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ORCIDJohn T. Ormerod
Person information
- affiliation: University of Sydney, Australia
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2020 – today
- 2023
[j20]Chong You
, John T. Ormerod, Xiangyang Li, Cheng Heng Pang, Xiao-Hua Zhou:
An Approximated Collapsed Variational Bayes Approach to Variable Selection in Linear Regression. J. Comput. Graph. Stat. 32(3): 782-792 (2023)- [j19]Jackson Zhou, John T. Ormerod, Clara Grazian:
Fast Expectation Propagation for Heteroscedastic, Lasso-Penalized, and Quantile Regression. J. Mach. Learn. Res. 24: 314:1-314:39 (2023) - 2022
- [j18]Nicolas P. Canete, Sourish S. Iyengar, John T. Ormerod, Heeva Baharlou, Andrew N. Harman, Ellis Patrick:
spicyR: spatial analysis of in situ cytometry data in R. Bioinform. 38(11): 3099-3105 (2022) - 2020
- [j17]Weichang Yu, Lamiae Azizi, John T. Ormerod:
Variational nonparametric discriminant analysis. Comput. Stat. Data Anal. 142 (2020) - [j16]Weichang Yu, John T. Ormerod, Michael Stewart:
Variational discriminant analysis with variable selection. Stat. Comput. 30(4): 933-951 (2020)
2010 – 2019
- 2019
- [j15]Shila Ghazanfar, Dario Strbenac, John T. Ormerod, Jean Yee Hwa Yang, Ellis Patrick:
DCARS: differential correlation across ranked samples. Bioinform. 35(5): 823-829 (2019) - [j14]Yue Cao, Yingxin Lin, John T. Ormerod, Pengyi Yang, Jean Yee Hwa Yang, Kitty K. Lo:
scDC: single cell differential composition analysis. BMC Bioinform. 20-S(19): 721 (2019) - [j13]Pengyi Yang, John T. Ormerod, Wei Liu, Chendong Ma, Albert Y. Zomaya, Jean Yee Hwa Yang:
AdaSampling for Positive-Unlabeled and Label Noise Learning With Bioinformatics Applications. IEEE Trans. Cybern. 49(5): 1932-1943 (2019) - 2018
- [i1]Sarah Elizabeth Romanes, John Thomas Ormerod, Jean Yee Hwa Yang:
Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data. CoRR abs/1807.01422 (2018) - 2016
- [j12]Shila Ghazanfar, Adam J. Bisogni, John T. Ormerod, David M. Lin, Jean Yee Hwa Yang:
Integrated single cell data analysis reveals cell specific networks and novel coactivation markers. BMC Syst. Biol. 10(S-5): 11-24 (2016) - [j11]Eugene Dubossarsky, Jerome H. Friedman, John T. Ormerod, Matthew P. Wand:
Wavelet-based gradient boosting. Stat. Comput. 26(1-2): 93-105 (2016) - [j10]Chong You, Samuel Müller, John T. Ormerod:
On generalized degrees of freedom with application in linear mixed models selection. Stat. Comput. 26(1-2): 199-210 (2016) - 2015
- [j9]Dario Strbenac, Graham J. Mann, John T. Ormerod, Jean Yee Hwa Yang:
ClassifyR: an R package for performance assessment of classification with applications to transcriptomics. Bioinform. 31(11): 1851-1853 (2015) - 2014
- [j8]Jan Luts, John T. Ormerod:
Mean field variational Bayesian inference for support vector machine classification. Comput. Stat. Data Anal. 73: 163-176 (2014) - 2013
- [j7]Tung H. Pham, John T. Ormerod, Matthew P. Wand:
Mean field variational Bayesian inference for nonparametric regression with measurement error. Comput. Stat. Data Anal. 68: 375-387 (2013) - 2012
- [j6]John T. Ormerod, Matthew P. Wand:
Comment. Technometrics 54(3): 233-236 (2012) - 2011
- [j5]John T. Ormerod:
Grid based variational approximations. Comput. Stat. Data Anal. 55(1): 45-56 (2011) - [j4]Ross Stewart Sparks, Gordon J. Sutton, Peter J. Toscas, John T. Ormerod:
Modelling Inverse Gaussian Data with Censored Response Values: EM versus MCMC. Adv. Decis. Sci. 2011: 571768:1-571768:8 (2011) - 2010
- [j3]Göran Kauermann, John T. Ormerod, Matthew P. Wand:
Parsimonious Classification Via Generalized Linear Mixed Models. J. Classif. 27(1): 89-110 (2010)
2000 – 2009
- 2008
- [j2]John T. Ormerod, Matthew P. Wand, Inge Koch:
Penalised spline support vector classifiers: computational issues. Comput. Stat. 23(4): 623-641 (2008) - 2006
- [j1]Vaithilingam Jeyakumar, John T. Ormerod, Robert S. Womersley:
Knowledge-based semidefinite linear programming classifiers. Optim. Methods Softw. 21(5): 693-706 (2006)
Coauthor Index
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