Research

Subscribe to email list

Please select the email list(s) to which you wish to subscribe.

User menu

You are here

Publications by Department Members

2019

Zolaktaf S, Dannenberg F, Winfree E, Bouchard-Côté A, Schmidt M, Condon A. Efficient Parameter Estimation for DNA Kinetics Modeled as Continuous-Time Markov Chains. In The 25th International Conference on DNA Computing and Molecular Programming. 2019. pp. 80–99.

2018

Ardiel EL, McDiarmid TA, Timbers TA, Lee KCY, Safaei J, Pelech SL, et al. Insights into the roles of CMK-1 and OGT-1 in interstimulus interval-dependent habituation in Caenorhabditis elegans. Proceedings of the Royal Society B. 2018; 285: 20182084.
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.
Bouchard-Côté A, Vollmer SJ, Doucet A. The Bouncy Particle Sampler: A non-reversible rejection-free Markov chain Monte Carlo method. Journal of the American Statistical Association. 2018; 113: 855–867.
Cai S, Chen J. Empirical likelihood inference for multiple censored samples. The Canadian Journal of Statistics. 2018; 46: 232.
Cai S, Chen J. Empirical likelihood inference for multiple censored samples. Canadian Journal of Statistics. 2018; 46: 212–232.
Campbell T, Cai D, Broderick T. Exchangeable trait allocations. Electronic Journal of Statistics. 2018; 12: 2290–2322.
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. URL: 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;. DOI: https://doi.org/10.1111/rssc.12286
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.
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: 1–10.
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;.
Homrighausen D, McDonald DJ. A study on tuning parameter selection for the high-dimensional lasso. Journal of Statistical Computation and Simulation [Internet]. 2018; 88: 2865–2892. URL: http://dx.doi.org/10.1080/00949655.2018.1491575
Joe H. Parsimonious graphical dependence models constructed from vines. Canadian Journal of Statistics. 2018; 46: 532-555. DOI: 10.1002/cjs.11481
Joe H. Dependence properties of conditional distributions of some copula models. Methodology and Computing in Applied Probability. 2018; 20: 975-1001. DOI: 10.1007/s11009-017-9544-9 ISSN = 1387-5841
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. DOI: 10.1177/0962280216668554 URL: http://journals.sagepub.com/doi/abs/10.1177/0962280216668554
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.
Kepplinger D, Freue GVCohen. Improving the Robust Estimation of the Residual Scale in High Dimensional Regression Problems with Refitted Cross-Validation using an Elastic Net S-Estimator. In 46th Annual Meeting of the Statistical Society of Canada. Montreal, Canada; 2018.
Kondo Y, Zidek JV, Taylor CG, van Eeden C. Bayesian subset selection procedures with an application to lumber strength properties. Sankhya Ser A. 2018;: Accepted Aug 08, 2018.
Kondo Y, Zidek JV, Taylor CG, van Eeden C. Subset selection procedures with an application to lumber strength properties. Sanhkya Ser B. 2018; 80: 146-172.
Krupskii P, Genton M. Linear Factor Copula Models and Their Properties. Scandinavian Journal of Statistics. 2018; to appear.
Krupskii P, Huser R, Genton M. Factor copula models for replicated spatial data. Journal of the American Statistical Association,. 2018; to appear.
Krupskii P, Joe H, Lee D, Genton M. Extreme value limit of the convolution of exponential and multivariate normal distributions: Link to the Husler- Reiss distribution. Journal of Multivariate Analysis. 2018; 163: 80-95.
Lee D, Joe H, Krupskii P. Tail-weighted dependence measures with limit being tail dependence coefficient. Journal of Nonparametric Statistics. 2018; to appear.
Lee D, Joe H, Krupskii P. Tail-weighted dependence measures with limit being the tail dependence coefficient. Journal of Nonparametric Statistics. 2018; 30: 262-290. DOI: 10.1080/10485252.2017.1407414
Liu Y, Salibian-Barrera M, Zamar R, Zidek JV. Using artificial censoring to improve extreme tail quantile estimates. Applied Statistics. 2018;: Accepted Dec 4, 2017.
Liu Y, Salibian-Barrera M, Zamar RH, Zidek JV. Using Artificial Censoring to Improve Extreme Tail Quantile Estimates. Journal of the Royal Statistical Society Series C [Internet]. 2018; 67(4): 791-812. DOI: 10.1111/rssc.12262 URL: https://doi.org/10.1111/rssc.12262
Maronna RA, Martin DR, Yohai VJ, Salibian-Barrera M. Robust Statistics: Theory and Methods (with R). 2nd ed. Wiley Series in Probability and Statistics. New York: John Wiley & Sons Ltd ; 2018. p. 464. DOI: 10.1002/9781119214656 Software: https://github.com/msalibian/RobStatTM, https://cran.r-project.org/package=RobStatTM
Nolde N, Zhang J. Conditional extremes in asymmetric financial markets. Journal of Business & Economic Statistics. 2018;.
Resende-Casquilho C, Le ND, Zidek JV. Design of Monitoring Networks using k-Determinantal Point Processes. Environmetrics. 2018; 29: Accepted Oct 14, 2017.
Shaddick G, Thomas M, Jobling A, Brauer M, van Donkelaar A, Burnett R, et al. Data Integration Model for Air Quality: A HierarchicalApproach to the Global Estimation of Exposures to Ambient Air Pollution. Applied Statistics. 2018; 67: 231-253.
Shupler M, Godwin W, Frostad J, Gustafson P, Arku RE, Brauer M. Global estimation of exposure to fine particulate matter (PM2. 5) from household air pollution. Environment international. Elsevier; 2018; 120: 354–363.
Wong S, Zidek JV. Dimensional and statistical foundations for accumulated damage models. Wood Science and Technology. 2018; 52: 45-65.
Xia M, Gustafson P. Bayesian inference for unidirectional misclassification of a binary response trait. Statistics in medicine. Wiley Online Library; 2018; 37: 933–947.

Pages