Research

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Publications by Department Members

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
Dean CB, Heckman N, Reid N. Practical Suggestions for Developing as an Academic Leader. In Leadership and Women in Statistics. Chapman and Hall; 2014.
Evans C, Zhu F, Kingwell E, Shirani A, van der Kop ML, Petkau J, et al. Association between beta-interferon exposure and hospital events in multiple sclerosis. Pharmacoepidemiology and Drug Safety. 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY-BLACKWELL; 2014; 23: 1213-1222. DOI: 10.1002/pds.3667
Evans C, Zhu F, Kingwell E, Shirani A, Kop ML, Petkau J, et al. Association between beta-interferon exposure and hospital events in multiple sclerosis. Pharmacoepidemiology and drug safety. Wiley Online Library; 2014; 23: 1213–1222. DOI: 10.1002/pds.3667
Falasinnu T, Gilbert M, Gustafson P, Shoveller J. Deriving and validating a risk estimation tool for screening asymptomatic chlamydia and gonorrhea. Sexually transmitted diseases. LWW; 2014; 41: 706–712. DOI: 10.1097/olq.0000000000000205
Falasinnu T, Gilbert M, Salway THottes, Gustafson P, Ogilvie G, Shoveller J. Predictors identifying those at increased risk for STDs: a theory-guided review of empirical literature and clinical guidelines. International journal of STD & AIDS. SAGE Publications; 2014;: 0956462414555930. DOI: 10.1177/0956462414555930
Falasinnu T, Gustafson P, Hottes TSalway, Gilbert M, Ogilvie G, Shoveller J. A critical appraisal of risk models for predicting sexually transmitted infections. Sexually transmitted diseases. LWW; 2014; 41: 321–330. DOI: 10.1097/olq.0000000000000120
Guan L, Collet JP, Yuskiv N, Skippen P, Brant R, Kissoon N. The effect of massage therapy on autonomic activity in critically ill children. Evid Based Complement Alternat Med. 2014; 2014: 656750.
Gustafson P. Bayesian Statistical Methodology for Observational Health Sciences Data. Statistics in Action: A Canadian Outlook. CRC Press; 2014;: 163. DOI: 10.1201/b16597-11
Gustafson P, Greenland S. Misclassification. In Handbook of Epidemiology. Springer; 2014. pp. 639–658. DOI: 10.1007/978-0-387-09834-0_58
Gustafson P, McCandless L. Commentary: Priors, Parameters, and Probability: A Bayesian Perspective on Sensitivity Analysis. Epidemiology. LWW; 2014; 25: 910–912.
Guzman J, Gomez-Ramirez O, Jurencak R, Shiff NJ, Berard RA, Duffy CM, et al. What matters most for patients, parents, and clinicians in the course of juvenile idiopathic arthritis? A qualitative study. J. Rheumatol. 2014; 41: 2260–2269.
Hajiaghayi M, Kirkpatrick B, Wang L, Bouchard-Côté A. Efficient continuous-time Markov chain estimation. In International Conference on Machine Learning (ICML). 2014. pp. 638–646.
Hollander Z, Lazárová M, Lam KKY, Ignaszewski A, Oudit GY, Dyck JRB, et al.. Proteomic biomarkers of recovered heart function. European journal of heart failure. Wiley Online Library; 2014; 16: 551–559.
Hua L, Joe H, Li H. Relations between hidden regular variation and the tail order of copulas. Journal of Applied Probability. Applied Probability Trust; 2014; 51: 37-57. DOI: 10.1017/S0021900200010068
Hua L, Joe H. Strength of tail dependence based on conditional tail expectation. Journal of Multivariate Analysis. Elsevier Inc; 2014; 123: 143-159. DOI: 10.1016/j.jmva.2013.09.001
Joe H. Dependence Modeling with Copulas [Internet]. Boca Raton, FL: Chapman & Hall/CRC; 2014. URL: http://www.crcpress.com/product/isbn/9781466583221

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