Publication Search Results
Exact matches for:
- Author = Ormerod JT [web profile page]
1.
Ormerod JT, Stewart M, Yu W, Romanes SE
J T Ormerod, M Stewart, W Yu and S E Romanes:
Bayesian hypothesis tests with diffuse priors: Can we have our cake and eat it too?,
Australian and New Zealand Journal of Statistics,
66
(2024),
no. 2,
204–227.
2.
Zhou J, Ormerod JT, Grazian C
Jackson Zhou, John T Ormerod, Clara Grazian:
Fast expectation propagation for heteroscedastic, lasso-penalized, and quantile regression.,
Journal of Machine Learning Research,
24
(2023),
Paper no. 314 (39 pages).
3.
You C, Ormerod JT, Li X, Pang CH, Zhou XH
Chong You, John T Ormerod, Xiangyang Li, Cheng Heng Pang and Xiao-Hua Zhou:
An Approximated Collapsed Variational Bayes Approach to Variable Selection in Linear Regression,
Journal of Computational and Graphical Statistics,
32
(2023),
no. 3,
782–792.
4.
Tran A, Yang P, Yang YH, Ormerod JT
Andy Tran, Pengyi Yang, Jean Y H Yang, John Ormerod:
Computational approaches for direct cell reprogramming: from the bulk omics era to the single cell era,
Briefings in Functional Genomics,
Volume 21
(2022),
Issue 4.
5.
Tran A, Yang P, Yang YH, Ormerod JT
Andy Tran, Pengyi Yang, Jean Y H Yang, John T Ormerod:
scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model,
NAR Genomics and Bioinformatics,
Volume 4
(2022),
Issue 1.
6.
Tzelios K, Williams LA, Ormerod JT, Bliss‑Moreau JOE
Kallie Tzelios, Lisa A Williams, John Omerod & Eliza Bliss‑Moreau:
Evidence of the unidimensional structure of mind perception,
Scientific Reports (Nature Publisher Group); London,
12
(2022),
no. 1,
Article 18978 (9 pages).
7.
Canete NP, Iyengar SS, Ormerod JT, Baharlou H, Harman AN, Patrick E
Nicolas P Canete, Sourish S Iyengar, John T Ormerod , Heeva Baharlou, Andrew N Harman and Ellis Patrick:
spicyR: spatial analysis of in situ cytometry data in R,
Bioinformatics,
38
(2022),
no. 11,
3099–3105.
8.
Yu W, Ormerod JT, Stewart M
Weichang Yu, John T. Ormerod and Michael Stewart:
Variational discriminant analysis with variable selection,
Statistics and Computing,
30
(2020),
no. 4,
933–951.
MR4108685
9.
Yu W, Azizi L, Ormerod JT
Weichang Yu, Lamiae Azizi and John Ormerod:
Variational nonparametric discriminant analysis,
Computational Statistics and Data Analysis,
142
(2020),
106817 (16 pages).
10.
Cao Y, Lin Y, Ormerod JT, Yang P, Yang YH, Lo KK
Yue Cao, Yingxin Lin, John T Ormerod, Pengyi Yang, Jean Y H Yang and Kitty K Lo:
ScDC: Single cell differential composition analysis,
BMC Bioinformatics,
20
(2019),
no. Suppl 19:721,
12 pages.
11.
Lin Y, Ghazanfar S, Wang KYX, Gagnon-Bartsch JA, Lo KK, Su X, Han ZG, Ormerod JT, Speed TP, Yang P, Yang YH
Yingxin Lin , Shila Ghazanfar, Kevin Y X Wang, Johann A Gagnon-Bartsch , Kitty K Lo , Xianbin Su, Ze-Guang Han, John T Ormerod , Terence P Speed, Pengyi Yang and Jean Yee Hwa Yang:
scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets,
PNAS,
116
(2019),
no. 20,
9775–9784.
12.
