Research

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Publications by Department Members

2015

Liu Y, Battaile BC, Trites AW, Zidek JV. Bias correction and uncertainty characterization of Dead-Reckoned paths of marine mammals. Animal Biotelemetry. BioMed Central; 2015; 3: 1.
Liu Y, Shaddick G, Zidek JV. incorporating high-dimensional exposure modelling into studies of air pollution and health. Statistics in Biosciences. 2015;: Accepted.
Liu Y, Dinsdale DR, Jun S-H, Briercliffe C, Bone J. Automatic Learning of Basketball Strategy via SportVU Tracking Data: the Potential Field Approach. Submitted to Sloan Sports Analytics Conference. 2015;.
Liu J, Liu W, Wu L, Yang G. A flexible approach for multivariate mixed-effects models in the presence of non-ignorable missingness and measurement error. Journal of Statistical Computation and Simulation. 2015;.
Marchant EA, Kan B, Sharma AA, van Zanten A, Kollmann TR, Brant R, et al. Attenuated innate immune defenses in very premature neonates during the neonatal period. Pediatr. Res. 2015; 78: 492–497.
McPherson A, Roth A, McAlpine J, Bouchard-Côté A, Shah SP. The Importance of Mutation Loss in Modelling Evolution and Metastasis in Genomically Unstable Cancers. In HitSeq. 2015.
Nikoloulopoulos AK, Joe H. Factor copula models for item response data. Psychometrika. Springer; 2015; 80: 126-150. DOI: 10.1007/s11336-013-9387-4
Nolde N, Jakob M. Challenging the standard dike freeboard: Methods to quantify statistical uncertainties in river flood protection. Canadian Water Resources Journal/Revue canadienne des ressources hydriques. Taylor & Francis; 2015;: 1–10.
Pierson E, Koller D, Battle A, Mostafavi S, Consortium GTE. Sharing and Specificity of Co-expression Networks across 35 Human Tissues. PLOS COMPUTATIONAL BIOLOGY. 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA: PUBLIC LIBRARY SCIENCE; 2015; 11: e1004220. DOI: 10.1371/journal.pcbi.1004220
Reich DS, White R, Cortese ICM, Vuolo L, Shea CD, Collins TL, et al. Sample-size calculations for short-term proof-of-concept studies of tissue protection and repair in multiple sclerosis lesions via conventional clinical imaging. Multiple Sclerosis Journal. 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND: SAGE PUBLICATIONS LTD; 2015; 21: 1693-1704. DOI: 10.1177/1352458515569098
Roth A, McPherson A, Bouchard-Côté A, Shah S. Inference of clonal genotypes from single cell sequencing data. In HitSeq. 2015.
Shaddick G, Zidek JV. Spatio-Temporal Methods in Environmental Epidemiology. CRC Press; 2015.
Shirani A, Zhao Y, Petkau J, Gustafson P, Karim ME, Evans C, et al. Multiple sclerosis in older adults: the clinical profile and impact of interferon beta treatment. BioMed Research International. 410 PARK AVENUE, 15TH FLOOR, \#287 PMB, NEW YORK, NY 10022 USA: HINDAWI PUBLISHING CORPORATION; 2015; 2015: ID451912, 11 pages. DOI: 10.1155/2015/451912
Shirani A, Zhao Y, Petkau J, Gustafson P, Karim MEhsanul, Evans C, et al. Multiple Sclerosis in Older Adults: The Clinical Profile and Impact of Interferon Beta Treatment. BioMed research international. Hindawi Publishing Corporation; 2015; 2015. DOI: 10.1155/2015/451912
Straub J, Campbell T, How JP, Fisher J. Small-variance nonparametric clustering on the hypersphere. In IEEE Conference on Computer Vision and Pattern Recognition. 2015.
Tomal JH, Welch WJ, Zamar RH. ENSEMBLING CLASSIFICATION MODELS BASED ON PHALANXES OF VARIABLES WITH APPLICATIONS IN DRUG DISCOVERY. ANNALS OF APPLIED STATISTICS. 3163 SOMERSET DR, CLEVELAND, OH 44122 USA: INST MATHEMATICAL STATISTICS; 2015; 9: 69-93. DOI: 10.1214/14-AOAS778
Tomal JH, Welch WJ, Zamar RH, others . Ensembling classification models based on phalanxes of variables with applications in drug discovery. The Annals of Applied Statistics [Internet]. 2015; 9: 69–93. URL: http://projecteuclid.org/euclid.aoas/1430226085
Tremlett H, Dai DLY, Hollander Z, Kapanen A, Aziz T, Wilson-McManus JE, et al. Serum proteomics in multiple sclerosis disease progression. Journal of proteomics. Elsevier; 2015; 118: 2–11*Senior Author.
Troncoso P. The SAGE handbook of multilevel modeling. International Journal of Research & Method in Education [Internet]. 2015; 38: 100–101. DOI: 10.1080/1743727X.2014.986027 URL: http://dx.doi.org/10.1080/1743727X.2014.986027
Vinall J, Zwicker JG, Grunau RE, Chau V, Poskitt KJ, Brant R, et al. Early neonatal pain exposure and brain microstructure interact to predict neurodevelopmental outcomes at 18 months corrected age in children born very preterm. Int. J. Dev. Neurosci. 2015; 47: 47.
Wang L, Chen J, Pu X. Resampling calibrated adjusted empirical likelihood. Canadian Journal of Statistics. 2015; 43: 42–59.
Wang L, Bouchard-Côté A, Doucet A. Bayesian phylogenetic inference using the combinatorial sequential Monte Carlo method. Journal of the American Statistical Association. 2015; 110: 1362–1374.
Wong SWK, LUM CONROY, WU LANG, Zidek JV. Quantifying uncertainty in lumber grading and strength prediction: a Bayesian approach. Technometrics. Taylor and Francis; 2015; 58: 236-243.
Xu C, Chen J. A Thresholding Algorithm for Order Selection in Finite Mixture Models. Communications in Statistics-Simulation and Computation. Taylor & Francis; 2015; 44: 433–453.
Zhang T, Shirani A, Zhao Y, Karim ME, Gustafson P, Petkau J, et al. Beta-interferon exposure and onset of secondary progressive multiple sclerosis. European Journal of Neurology [Internet]. 2015; 22: 990–1000. DOI: 10.1111/ene.12698 URL: http://onlinelibrary.wiley.com/doi/10.1111/ene.12698/abstract
Zhang T, Shirani A, Zhao Y, Karim ME, Gustafson P, Petkau J, et al. Beta-interferon exposure and onset of secondary progressive multiple sclerosis. European Journal of Neurology. Wiley Online Library; 2015; 22: 990–1000. DOI: 10.1111/ene.12698
Zhao T, Wang Z, Cumberworth A, Gsponer J, de Freitas N, Bouchard-Côté A. Bayesian analysis of continuous time Markov chains with application to phylogenetic modelling. Bayesian Analysis. 2015; (In Press).
Zhao T, Cumberworth A, Wang Z, Gsponer J, de Freitas N, Bouchard-Côté A. Bayesian analysis of continuous time Markov chains with application to phylogenetic modelling. Bayesian Analysis. 2015; 11: 1203–1237.
Zhao Y, Kondo Y, Traboulsee A, Li DKB, Riddehough A, Petkau AJ. Personalized activity index, a new safety monitoring tool for multiple sclerosis clinical trials. Multiple Sclerosis Journal – Experimental, Translational and Clinical [Internet]. 2015; 1: 1-15. DOI: 10.1177/2055217315577829 URL: http://mso.sagepub.com/content/1/2055217315577829
Zidek JV, others . Discussion of ``Optimal design in geostatistics under preferential sampling '' Ferreira and Gamerman. Bayesian Analysis. International Society for Bayesian Analysis; 2015; 10: 749–752.
Zlosnik JE, Zhou G, Brant R, Henry DA, Hird TJ, Mahenthiralingam E, et al. Burkholderia species infections in patients with cystic fibrosis in British Columbia, Canada. 30 years' experience. Ann Am Thorac Soc. 2015; 12: 70–78.

