Export 1675 results:
2017
Ye, Q. & Wu, L., 2017. Two-Step and Likelihood Methods for Joint Models of Longitudinal and Survival Data. Communication in Statistics, 46(8).
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.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.
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.
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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