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.            
  • Watson J.   A fast Monte Carlo test for preferential sampling   . 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.            

2023

2022

  • Chen J, Liu Y, Taylor CG, Zidek JV.   Permutation tests under a rotating sampling plan with clustered data   Ann. Appl. Statist.. 2022   16:   936-958.   DOI: 10.1214/21-AOAS1526      
  • Dean CB, El-Shaarawi AH, Esterby SR, Flemming JMills, Routledge RD, Taylor SW, et al.   Canadian contributions to environmetrics   Canadian Journal of Statistics [Internet]. 2022   n/a:     DOI: https://doi.org/10.1002/cjs.11743   URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/cjs.11743    
  • Ding L, Zentner GE, McDonald DJ.   Sufficient principal component regression for genomics   Bioinformatics Advances [Internet]. 2022   2:   vbac033.     URL: https://doi.org/10.1093/bioadv/vbac033    
  • Fan S, Wong SW;K, Zidek JV.   Knots and their effect on the tensile strength of lumber   Journal of Quality Technology. 2022     Submitted.        
  • He M, Chen J.   Consistency of the MLE under a two-parameter gamma mixture model with a structural shape parameter   Metrika [Internet]. 2022   ??:       URL: https://doi.org/10.1007/s00184-021-00856-9    
  • He M, Chen J.   Consistency of the MLE under two-parameter mixture models with a structural scale parameter   Advances in Data Analysis and Classification [Internet]. 2022   16:   125-154.     URL: http://doi.org/10.1007/s11634-021-00472-5    
  • Isberg S, Welch WJ.   Adaptive Design and Analysis Via Partitioning Trees for Emulation of a Complex Computer Code   Journal of Computational and Graphical Statistics [Internet]. 2022     1-12.   DOI: 10.1080/10618600.2022.2039160   URL: https://doi.org/10.1080/10618600.2022.2039160    
  • Lee TYoon, Zidek JV, Heckman N.   Nondimensionalizing physical and statistical models: a unified approach   Electronic Journal of Statistics. 2022     Submitted.        
  • Zhang AGong, Chen Jand.   Density ratio model with data-adaptive basis function   Journal of Multivariate Analysis. 2022   191:          
  • Zhang Q, Chen J.   Distributed learning of finite Gaussian mixtures   Journal of Machine Learning Research [Internet]. 2022   23:   1-40.     URL: \urlhttp://jmlr.org/papers/v23/21-0093.html    

2021

  • Boente G, Salibian-Barrera M.   Robust functional principal components for sparse longitudinal data   Metron. 2021       DOI: 10.1007/s40300-020-00193-3     Preprint: https://arxiv.org/abs/2012.01540   Software: https://github.com/msalibian/sparseFPCA
  • Boix CA, James BT, Park YP, Meuleman W, Kellis M.   Regulatory genomic circuitry of human disease loci by integrative epigenomics   Nature. 2021   590:   300–307.        
  • 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.   Permutation tests under a rotating sampling plan with clustered data   Journal of nonparametric statistics. 2021   33:   60-81.        
  • 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.   Composite empirical likelihood for multisample clustered data   Journal of Nonparametric Statistics. 2021   33:   60–81.        
  • Chen J, Li P, Qin J, Yu T.   Test for homogeneity with unordered paired observations   Electronic Journal of Statistics. 2021   15:   1661–1694.        
  • Chen J, Li P, Yukun L, Zidek J.   Monitoring test under nonparametric random effects model   Journal of Nonparametric Statistics [Internet]. 2021   33:   60-81.     URL: https://doi.org/10.1080/10485252.2021.1914337    
  • He L, Loika Y, Park Y, consortium GTissueExp, Bennett DA, Kellis M, et al.   Exome-wide age-of-onset analysis reveals exonic variants in ERN1 and SPPL2C associated with Alzheimer's disease   Transl. Psychiatry. 2021   11:   146.        
  • 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:     DOI: 10.21105/joss.02992       Software: https://github.com/msalibian/RBF
  • McDonald DJ, Bien J, Green A, Hu AJ, DeFries N, Hyun S, et al.   Can Auxiliary Indicators Improve COVID-19 Forecasting and Hotspot Prediction?   Proceedings of the National Academy of Sciences [Internet]. 2021   118:   e2111453118.     URL: https://doi.org/10.1073/pnas.2111453118    
  • McDonald DJ, McBride M, Gu Y, Raphael C.   Markov-switching State Space Models for Uncovering Musical Interpretation   Annals of Applied Statistics [Internet]. 2021   15:   1147–1170.     URL: https://doi.org/10.1214/21-AOAS1457    
  • 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            
  • Park Y, He L, Davila-Velderrain J, Hou L, Mohammadi S, Mathys H, et al.   Single-cell deconvolution of 3,000 post-mortem brain samples for eQTL and GWAS dissection in mental disorders   Cold Spring Harbor Laboratory. 2021     2021.01.21.426000.        
  • Park Y, Kellis M.   Counterfactual inference for single-cell gene expression analysis   medRxiv. 2021            
  • Park YP, Kellis M.   CoCoA-diff: counterfactual inference for single-cell gene expression analysis   Genome Biol.. 2021   22:   1–23.        
  • Policastro RA, McDonald DJ, Brendel VP, Zentner GE.   Flexible analysis of TSS mapping data and detection of TSS shifts with TSRexploreR   NAR Genomics and Bioinformatics [Internet]. 2021   3:   1–10.     URL: https://doi.org/10.1093/nargab/lqab051    
  • Reinhart A, Brooks L, Jahja M, Rumack A, Tang J, Saeed WAl, et al.   An Open Repository of Real-Time COVID-19 Indicators   Proceedings of the National Academy of Sciences [Internet]. 2021   118:   e2111452118.     URL: https://doi.org/10.1073/pnas.2111452118    
  • Salehi S, others .   Clonal fitness inferred from time-series modelling of single-cell cancer genomes   Nature. 2021   (Accepted):          
  • Sidrow E, Heckman N, Fortune SME, Trites A, Murphy I, Auger-Méthé M.   Modelling multi-scale, state-switching functional data with hidden Markov models   Canadian Journal of Statistics. 2021   50:          
  • 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):          
  • Syed S, Romaniello V, Campbell T, Bouchard-Côté A.   Parallel Tempering on Optimized Paths   International Conference on Machine Learning (ICML). 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.        
  • Xiong X, Hou L, Park YP, Molinie B, Consortium G, Gregory RI, et al.   Genetic drivers of m6A methylation in human brain, lung, heart and muscle   Nat. Genet.. 2021            
  • Zhang AGong, Chen J.   Empirical likelihood ratio test on quantiles under a density ratio model   Electronic Journal of Statistics [Internet]. 2021   15:   6191-6227.     URL: https://doi.org/10.1214/21-EJS1943    
  • Zhang Q, Chen J.   Minimum Wasserstein Distance Estimator under Finite Location-scale Mixtures   arXiv preprint arXiv:2107.01323. 2021            
  • 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