Research

Research

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

Publications by Department Members

2018

Zhang A, others . Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer. Cell. 2018; (Accepted).
Zhang H, Wu L. A Mechanistic Nonlinear Model for Truncated and Mis-Measured Time-varying Covariates in Survival Models, with Applications in HIV/AIDS. Journal of the Royal Statistical Society, C. 2018;.
Zhang A, others . Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer. Cell. 2018; 173: 1755–1769.
Zhao B, Chang B, Jie Z, Sigal L. Modular Generative Adversarial Networks. In European Conference on Computer Vision. 2018.
Zhou G, Wu L. Modeling semi-continuous longitudinal data with order constraints. Statistics in Medicine. 2018;.
Zhu G, Chen J. Multi-Parameter One-Sided Monitoring Tests. Technometrics. 2018; 60: 398–407.
Zhuang W, Hu B, Chen J. Semiparametric inference for the dominance index under the density ratio model. Biometrika. 2018;.
Zidek JV, LUM CONROY. Statistical challenges in assessing the engineering properties of forest products. Annual review of statistics and its application - invitation only. 2018; 5: 237-264.

2017

Béliveau A, Goring S, Platt RW, Gustafson P. Network meta-analysis of disconnected networks: How dangerous are random baseline treatment effects?. Research synthesis methods. Wiley Online Library; 2017; 8: 465–474.
Boente G, Martínez A, Salibian-Barrera M. Robust estimators for additive models using backfitting. Journal of Nonparametric Statistics [Internet]. Taylor & Francis; 2017; 29: 744-767. DOI: 10.1080/10485252.2017.1369077 URL: https://doi.org/10.1080/10485252.2017.1369077 Software: https://github.com/msalibian/RBF
Bouchard-Côté A, Doucet A, Roth A. Particle Gibbs split-merge sampling for Bayesian inference in mixture models. Journal of Machine Learning Research. 2017; 18: 1–39.
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. 2017; (Accepted).
Burstyn I, Gustafson P, Pintos J, Lavoué J, Siemiatycki J. Correction of odds ratios in case-control studies for exposure misclassification with partial knowledge of the degree of agreement among experts who assessed exposures. Occup Environ Med. BMJ Publishing Group Ltd; 2017;: oemed–2017.
Cai S, Chen J, Zidek JV. Hypothesis testing in the presence of multiple samples under density ratio models. Statistica Sinica. 2017; 27: 716–783.
Campbell H, Gustafson P. Conditional Equivalence Testing: an alternative remedy for publication bias. arXiv preprint arXiv:1710.01771. 2017;.
Casquilho-Resende CM, Le ND, Zidek JV. Spatio-temporal modelling of temperature fields in the Pacific Northwest. Environmetrics. 2017;: Resubmitted.
Chen H, Loeppky JL, Welch WJ. Flexible Correlation Structure for Accurate Prediction and Uncertainty Quantification in Bayesian Gaussian Process Emulation of a Computer Model. SIAM/ASA Journal on Uncertainty Quantification [Internet]. 2017; 5: 598–620. DOI: https://doi.org/10.1137/15M1008774 URL: https://doi.org/10.1137/15M1008774
Chen H, Loeppky J, Welch W. Flexible Correlation Structure for Accurate Prediction and Uncertainty Quantification in Bayesian Gaussian Process Emulation of a Computer Model. SIAM/ASA Journal on Uncertainty Quantification [Internet]. 2017; 5(1): 620. URL: http://epubs.siam.org/doi/abs/10.1137/15M1008774
Chen J. Consistency of the MLE under mixture models. Statistical Science. Institute of Mathematical Statistics; 2017; 32: 47–63.
Chen J. On finite mixture models. Statistical Theory and Related Fields. Taylor & Francis; 2017; 1: 15–27.
de Jong HJI, Kingwell E, Shirani A, Cohen-Tervaert JW, Hupperts R, Zhao Y, et al. Evaluating the safety of beta-interferons in multiple sclerosis: A series of nested case-control studies. Neurology. 2017; 88: 2310-2320. DOI: 10.1212/WNL.0000000000004037
de Souza CPE, Heckman NE, Xu F. Switching nonparametric regression models for multi-curve data. Canadian Journal of Statistics [Internet]. 2017; 45: 442–460. DOI: 10.1002/cjs.11331 URL: http://dx.doi.org/10.1002/cjs.11331
Deligiannidis G, Bouchard-Côté A, Doucet A. Exponential ergodicity of the Bouncy Particle Sampler. arXiv. 2017; 1705.04579.
Ding X, Qiu Z, Chen X. Sparse transition matrix estimation for high-dimensional and locally stationary vector autoregressive models. Electronic Journal of Statistics. 2017; 11: 3871–3902.
Ding L, McDonald DJ. Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression. Bioinformatics [Internet]. 2017; 33: i350–i358. URL: http://dx.doi.org/10.1093/bioinformatics/btx265
Högg T, Petkau J, Zhao Y, Gustafson P, Wijnands JMA, Tremlett H. Bayesian analysis of pair-matched case-control studies subject to outcome misclassification. Statistics in medicine. Wiley Online Library; 2017; 36: 4196–4213.
Högg T, Petkau J, Zhao Y, Gustafson P, Wijnands JMA, Tremlett H. Bayesian analysis of pair-matched case-control studies subject to outcome misclassification. Statistics in Medicine. 2017; 36(26). DOI: 10.1002/sim.7427
