Research

Research

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  1. Department of Statistics
  2. Research
  3. Publications by Department Members

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

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;.
Campbell T, Huggins J, How J, Broderick T. Truncated random measures. Bernoulli. 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

Ju X, Salibian-Barrera M. Robust Boosting for Regression Problems. Computational Statistics and Data Science [Internet]. 2021;153. https://arxiv.org/abs/2002.02054

2020

Boente G, Salibian-Barrera M, Vena P. Robust estimation for semi-functional linear regression models. Computational Statistics and Data Science [Internet]. 2020;152. https://arxiv.org/abs/2006.16156
Wang Y, Le ND, Zidek JV. Approximately Optimal Spatial Design: How Good is it?. Spatial Statistics. 2020;:To appear.

2019

al. KRCampbell. clonealign: statistical integration of independent single-cell RNA & DNA-seq from human cancers. Genome Biology. 2019;(Accepted).
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 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. https://openreview.net/forum?id=ryxepo0cFX
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. http://dx.doi.org/10.1214/19-AOAS1269
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. Methods for preferential sampling in geostatistics. Journal of the Royal Statistical Society Series C [Internet]. 2019;68(1):198. https://dx.doi.org/10.1111/rssc.12286
Dinsdale DR, Salibian-Barrera M. Modelling ocean temperatures from bio-probes under preferential sampling. Annals of Applied Statistics [Internet]. 2019;13(2):713-745. https://arxiv.org/abs/1901.02630
Fu E, Heckman N. Model-based curve registration via stochastic approximation EM algorithm. Computational Statistics and Data Analysis [Internet]. 2019;131. https://arxiv.org/abs/1712.07265
Jun S-H, Wong SWK, Zidek JV, Bourchard-Cote A. Sequential decision model for inference and prediction on non-uniform hypergraphs with application to knot matching from computational forestry. Annals of Applied Statistics. 2019;13:1678-1707 .
Surjanovic S, Welch WJ. Adaptive Partitioning Design and Analysis for Emulation of a Complex Computer Code. arXiv preprint arXiv:1907.01181. 2019;.
Wang L, Wang S, Bouchard-Côté A. An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics. Systematic Biology. 2019;(Accepted).
Wang Y, Le ND, Zidek JV. Determinental point processes stochastic approximation for combinatorial optimization. Optimization Letters. 2019;.
Watson J, Zidek JV, Shaddick G. A General Theory for Preferential Sampling in Environmental Networks. Annals of Applied Statistics. 2019;:Accepted.
Watson J, V. Zidek J, Shaddick G. A general theory for preferential sampling in environmental networks. Annals of Applied Statistics. 2019;:2662-2700.
Wong SWK, Zidek JV. The duration of load effect in lumber as stochastic degradation. IEEE Transactions on Reliability. 2019;:410-419.
Yang C-H, Zidek JV, Wong SWK. Bayesian analysis of accumulated damage models in lumber reliability. Technometrics. 2019;61:1-14.

2018

Bierkens J, Bouchard-Côté A, Doucet A, Duncan AB, Fearnhead P, Lienart T, et al.. Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains. Statistics and Probability Letters. 2018;136:148–154.
Cai S, Chen J. Empirical likelihood inference for multiple censored samples. The Canadian Journal of Statistics. 2018;46:232.
Campbell K, Bouchard-Côté A, Shah S. Probabilistic inference of clonal gene expression through integration of RNA & DNA-seq at single-cell resolution. In HitSeq. 2018.
Campbell T, Cai D, Broderick T. Exchangeable trait allocations. Electronic Journal of Statistics. 2018;12:2290–2322.
Campbell H, Gustafson P. The Validity and Efficiency of Hypothesis Testing in Observational Studies with Time-Varying Exposures. Observational Studies [Internet]. 2018;. https://obsstudies.org/validity-efficiency-hypothesis-testing-observational-studies-time-varying-exposures/
Campbell T, Broderick T. Bayesian coreset construction via greedy iterative geodesic ascent. In International Conference on Machine Learning. 2018.
Campbell H, Gustafson P. Conditional equivalence testing: An alternative remedy for publication bias. PloS one. Public Library of Science; 2018;13:e0195145.
Chang B, Meng L, Haber E, Ruthotto L, Begert D, Holtham E. Reversible Architectures for Arbitrarily Deep Residual Neural Networks. In AAAI Conference on Artificial Intelligence. 2018.
Chang B, Zhang Q, Pan S, Meng L. Generating Handwritten Chinese Characters Using CycleGAN. In IEEE Winter Conference on Applications of Computer Vision. 2018.
Chang B, Meng L, Haber E, Tung F, Begert D. Multi-level Residual Networks from Dynamical Systems View. In International Conference on Learning Representations [Internet]. 2018. https://openreview.net/forum?id=SyJS-OgR-
Dinsdale DR, Salibian-Barrera M. Methods for preferential sampling in geostatistics. Journal of the Royal Statistical Society Series C. 2018;.
Dorri F, Jewell S, Bouchard-Côté A, Shah S. MuClone: somatic mutation detection and classification through probabilistic integration of clonal population information. Communications Biology . 2018;2.
Fernandez M, Ban F, Woo G, Hsing M, Yamazaki T, LeBlanc E, et al. Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images. Journal of Chemical Information and Modeling. 2018;(in press).
Gomulkiewicz R, Kingsolver J, Carter P, Heckman N. Variation and evolution of function-valued traits. Annual Review of Ecology, Evolution, and Systematics. 2018;49(1).
Gustafson P, McCandless LC. When Is a Sensitivity Parameter Exactly That?. Statistical Science. Institute of Mathematical Statistics; 2018;33:86–95.
Högg T, Wijnands J, Kingwell E, Zhu F, Lu X, Evans C, et al.. Mining healthcare data for markers of the multiple sclerosis prodrome. Multiple Sclerosis and Related Disorders. 2018;.
Karim MEhsanul, Petkau J, Gustafson P, Platt RW, Tremlett H, Group BAMSStudy. Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies. Statistical methods in medical research. SAGE Publications Sage UK: London, England; 2018;27:1709–1722.
Karim ME, Petkau J, Gustafson P, Platt RW, Tremlett H, Group BAMSStudy. Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies. Statistical Methods in Medical Research [Internet]. 2018;27 :1709-1722. http://journals.sagepub.com/doi/abs/10.1177/0962280216668554

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