John T. Ormerod

School of Mathematics and Statistics F07
University of Sydney NSW 2006
Australia

john.ormerod@sydney.edu.au

Research Interests

Variational approximations, generalised linear mixed models, splines, data mining, semiparametric regresssion and missing data.

Software

Under consideration

Refereed Journal Articles

2018

  1. Pengyi Yang, John T Ormerod, W Liu, C Ma, AY Zomaya, JYH Yang
    AdaSampling for Positive-Unlabeled and Label Noise Learning With Bioinformatics Applications.
    IEEE Transactions on Cybernetics, 1-12 link

2017

  1. John T Ormerod, Chong You, Samuel Muller
    A variational Bayes approach to variable selection.
    Electronic Journal of Statistics , 11 (2), 3549-3594 link
  2. FKC Hui, DI Warton, JT Ormerod, V Haapaniemi, S Taskinen
    Variational approximations for generalized linear latent variable models.
    Journal of Computational and Graphical Statistics, 26 (1), 35-43 link
  3. Ellis Patrick, Sarah-Jane Schramm, John T Ormerod, Richard A Scolyer, Graham J Mann, Samuel Mueller, Jean YH Yang
    A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types.
    Oncotarget, 8 (2), 2807 link
  4. Bader Al-Anzi, Sherif Gerges, Noah Olsman, Christopher Ormerod, Georgios Piliouras, John Ormerod, Kai Zinn
    Modeling and analysis of modular structure in diverse biological networks.
    Journal of theoretical biology, 422, 18-30 link

2016

  1. Eugene Dubossarsky, Jerome H Friedman, John T Ormerod, Matthew P Wand
    Wavelet-based gradient boosting.
    Statistics and Computing, 26, 93-105. link
  2. Chong You, Samuel Müller, John T Ormerod
    On generalized degrees of freedom with application in linear mixed models selection.
    Statistics and Computing, 26, 199-210. link
  3. Allan E Clark, Res Altwegg, John T Ormerod
    A variational Bayes approach to the analysis of occupancy models.
    PloS one, 11, e0148966. link
  4. Shila Ghazanfar, Adam J Bisogni, John T Ormerod, David M Lin, Jean YH Yang
    Integrated single cell data analysis reveals cell specific networks and novel coactivation markers.
    BMC systems biology, 10, 127. link

2015

  1. Dario Strbenac, Graham J Mann, John T Ormerod, Jean YH Yang
    ClassifyR: an R package for performance assessment of classification with applications to transcriptomics.
    Bioinformatics, 31, 1851-1853. link

2014

  1. Neville, S.E., Ormerod, J.T. and Wand, M.P.
    Mean field variational Bayes for continuous sparse signal shrinkage: pitfalls and remedies.
    Electronic Journal of Statistics, 1 , 1113-1151. (preprint)
  2. You, C., Ormerod, J.T. and Muller, S.
    On variational Bayes estimation and variational Bayes information criteria for linear regression models.
    Australian and New Zealand Journal of Statistics, 56, 83-87. (preprint) (paper)
  3. Luts, J. and Ormerod, J.T. (2014).
    Mean field variational Bayesian inference for support vector machine classification.
    Computational Statistics and Data Analysis, 73, 163-176. (preprint) (paper)

2013

  1. Pham, T.H., Ormerod, J.T. and Wand, M.P. (2013).
    Mean field variational Bayesian inference for nonparametric regression with measurement error.
    Computational Statistics and Data Analysis, 68, 375-–387. (preprint) (paper)

2012

  1. Ormerod, J.T. and Wand, M.P. (2012).
    Gaussian variational approximate inference for generalized linear mixed models.
    Journal of Computational and Graphical Statistics, 21, 2-17. (preprint) (paper)
  2. Wand, M.P. and Ormerod, J.T. (2012).
    Continued fraction enhancement of Bayesian computing.
    STAT, 1, 31-41. (paper)

2011

  1. Wand, M.P., Ormerod, J.T., Padoan, S.A. and Fruhwirth, R. (2011).
    Mean field variational Bayes for elaborate distributions.
    Bayesian Analysis, 6, Number 4, 847-900. (paper)
  2. Wand, M.P. and Ormerod, J.T. (2011).
    Penalized wavelets: embedding wavelets into semiparametric regression.
    Electronic Journal of Statistics, 5, 1654-1717. (preprint)
  3. Sparks, R.S., Sutton, G., Toscas, P. and Ormerod, J.T. (2011).
    Modelling inverse Gaussian data with censored response values: EM versus MCMC.
    Advances in Decision Sciences, Volume 2011, 8 pages, DOI:10.1155/2011/571768. (paper)
  4. Faes, C., Ormerod J.T. and Wand M.P. (2011).
    Variational Bayesian inference for parametric and nonparametric regression with missing data.
    Journal of the American Statistical Association, 106, 959-971. (preprint)(paper)
  5. Hall, P., Ormerod, J.T. and Wand, M.P. (2011).
    Theory of Gaussian variational approximation for a Poisson mixed model.
    Statistica Sinica, 21, 369-389. (preprint)
  6. Ormerod, J.T. (2011).
    Grid based variational approximations.
    Computational Statistics and Data Analysis, 55, 45-56. (preprint)

2010

  1. Ormerod, J.T. and Wand, M.P. (2010).
    Explaining variational approximations.
    The American Statistician, 64, 140-153. (paper)
  2. Kauermann, G., Ormerod, J.T. and Wand, M.P. (2010).
    Parsimonious classification via generalised linear mixed models.
    Journal of Classification, 27, 89-110. (preprint)

2008

  1. Wand, M.P. and Ormerod, J.T. (2008).
    On semiparametric regression with O'Sullivan penalised splines.
    Australian and New Zealand Journal of Statistics, 50, 179-198. (paper) (correction notice) (appendix code)
  2. Ormerod, J.T., Wand, M.P. and Koch, I. (2008).
    Penalised spline support vector classifiers: computational issues.
    Computational Statistics, 23, 623-641. (preprint)

2006

  1. Jeyakumar, V., Ormerod, J. and Womersley, R.S. (2006).
    Knowledge-based Semidefinite linear programming classifiers.
    Optimization Methods and Software, 21, 693-706.

Other Publications

  1. Ormerod, J.T. and Wand, M.P. (2012).
    Comment on "Bayesian Computation Using Design of Experiments-based Interpolation Technique" by V. Roshan Joseph.
    Technometrics, 54, 233-236.
    (preprint)
  2. Ormerod, J.T. (2011).
    Book Review: "Mixed effects models for complex data" by Lang Wu, Chapman & Hall/CRC, Boca Raton, 2010.
    Statistics in Medicine, 30, 1326-1327.
  3. Ormerod, J.T. and Wand, M.P. (2009).
    Discussion of "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations", by H. Rue, S. Martino and N. Chopin.
    Journal of the Royal Statistical Society, Series B, 71, 377-378.