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In Press
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.
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.
Salibian-Barrera, M., 2023. Robust nonparametric regression: review and practical considerations. Econometrics and Statistics.
Ju, X. & Salibian-Barrera, M., 2023. Tree-based boosting with functional data. Computational Statistics.
Isberg, S. & Welch, W.J., 2022. Adaptive Design and Analysis Via Partitioning Trees for Emulation of a Complex Computer Code. Journal of Computational and Graphical Statistics, pp.1-12. Available at: https://doi.org/10.1080/10618600.2022.2039160.
Dean, C.B. et al., 2022. Canadian contributions to environmetrics. Canadian Journal of Statistics, n/a. Available at: https://onlinelibrary.wiley.com/doi/abs/10.1002/cjs.11743.
He, M. & Chen, J., 2022. Consistency of the MLE under two-parameter mixture models with a structural scale parameter. Advances in Data Analysis and Classification, 16, pp.125-154. Available at: http://doi.org/10.1007/s11634-021-00472-5.
Zhang, A.Gong & Chen, Jand, 2022. Density ratio model with data-adaptive basis function. Journal of Multivariate Analysis, 191.
Zhang, Q. & Chen, J., 2022. Distributed learning of finite Gaussian mixtures. Journal of Machine Learning Research, 23, pp.1-40. Available at: \urlhttp://jmlr.org/papers/v23/21-0093.html.
Cramer, E.Y. et al., 2022. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proceedings of the National Academy of Sciences, 119, p.e2113561119. Available at: https://www.pnas.org/doi/abs/10.1073/pnas.2113561119.
Fan, S., Wong, S.W.;K. & Zidek, J.V., 2022. Knots and their effect on the tensile strength of lumber. Journal of Quality Technology, p.Submitted.
Lee, T.Yoon, Zidek, J.V. & Heckman, N., 2022. Nondimensionalizing physical and statistical models: a unified approach. Electronic Journal of Statistics, p.Submitted.
Chen, J. et al., 2022. Permutation tests under a rotating sampling plan with clustered data. Ann. Appl. Statist., 16, pp.936-958.
Ding, L., Zentner, G.E. & McDonald, D.J., 2022. Sufficient principal component regression for genomics. Bioinformatics Advances, 2, p.vbac033. Available at: https://doi.org/10.1093/bioadv/vbac033.
Zhao, T. & Bouchard-Côté, A., 2021. Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler. Journal of Machine Learning Research, 22, pp.1–41.
Wang, Y., Le, N.D. & Zidek, J.V., 2021. Approximately Optimal Subset Selection for Statistical Design and Modelling. Journal of Statistical Computation and Simulation, pp.1-13.
Bouchard-Côté, A. et al., 2021. Blang: Probabilitistic Programming for Combinatorial Spaces. Journal of Statistical Software, (Accepted).