Yang P, Ormerod JT, Liu W, Ma C, Zomaya AY, Yang YH
Pengyi Yang , John T Ormerod, Wei Liu, Chendong Ma, Albert Y Zomaya and Jean Y H Yang:
AdaSampling for Positive-Unlabeled and Label Noise Learning With Bioinformatics Applications,
IEEE Transactions on Cybernetics,
49
(2019),
no. 5,
1932–1943.
13.
Ghazanfar S, Strbenac D, Ormerod JT, Yang YH, Patrick E
Shila Ghazanfar, Dario Strbenac, John T Ormerod, Jean Y H Yang and Ellis Patrick:
DCARS: Differential correlation across ranked samples,
Bioinformatics,
35
(2019),
no. 5,
823–829.
14.
Ghazanfar S, Strbenac D, Ormerod JT, Yang YH, Patrick E
Shila Ghazanfar, Dario Strbenac, John T Ormerod, Jean Y H Yang, Ellis Patrick:
DCARS: differential correlation across ranked samples,
Bioinformatics,
1
(2018),
bty698.
15.
Yang P, Ormerod JT, Liu W, Ma C, Zomaya AY, Yang YH
Pengyi Yang, John T. Ormerod, Wei Liu, Chendong Ma, Albert Y. Zomaya and Jean Y. H. Yang:
AdaSampling for positive-unlabeled and label noise learning with bioinformatics applications,
IEEE Transactions on Cybernetics,
1
(2018),
1–12.
16.
Luts J, Wang Q, Ormerod JT, Wand MP
Jan Luts, Shen Wang, John T. Ormerod and Matt P. Wand:
Semiparametric regression analysis via Infer.NET,
Journal of Statistical Software,
87
(2018),
no. 2,
1–37.
17.
Ormerod JT, You C, Muller S
JT Ormerod, C You, S Müller:
A variational Bayes approach to variable selection,
Electronic Journal of Statistics,
11
(2017),
3549–3594.
18.
Al-Anzi B, Gerges S, Olsman N, Ormerod CM, Piliouras G, Ormerod JT, Zinn K
Bader Al-Anzi, Sherife Gerges,Noah Olsman, Christopher Ormerod, Georgio Piliouras, John Ormerod, Kai Zinn:
Modeling and analysis of modular structure in diverse biological networks,
Journal of Theoretical Biology,
422
(2017),
18–30.
19.
Hui FKC, Warton DI, Ormerod JT, Haapaniemi V, Taskinen S
Francis K C Hui, David I Warton, John T Ormerod, Viivi Haapaniemi and Sara Taskinen:
Variational Approximations for Generalized Linear Latent Variable Models,
Journal of Computational and Graphical Statistics,
26
(2017),
no. 1,
35–43.
20.
Patrick E, Schramm S-J, Ormerod JT, Scolyer RA, Mann GJ, Muller S, Yang YH
E Patrick, S-J Schramm, JT Ormerod, RA Scolyer, GJ Mann, S Mueller, YH Yang:
A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types,
Oncotarget,
8
(2017),
no. 2,
2807–2815.
21.
Ghazanfar S, Bisogni AJ, Ormerod JT, Lin DM, Yang YH
Shila Ghazanfar, Adam J. Bisogni, John T. Ormerod, David M. Lin, and Jean Y. H. Yang:
Integrated single cell data analysis reveals cell specific networks and novel coactivation markers,
BMC Systems Biology,
10
(2016),
no. S5,
11–24.
22.
Strbenac D, Mann GJ, Yang YH, Ormerod JT
Dario Strbenac, Graham J Mann, Jean Y H Yang and John T Ormerod:
Differential distribution improves gene selection stability and has competitive classification performance for patient survival,
Nucleic Acids Research,
44
(2016),
no. 13,
e119.
23.
Clark AE, Altwegg R, Ormerod JT
Allan E Clark, Res Altwegg, John T Ormerod:
A Variational Bayes Approach to the Analysis of Occupancy Models,
PLoS One,
11
(2016),
no. 2,
e0148966 (18 pages).