Högg T, Petkau J, Zhao Y, Gustafson P, Wijnands JMA, Tremlett H. Bayesian analysis of pair-matched case-control studies subject to outcome misclassification. Statistics in Medicine. 2017; 36: 4196-4213. DOI: 10.1002/sim.7427
Homrighausen D, McDonald DJ. Risk consistency of cross-validation for lasso-type procedures. Statistica Sinica [Internet]. 2017; 27: 1017–1036. URL: http://dx.doi.org/10.5705/ss.202015.0355
Hua L, Joe H. Multivariate dependence modeling based on comonotonic factors. Journal of Multivariate Analysis. 2017; 155: 317-333. DOI: 10.1016/j.jmva.2017.01.008
Islam N, Krajden M, Shoveller J, Gustafson P, Gilbert M, Buxton JA, et al. Incidence, risk factors, and prevention of hepatitis C reinfection: a population-based cohort study. The Lancet Gastroenterology & Hepatology. Elsevier; 2017; 2: 200–210.
Islam N, Krajden M, Gilbert M, Gustafson P, Yu A, Kuo M, et al. Role of primary T-cell immunodeficiency and hepatitis B coinfection on spontaneous clearance of hepatitis C: The BC Hepatitis Testers Cohort. Journal of viral hepatitis. Wiley Online Library; 2017; 24: 421–429.
Islam N, Krajden M, Shoveller J, Gustafson P, Gilbert M, Wong J, et al. Hepatitis C cross-genotype immunity and implications for vaccine development. Scientific reports. Nature Publishing Group; 2017; 7: 12326.
Joe H. Parametric copula families for statistical models. In: Ubeda-Flores M, de Amo-Artero E, Durante F, Fernandez-Sanchez J. Copulas and Dependence Models with Applications: Contributions in Honor of Roger B. Nelsen [Internet]. Berlin: Springer; 2017. pp. 119–134. URL: https://link.springer.com/book/10.1007/978-3-319-64221-5
Jun S-H, Wong SWK, Zidek JV, Bouchard-Côté A. Sequential Graph Matching with Sequential Monte Carlo. In AISTATS. 2017. pp. 1075–1084.
Jun S-H, Wong SWK, Zidek JV, Bouchard-Côté A. Sequential decision model for inference and prediction on non-uniform hypergraphs with application to knot matching from computational forestry. arXiv. 2017; 1708.07592.
Jun S-H, Bouchare-Cote A. Sequential graph matching with sequential monte carlo. Wonlg SWK. 20th International Conference on Artificial Intelligence and Statistics. Fort Lauderdale, Florida: AISTATS; 2017.
Karim MEhsanul, Petkau J, Gustafson P, Tremlett H, Group TBeams Stud. On the application of statistical learning approaches to construct inverse probability weights in marginal structural cox models: hedging against weight-model misspecification. Communications in Statistics-Simulation and Computation. Taylor & Francis; 2017; 46: 7668–7697.
Karim ME, Petkau J, Gustafson P, Tremlett H, Group BAMSStudy. On the application of statistical learning approaches to construct inverse probability weights in marginal structural Cox models: Hedging against weight-model misspecification. Communications in Statistics - Simulation and Computation. 2017; 46: 7668-7697. DOI: doi.org/10.1080/03610918.2016.1248574
Kepplinger D, Takhar M, Sasaki M, Hollander Z, Smith D, McManus B, et al. PGCA: An algorithm to link protein groups created from MS/MS data. PLOS ONE [Internet]. Public Library of Science; 2017; 12: 1-19. DOI: 10.1371/journal.pone.0177569 URL: https://doi.org/10.1371/journal.pone.0177569
Khalili A, Chen J, . Regularization in regime-switching Gaussian autoregressive models. The Canadian Journal of Statistics. 2017; 45: 374.
Kondo Y, Zhao Y, Petkau J. Identification of treatment responders based on multiple longitudinal outcomes with applications to multiple sclerosis patients. Statistics in Medicine. 2017; 36: 1862-1883. DOI: 10.1002/sim.7230
Krupskii P. Copula-based measures of reflection and permutation asymmetry and statistical tests. Statistical Papers. 2017; 58(4): 1165-1187.
Krupskii P, Genton M. Factor copula models for data with spatio-temporal dependence. Spatial Statistics. 2017; 22(1): 180-195.
Lee W, Greenwood P, Heckman N, Wefelmeyer W. Pre-averaged kernel estimators for the drift function of a diffusion process in the presence of microstructure noise. Statistical Inference for Stochastic Processes. 2017; 20(2).
Lenzi A, de Souza CPE, Dias R, Garcia NL, Heckman NE. Analysis of Aggregated Functional Data from Mixed Populations with Application to Energy Consumption. Environmetrics. 2017; 28(2).
Leung A, Yohai V, Zamar R. Multivariate Location and Scatter Matrix Estimation Under Cellwise and Casewise Contamination. CSDA. 2017;. DOI: 10.1016/j.csda.2017.02.007
Lindsten F, Johansen AM, Naesseth CA, Kirkpatrick B, Schon TB, Aston J, et al. Divide-and-conquer with sequential Monte Carlo. Journal of Computational Statistics and Graphics. 2017; 26: 445–458.
Lindsten F, Johansen AM, Naesseth CA, Kirkpatrick B, Schon TB, Aston J, et al. Divide-and-conquer with sequential Monte Carlo. Journal of Computational Statistics and Graphics. 2017; 26: 445–458.
McCandless LC, Gustafson P. A comparison of Bayesian and Monte Carlo sensitivity analysis for unmeasured confounding. Statistics in medicine. Wiley Online Library; 2017; 36: 2887–2901.

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