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2016
Riddell, C.A., Zhao, Y. & Petkau, J., 2016. An adaptive clinical trials procedure for a sensitive subgroup examined in the multiple sclerosis context. Statistical Methods in Medical Research, 25, pp.1330-1345. Available at: http://smm.sagepub.com/content/early/2013/04/01/0962280213480576.
Chen, H. et al., 2016. Analysis Methods for Computer Experiments: How to Assess and What Counts?. Statistical Science, 31, pp.40–60. Available at: https://dx.doi.org/10.14288/1.0302078.
Zhang, T. et al., 2016. Association between the use of selective serotonin reuptake inhibitors and multiple sclerosis disability progression. Pharmacoepidemiology and Drug Safety, 25, pp.1150-1159.
Liu, Y. et al., 2016. Bayesian Data Fusion Approaches to Predicting Spatial Tracks: Application to Marine Mammals. Annals of Applied Statistics, p.Accepted.
Xia, M. & Gustafson, P., 2016. Bayesian regression models adjusting for unidirectional covariate misclassification. Canadian Journal of Statistics, 44, pp.198–218.
Roth, A. et al., 2016. Clonal genotype and population structure inference from single-cell tumor sequencing. Nature Methods, 13, pp.575–576.
Ng, C.T. & Joe, H., 2016. Comparison of non-nested models under a general measure of distance. Journal of Statistical Planning and Inference, 170, pp.166-185.
Karim, M.E. et al., 2016. Comparison of statistical approaches for dealing with immortal time bias in drug effectiveness studies. American Journal of Epidemiology, 184, pp.325-335.
Karim, M.Ehsanul et al., 2016. Comparison of Statistical Approaches for Dealing With Immortal Time Bias in Drug Effectiveness Studies. American Journal of Epidemiology, p.kwv445.
Karim, M.E. et al., 2016. Comparison of statistical approaches for dealing with immortal time bias in drug effectiveness studies. American Journal of Epidemiology, 184, pp.857-858.
Chen, J., Huang, Y. & Wang, P., 2016. Composite likelihood under hidden Markov model. Statistica Sinica, 26, pp.1569–1586.
Chen, J., Huang, Y. & Wang, P., 2016. Composite likelihood under hidden Markov model. Statistica Sinica, 26, pp.1569–1586.
Chen, J., 2016. Consistency of the MLE under mixture models. arXiv preprint arXiv:1607.01251.
Chen, J., Li, S. & Tan, X., 2016. Consistency of the penalized MLE for two-parameter gamma mixture models. Science China Mathematics, 59, pp.2301–2318.
Chen, J., Li, S. & Tan, X., 2016. Consistency of the penalized MLE for two-parameter gamma mixture models. Science China Mathematics, 59, pp.2301–2318.
Huggins, J., Campbell, T. & Broderick, T., 2016. Coresets for scalable Bayesian logistic regression. In Advances in Neural Information Processing Systems. Advances in Neural Information Processing Systems.

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