at: http://onlinelibrary.wiley.com/doi/10.1111/biom.12317/abstract.

Shirani, A. et al., 2015. Multiple sclerosis in older adults: the clinical profile and impact of interferon beta treatment. BioMed Research International, 2015, p.ID451912, 11 pages.

Shirani, A. et al., 2015. Multiple Sclerosis in Older Adults: The Clinical Profile and Impact of Interferon Beta Treatment. BioMed research international, 2015.

Lavoie, P.M. et al., 2015. Oral glucose during targeted neonatal echocardiography: is it useful?. Arch. Dis. Child. Fetal Neonatal Ed., 100, pp.F374–375.

Bouchard-Côté, A., Doucet, A. & Roth, A., 2015. Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models. arXiv, 1508.02663.

Bouchard-Côté, A., Doucet, A. & Roth, A., 2015. Particle Gibbs split-merge sampling for Bayesian inference in mixture models. Journal of Machine Learning Research, (Accepted).

Zhao, Y. et al., 2015. Personalized activity index, a new safety monitoring tool for multiple sclerosis clinical trials. Multiple Sclerosis Journal – Experimental, Translational and Clinical, 1, pp.1-15. Available at: http://mso.sagepub.com/content/1/2055217315577829.

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.