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2019
Karim, M.E. et al., 2019. Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies. Statistical Methods in Medical Research, 28, pp.323-324 .
Chen, J. & Feng, Z., 2019. A discussion of ‘prior-based Bayesian information criterion’. Statistical Theory and Related Fields, pp.1–3.
Wong, S.W.K. & Zidek, J.V., 2019. The duration of load effect in lumber as stochastic degradation. IEEE Transactions on Reliability, pp.410-419.
Zolaktaf, S. et al., 2019. Efficient Parameter Estimation for DNA Kinetics Modeled as Continuous-Time Markov Chains. In The 25th International Conference on DNA Computing and Molecular Programming. The 25th International Conference on DNA Computing and Molecular Programming. pp. 80–99.
Deligiannidis, G., Bouchard-Côté, A. & Doucet, A., 2019. Exponential Ergodicity of the Bouncy Particle Sampler. Annals of Statistics, 47, pp.1268–1287.
Watson, J., Zidek, J.V. & Shaddick, G., 2019. A General Theory for Preferential Sampling in Environmental Networks. Annals of Applied Statistics, p.Accepted.
Watson, J., V. Zidek, J. & Shaddick, G., 2019. A general theory for preferential sampling in environmental networks. Annals of Applied Statistics, pp.2662-2700.
Dinsdale, D.R. & Salibian-Barrera, M., 2019. Methods for preferential sampling in geostatistics. Journal of the Royal Statistical Society Series C, 68(1), p.198. Available at: https://dx.doi.org/10.1111/rssc.12286 .
Fu, E. & Heckman, N., 2019. Model-based curve registration via stochastic approximation EM algorithm. Computational Statistics and Data Analysis, 131. Available at: https://arxiv.org/abs/1712.07265.
Dinsdale, D.R. & Salibian-Barrera, M., 2019. Modelling ocean temperatures from bio-probes under preferential sampling. Annals of Applied Statistics, 13(2), pp.713-745. Available at: https://arxiv.org/abs/1901.02630.
Zhu, G. & Chen, J., 2019. Multi-parameter One-Sided Monitoring Tests. Technometrics, 60. Available at: 10.1093/biomet/asy068.
Kingwell, E. et al., 2019. Multiple sclerosis: Effect of beta-interferon treatment on survival. Brain, 142, pp.1324-1333.
Luo, H. et al., 2019. A new perspective on the benefits of the gene-environment independence in case-control studies. The Canadian Journal of Statistics, 47, pp.473–486.
Krupskii, P. & Joe, H., 2019. Nonparametric estimation of multivariate tail probabilities and tail dependence coefficients. Journal of Multivariate Analysis, 172, pp.147-161.
Chang, B. & Joe, H., 2019. Prediction based on conditional distributions of vine copulas. Computational Statistics & Data Analysis, 139, pp.45–63.
Chang, B. & Joe, H., 2019. Prediction based on conditional distributions of vine copulas. Computational Statistics & Data Analysis, 139, pp.45-63.
Cohen-Freue, G.V. et al., 2019. Robust elastic net estimators for variable selection and identification of proteomic biomarkers. Annals of Applied Statistics, 13(4), pp.2065-2090. Available at: http://dx.doi.org/10.1214/19-AOAS1269.
Cornish, R. et al., 2019. Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets. In International Conference on Machine Learning (ICML). International Conference on Machine Learning (ICML). pp. 1351–1360.
Hadley, D., Joe, H. & Nolde, N., 2019. On the selection of loss severity distributions to model operational risk. Journal of Operational Risk, 14, pp.73-94.

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