2014

Ascherio A, Munger KL, White R, Köchert K, Simon KClaire, Polman CH, et al.. Vitamin D as an early predictor of multiple sclerosis activity and progression. JAMA neurology. American Medical Association; 2014; 71: 306–314.
Ascherio A, Munger K, White R, Köchert K, Simon KClaire, Freedman M, et al.. Vitamin D As Predictor Of Multiple Sclerosis Activity And Progression In Patients With CIS Treated Early With Interferon beta-1b (P5. 016). Neurology. AAN Enterprises; 2014; 82: P5–016.
Ayad M, Coia V, Kihel O. The Number of Relatively Prime Subsets of a Finite Union of Sets of Consecutive Integers. Journal of Integer Sequences. 2014; 17: 3.
Barr RG, Fairbrother N, Pauwels J, Green J, Chen M, Brant R. Maternal frustration, emotional and behavioural responses to prolonged infant crying. Infant Behav Dev. 2014; 37: 652–664.
Battle A, Mostafavi S, Zhu X, Potash JB, Weissman MM, McCormick C, et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. GENOME RESEARCH. 1 BUNGTOWN RD, COLD SPRING HARBOR, NY 11724 USA: COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT; 2014; 24: 14-24. DOI: 10.1101/gr.155192.113
Bonner SJ, Newlands NK, Heckman NE. Modeling regional impacts of climate teleconnections using functional data analysis. ENVIRONMENTAL AND ECOLOGICAL STATISTICS. VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS: SPRINGER; 2014; 21: 1-26. DOI: 10.1007/s10651-013-0241-8
Bouchard-Côté A. Sequential Monte Carlo (SMC) for Bayesian phylogenetics. In: Chen M-H, Kuo L, Lewis PO (eds.). Bayesian phylogenetics: methods, algorithms, and applications. 2014. pp. 163–186.
Brechmann EC, Joe H. Parsimonious parameterization of correlation matrices using truncated vines and factor analysis. Computational Statistics & Data Analysis. Elsevier Science BV; 2014; 77: 233-251. DOI: 10.1016/j.csda.2014.03.002
Cai S, Zidek JV, Newlands NK, Neilsen D. Statistical modeling and forecasting of fruit crop phenology under climate change. Environmetrics. Wiley Online Library; 2014; 25: 621–629.
Campbell H, Dean CB. The consequences of proportional hazards based model selection. Statistics in medicine. Wiley Online Library; 2014; 33: 1042–1056.
Campbell T, How JP. Approximate decentralized Bayesian inference. In Uncertainty in Artificial Intelligence. 2014.
Coia V, Huang MLing. A Sieve model for extreme values. Journal of Statistical Computation and Simulation. 2014; 84: 1692–1710.
Cubranic D, Dunham B, Kim D. On-line homework in probability and statistics: WeBWorK incorporating R. In 9th International Conference on Teaching Statistics. 2014.
de Souza CPE, Heckman NE. Switching nonparametric regression models. JOURNAL OF NONPARAMETRIC STATISTICS. 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND: TAYLOR & FRANCIS LTD; 2014; 26: 617-637. DOI: 10.1080/10485252.2014.941364

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