Publications
] , 2015. Rehabilitation therapies for older clients of the Ontario home care system: regional variation and client-level predictors of service provision. Disability and Rehabilitation, 37, pp.625–631. Available at: http://doi.org/10.3109/09638288.2014.935494.
, 2015. Opinion versus practice regarding the use of rehabilitation services in home care: an investigation using machine learning algorithms. BMC Medical Informatics and Decision Making, 15, p.80. Available at: http://doi.org/10.1186/s12911-015-0203-1.
, 2014. Residue-specific side-chain polymorphisms via particle belief propagation. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11, pp.33–41. Available at: http://doi.org/10.1109/TCBB.2013.130.
, 2013. Content-boosted matrix factorization techniques for recommender systems. Statistical Analysis and Data Mining: The ASA Data Science Journal, 6, pp.286–301. Available at: http://doi.org/10.1002/sam.11184.
, 2012. Stochastic stepwise ensembles for variable selection. Journal of Computational and Graphical Statistics, 21, pp.275–294. Available at: http://doi.org/10.1080/10618600.2012.679223.
, 2012. K-means cluster analysis of rehabilitation service users in the home health care system of Ontario: Examining the heterogeneity of a complex geriatric population. Archives of Physical Medicine and Rehabilitation, 93, pp.2198–2205. Available at: http://doi.org/10.1016/j.apmr.2012.05.026.
, 2012. On individual neutrality and collective decision making. The Mathematical Scientist, 37, pp.141–146. Available at: http://www.appliedprobability.org/data/files/TMS%20articles/37_2_8.pdf.
, 2012. Chemical descriptors are more important than learning algorithms for modelling. Molecular Informatics, 31, pp.707–710. Available at: http://doi.org/10.1002/minf.201200031.
, 2011. Variable selection by ensembles for the Cox model. Journal of Statistical Computation and Simulation, 81, pp.1983–1992. Available at: http://doi.org/10.1080/00949655.2010.511622.
, 2011. Pseudo-likelihood inference underestimates model uncertainty: Evidence from Bayesian nearest neighbours. Journal of the Iranian Statistical Society, 10, pp.167–180. Available at: http://jirss.irstat.ir/article-1-162-en.html.
, 2011. Detection of rare items with TARGET. Statistics and Its Interface, 4, pp.11–17. Available at: http://doi.org/10.4310/SII.2011.v4.n1.a2.
, 2010. Partial generalized additive models: An information-theoretic approach for dealing with concurvity and selecting variables. Journal of Computational and Graphical Statistics, 19, pp.531–551. Available at: http://doi.org/10.1198/jcgs.2010.07139.
, 2010. Letter to the editor. Journal of the American Statistical Association, 105, pp.880–881. Available at: http://doi.org/10.1198/jasa.2010.tm09295.
, 2008. LAGO on the unit sphere. Neural Networks, 21, pp.1220–1223. Available at: http://doi.org/10.1016/j.neunet.2008.08.002.
, 2008. Kernels and ensembles: Perspectives on statistical learning. The American Statistician, 62, pp.97–109. Available at: http://doi.org/10.1198/000313008X306367.
, 2007. Using machine learning algorithms to guide rehabilitation planning for home care clients. BMC Medical Informatics and Decision Making, 7, p.41. Available at: http://doi.org/10.1186/1472-6947-7-41.
, 2007. The K-nearest neighbor algorithm predicted rehabilitation potential better than current Clinical Assessment Protocol. Journal of Clinical Epidemiology, 60, pp.1015–1021. Available at: http://doi.org/10.1016/j.jclinepi.2007.06.001.
, 2006. LAGO: A computationally efficient approach for statistical detection. Technometrics, 48, pp.193–205. Available at: http://doi.org/10.1198/004017005000000643.
, 2006. A factor analysis model for functional genomics. BMC Bioinformatics, 7, p.216. Available at: http://doi.org/10.1186/1471-2105-7-216.
, 2006. Discriminant analysis with common principal components. Biometrika, 93, pp.1018–1024. Available at: http://doi.org/10.1093/biomet/93.4.1018.
