John T. Ormerod

According to our database, John T. Ormerod authored at least 21 papers between 2006 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Fast Expectation Propagation for Heteroscedastic, Lasso-Penalized, and Quantile Regression.

Jackson Zhou, John T. Ormerod, Clara Grazian

J. Mach. Learn. Res., 2023

An Approximated Collapsed Variational Bayes Approach to Variable Selection in Linear Regression.

Chong You, John T. Ormerod, Xiangyang Li, Cheng Heng Pang, Xiao-Hua Zhou

J. Comput. Graph. Stat., 2023

2022

spicyR: spatial analysis of <i>in situ</i> cytometry data in R.

Nicolas P. Canete, Sourish S. Iyengar, John T. Ormerod, Heeva Baharlou, Andrew N. Harman, Ellis Patrick

Bioinform., 2022

2020

Variational discriminant analysis with variable selection.

Weichang Yu, John T. Ormerod, Michael Stewart

Stat. Comput., 2020

Variational nonparametric discriminant analysis.

Weichang Yu, Lamiae Azizi, John T. Ormerod

Comput. Stat. Data Anal., 2020

2019

AdaSampling for Positive-Unlabeled and Label Noise Learning With Bioinformatics Applications.

Pengyi Yang, John T. Ormerod, Wei Liu, Chendong Ma, Albert Y. Zomaya, Jean Yee Hwa Yang

IEEE Trans. Cybern., 2019

scDC: single cell differential composition analysis.

Yue Cao, Yingxin Lin, John T. Ormerod, Pengyi Yang, Jean Yee Hwa Yang, Kitty K. Lo

BMC Bioinform., 2019

DCARS: differential correlation across ranked samples.

Shila Ghazanfar, Dario Strbenac, John T. Ormerod, Jean Yee Hwa Yang, Ellis Patrick

Bioinform., 2019

2018

Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data.

Sarah Elizabeth Romanes, John Thomas Ormerod, Jean Yee Hwa Yang

CoRR, 2018

2016

On generalized degrees of freedom with application in linear mixed models selection.

Chong You, Samuel Müller, John T. Ormerod

Stat. Comput., 2016

Wavelet-based gradient boosting.

Eugene Dubossarsky, Jerome H. Friedman, John T. Ormerod, Matthew P. Wand

Stat. Comput., 2016

Integrated single cell data analysis reveals cell specific networks and novel coactivation markers.

Shila Ghazanfar, Adam J. Bisogni, John T. Ormerod, David M. Lin, Jean Yee Hwa Yang

BMC Syst. Biol., 2016

2015

ClassifyR: an R package for performance assessment of classification with applications to transcriptomics.

Dario Strbenac, Graham J. Mann, John T. Ormerod, Jean Yee Hwa Yang

Bioinform., 2015

2014

Mean field variational Bayesian inference for support vector machine classification.

Jan Luts, John T. Ormerod

Comput. Stat. Data Anal., 2014

2013

Mean field variational Bayesian inference for nonparametric regression with measurement error.

Tung H. Pham, John T. Ormerod, Matthew P. Wand

Comput. Stat. Data Anal., 2013

2012

Comment.

John T. Ormerod, Matthew P. Wand

Technometrics, 2012

2011

Modelling Inverse Gaussian Data with Censored Response Values: EM versus MCMC.

Ross Stewart Sparks, Gordon J. Sutton, Peter J. Toscas, John T. Ormerod

Adv. Decis. Sci., 2011

Grid based variational approximations.

John T. Ormerod

Comput. Stat. Data Anal., 2011

2010

Parsimonious Classification Via Generalized Linear Mixed Models.

Göran Kauermann, John T. Ormerod, Matthew P. Wand

J. Classif., 2010

2008

Penalised spline support vector classifiers: computational issues.

John T. Ormerod, Matthew P. Wand, Inge Koch

Comput. Stat., 2008

2006

Knowledge-based semidefinite linear programming classifiers.

Vaithilingam Jeyakumar, John T. Ormerod, Robert S. Womersley

Optim. Methods Softw., 2006