Research

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Publications by Department Members

Submitted

Campbell T, Broderick T. Automated scalable Bayesian inference via Hilbert coresets. arXiv:1710.05053. Submitted;.
Freue GVCohen, Kepplinger D, Salibian-Barrera M, Smucler E. Proteomic biomarker study using novel robust penalized elastic net estimators. Annals of Applied Statistics. Submitted.
Huggins J, Campbell T, Kasprzak M, Broderick T. Scalable Gaussian process inference with finite-data mean and variance guarantees. arXiv:1806.10234. Submitted;.
Watson J, Joy R, Tollit D, Thornton SJ, Auger-Méthé M. A general framework for estimating the spatio-temporal distribution of a species using multiple data types. Submitted.

In Press

Campbell T, Huggins J, How J, Broderick T. Truncated random measures. Bernoulli. In Press;.
Campbell T, Kulis B, How J. Dynamic clustering algorithms via small-variance analysis of Markov chain mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence. In Press;.
Högg T, Zhao Y, Gustafson P, Petkau J, Fisk J, Marrie RAnn, et al.. Adjusting for differential misclassification in matched case-control studies utilizing health administrative data. Statistics in Medicine. In Press;.
Lennox RJ, Engler-Palma C, Kowarski K, Filous A, Whitlock R, Cooke SJ, et al. Optimizing marine spatial plans with animal tracking data. Canadian Journal of Fisheries and Aquatic Sciences. In Press;.

2021

Bouchard-Côté A, Chern K, Cubranic D, Hosseini S, Hume J, Lepur M, et al.. Blang: Probabilitistic Programming for Combinatorial Spaces. Journal of Statistical Software. 2021; (Accepted).
Chen J, Li P, Liu Y, Zidek JV. Composite empirical likelihood for multisample clustered data. J Nonparametric Statistics. 2021;: Accepted Apr 2021.
Chen J, Li P, Liu Y, Zidek JV. Permutation tests under a rotating sampling plan with clustered data. Journal of nonparametric statistics. 2021; 33: 60-81.
Ju X, Salibian-Barrera M. Robust Boosting for Regression Problems. Computational Statistics and Data Science [Internet]. 2021; 153. DOI: 10.1016/j.csda.2020.107065 URL: https://arxiv.org/abs/2002.02054 Software: https://github.com/xmengju/RRBoost
Martínez A, Salibian-Barrera M. RBF: An R package to compute a robust backfitting estimator for additive models. The Journal of Open Source Software. 2021; 6(60). DOI: 10.21105/joss.02992 Software: https://github.com/msalibian/RBF
Pan S, Fan S, Wong S, Zidek JV, Rhodin H. Ellipse Detection and Localization with Application to Knots in Sawn Lumber I. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). 2021.
Syed S, Romaniello V, Campbell T, Bouchard-Côté A. Parallel Tempering on Optimized Paths. In International Conference on Machine Learning (ICML). 2021.
Syed S, Bouchard-Côté A, Deligiannidis G, Doucet A. Non-Reversible Parallel Tempering: a Scalable Highly Parallel MCMC Scheme. Journal of Royal Statistical Society, Series B. 2021; (Accepted).
Wang Y, Le ND, Zidek JV. Approximately Optimal Subset Selection for Statistical Design and Modelling. Journal of Statistical Computation and Simulation. 2021;: 1-13.
Zhao T, Bouchard-Côté A. Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler. Journal of Machine Learning Research. 2021; 22: 1–41.

