Campbell, T. & Broderick, T., Submitted. Automated scalable Bayesian inference via Hilbert coresets. arXiv:1710.05053.

Watson, J., Submitted. A fast Monte Carlo test for preferential sampling.

Watson, J. et al., Submitted. A general framework for estimating the spatio-temporal distribution of a species using multiple data types.

Freue, G.V.Cohen et al., Submitted. Proteomic biomarker study using novel robust penalized elastic net estimators. Annals of Applied Statistics.

Huggins, J. et al., Submitted. Scalable Gaussian process inference with finite-data mean and variance guarantees. arXiv:1806.10234.

Thomas, M.L. et al., Submitted. Spatio-temporal downscaling for continental scale estimation of air pollution concentrations. Journal of the Royal Statistical Society: Series C.

Högg, T. et al., In Press. Adjusting for differential misclassification in matched case-control studies utilizing health administrative data. Statistics in Medicine.

Campbell, T., Kulis, B. & How, J., In Press. Dynamic clustering algorithms via small-variance analysis of Markov chain mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence.

Zhu, G. & Chen, J., In Press. Multi-parameter One-Sided Monitoring Tests. Technometrics.

Lennox, R.J. et al., In Press. Optimizing marine spatial plans with animal tracking data. Canadian Journal of Fisheries and Aquatic Sciences.

Campbell, T. et al., In Press. Truncated random measures. Bernoulli.

Ju, X. & Salibian-Barrera, M., 2021. Robust Boosting for Regression Problems. Computational Statistics and Data Science, 153. Available at: https://arxiv.org/abs/2002.02054.

Wang, Y., Le, N.D. & Zidek, J.V., 2020. Approximately Optimal Spatial Design: How Good is it?. Spatial Statistics, p.To appear.

Watson, J., 2020. CV.

Karim, M.E. et al., 2020. Dealing with treatment-confounder feedback and sparse follow-up in longitudinal studies - application of a marginal structural model in a multiple sclerosis cohort. . American Journal of Epidemiology, 189, p.To appear.

Boente, G., Salibian-Barrera, M. & Vena, P., 2020. Robust estimation for semi-functional linear regression models. Computational Statistics and Data Science, 152. Available at: https://arxiv.org/abs/2006.16156.

Sadatsafavi, M. et al., 2020. Should the number of acute exacerbations in the previous year be used to guide treatments in COPD? . European Journal of Epidemiology, 35, p.To appear.

Surjanovic, S. & Welch, W.J., 2019. Adaptive Partitioning Design and Analysis for Emulation of a Complex Computer Code. arXiv preprint arXiv:1907.01181.

Högg, T. et al., 2019. Adjusting for differential misclassification in matched case-control studies utilizing health administrative data. Statistics in Medicine, 38, pp.3669-3681.

Wang, L., Wang, S. & Bouchard-Côté, A., 2019. An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics. Systematic Biology, (Accepted).

Chang, B. et al., 2019. AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks. In International Conference on Learning Representations. International Conference on Learning Representations. Available at: https://openreview.net/forum?id=ryxepo0cFX.

Yang, C.-H., Zidek, J.V. & Wong, S.W.K., 2019. Bayesian analysis of accumulated damage models in lumber reliability. Technometrics, 61, pp.1-14.

al., K.R.Campbell, 2019. clonealign: statistical integration of independent single-cell RNA & DNA-seq from human cancers. Genome Biology, (Accepted).

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 .

Wang, Y., Le, N.D. & Zidek, J.V., 2019. Determinental point processes stochastic approximation for combinatorial optimization. Optimization Letters.