2020

Boente G, Salibian-Barrera M, Vena P. Robust estimation for semi-functional linear regression models. Computational Statistics and Data Science [Internet]. 2020; 152. DOI: 10.1016/j.csda.2020.107041 URL: https://arxiv.org/abs/2006.16156 Software: https://github.com/msalibian/RobustFPLM
Cooke RM, Joe H, Chang B. Vine copula regression for observational studies. ASTA-Advances in Statistical Analysis. 2020; 104: 141-167. DOI: 10.1007/s10182-019-00353-5
Deligiannidis G, Paulin D, Bouchard-Côté A, Doucet A. Randomized Hamiltonian Monte Carlo as Scaling Limit of the Bouncy Particle Sampler and Dimension-Free Convergence Rates. Annals of Applied Probability. 2020; (Accepted).
Karim ME, Tremlett H, Zhu F, Petkau J, Kingwell E. 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. 2020; 189: To appear.
Krupskii P, Joe H. Flexible copula models with dynamic dependence and application to financial data. Econometrics and Statistics. 2020; 16: 148-167. DOI: 10.1016/j.ecosta.2020.01.005
Lee TYoon, Zidek JV, Heckman N. Dimensional Analysis in Statistical Modelling. Statistical Science. 2020;: Submitted.
Lee TYoon, Zidek JV. Scientific versus statistical modelling: a unifying approach. arXiv preprint arXiv:2002.11259. 2020 .
Pan S, Fan S, Wong SWK, Zidek JV, Rhodin H. Ellipse Detection and Localization with Applications to Knots in Sawn Lumber Images. arXiv preprint arXiv:2011.04844. 2020 .
Sadatsafavi M, McCormack J, Lee TY, Petkau J, Lynd L, Sin D. Should the number of acute exacerbations in the previous year be used to guide treatments in COPD? . European Journal of Epidemiology. 2020; 35: To appear.
Thomas ML, Shaddick G, Simpson D, de Hoogh K, Zidek JV. Spatio-temporal downscaling for continental scale estimation of air pollution concentrations. Journal of the Royal Statistical Society: Series C. 2020;: Submitted.
Wang Y, Le ND, Zidek JV. Approximately Optimal Spatial Design: How Good is it?. Spatial Statistics. 2020;: To appear.
Zhu P, Bouchard-Côté A, Campbell T. Slice Sampling for General Completely Random Measures. In Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). 2020. pp. 699–708.

2019

Chang B, Pan S, Joe H. Vine Copula Structure Learning via Monte Carlo Tree Search. In International Conference on Artificial Intelligence and Statistics. 2019.
Chang B, Chen M, Haber E, Chi EH. AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks. In International Conference on Learning Representations [Internet]. 2019. URL: https://openreview.net/forum?id=ryxepo0cFX
Chang B, Joe H. Prediction based on conditional distributions of vine copulas. Computational Statistics & Data Analysis. 2019; 139: 45-63. DOI: 10.1016/j.csda.2019.04.015
Chang B, Joe H. Prediction based on conditional distributions of vine copulas. Computational Statistics & Data Analysis. 2019; 139: 45–63.
Chang B, Pan S, Joe H. Vine copula structure learning via Monte Carlo tree search. In: Chaudhuri K, Sugiyama M. 22ND International Conference on Artificial Intelligence and Statistics, Vol 89. 2019. pp. 353-361.
Chen J, Liu Y. Small area quantile estimation. International Statistical Review. 2019; 87: S219–S238.
Chen J, Feng Z. A discussion of ‘prior-based Bayesian information criterion’. Statistical Theory and Related Fields. 2019;: 1–3.
Cohen-Freue GV, Kepplinger D, Salibian-Barrera M, Smucler E. Robust elastic net estimators for variable selection and identification of proteomic biomarkers. Annals of Applied Statistics [Internet]. 2019; 13(4): 2065-2090. DOI: 10.1214/19-AOAS1269 URL: http://dx.doi.org/10.1214/19-AOAS1269 Software: https://cran.r-project.org/package=pense
Cornish R, Vanetti P, Bouchard-Côté A, Deligiannidis G, Doucet A. Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets. In International Conference on Machine Learning (ICML). 2019. pp. 1351–1360.
Deligiannidis G, Bouchard-Côté A, Doucet A. Exponential Ergodicity of the Bouncy Particle Sampler. Annals of Statistics. 2019; 47: 1268–1287.
Dinsdale DR, Salibian-Barrera M. Modelling ocean temperatures from bio-probes under preferential sampling. Annals of Applied Statistics [Internet]. 2019; 13(2): 713-745. DOI: 10.1214/18-AOAS1217 URL: https://arxiv.org/abs/1901.02630 Software: https://github.com/msalibian/PreferentialMovement
Dinsdale DR, Salibian-Barrera M. Methods for preferential sampling in geostatistics. Journal of the Royal Statistical Society Series C [Internet]. 2019; 68(1): 198. DOI: 10.1111/rssc.12286 URL: https://dx.doi.org/10.1111/rssc.12286 Software: https://github.com/msalibian/PreferentialSampling
Fernandez-Fontelo A, Cabana A, Joe H, Puig P, Morina D. Untangling serially dependent underreported count data for gender-based violence. Statistics in Medicine. 2019; 38: 4404-4422. DOI: 10.1002/sim.8306, Early Access Date = JUL 2019
Fu E, Heckman N. Model-based curve registration via stochastic approximation EM algorithm. Computational Statistics and Data Analysis [Internet]. 2019; 131. URL: https://arxiv.org/abs/1712.07265

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