24.
Dubossarsky E, Friedman JH, Ormerod JT, Wand MP
E Dubossarsky, J H Friedman, J T Ormerod, M P Wand:
Wavelet-based gradient boosting,
Statistics and Computing,
26
(2016),
no. 1,
93–105.
MR3439361
25.
You C, Muller S, Ormerod JT
C You, S Müller, J Ormerod:
On generalized degrees of freedom with application in linear mixed models selection,
Statistics and Computing,
26
(2016),
no. 1,
199–210.
MR3439368
26.
Strbenac D, Mann GJ, Ormerod JT, Yang YH
Dario Strbenac, Graham J Mann, John T Ormerod and Jean Y H Yang:
ClassifyR: an R package for performance assessment of classification with applications to transcriptomics,
Bioinformatics,
31
(2015),
no. 11,
1851–1853.
27.
Neville SE, Ormerod JT, Wand MP
Sarah E Neville, John T Ormerod and M P Wand:
Mean field variational Bayes for continuous sparse signal shrinkage: Pitfalls and remedies,
Electronic Journal of Statistics,
8
(2014),
1113–1151.
28.
You C, Ormerod JT, Muller S
C You, JT Ormerod, S Müller:
On Variational Bayes Estimation and Variational Bayes Information Criteria for Linear Regression Models,
Australian and New Zealand Journal of Statistics,
56
(2014),
73–87.
29.
Luts J, Ormerod JT
Jan Luts and John T Ormerod:
Mean field variational Bayesian inference for support vector machine classification,
Computational Statistics and Data Analysis,
73
(2014),
163–176.
30.
You C, Muller S, Ormerod JT
You C, Müller S, Ormerod JT:
On Generalized Degrees of Freedom and their Application in Linear Mixed Model Selection,
Proceedings,
59th ISI World Statistics Congress,
(2013),
1–5.
31.
Pham TH, Ormerod JT, Wand MP
Tung H Pham, John T Ormerod and M P Wand:
Mean field variational Bayesian inference for nonparametric regression with measurement error,
Computational Statistics and Data Analysis,
68
(2013),
375–387.
32.
Wand MP, Ormerod JT, Padoan SA, Frühwirth R
Matthew P Wand, John T Ormerod, Simone A Padoan and Rudolf Frühwirth:
Mean Field Variational Bayes for Elaborate Distributions,
Bayesian Analysis,
7
(2012),
no. 2,
847–900.
33.
Wand MP, Ormerod JT
Matt P Wand and John T Ormerod:
Continued fraction enhancement of Bayesian computing,
STAT,
1
(2012),
31–41.
34.
Ormerod JT, Wand MP
J T Ormerod & M P Wand:
Comment,
Technometrics,
54
(2012),
no. 3,
233–236.
MR2967972
35.
Ormerod JT, Wand MP
J T Ormerod & M P Wand:
Gaussian Variational Approximate Inference for Generalized Linear Mixed Models,
Journal of Computational and Graphical Statistics,
21
(2012),
no. 1,
2–17.
MR2913353
36.
Faes C, Ormerod JT, Wand MP
C Faes, J T Ormerod, M P Wand:
Variational Bayesian Inference for Parametric and Nonparametric Regression With Missing Data,
Journal of the American Statistical Association,
106
(2011),
no. 495,
959–971.
37.
Sparks RS, Sutton G, Toscas P, Ormerod JT
R S Sparks, G Sutton, P Toscas, and J T Ormerod:
Modelling Inverse Gaussian Data with Censored Response Values: EM versus MCMC,
Advances in Decision Sciences,
2011
(2011),
no. Article ID 571768,
8 pages.
38.
Hall P, Ormerod JT, Wand MP
Hall, P., Ormerod, J.T. and Wand, M.P.:
Theory of Gaussian Variational Approximation for a Poisson Mixed Model.,
Statistica Sinica,
21
(2011),
369–389.
39.
Ormerod JT
Ormerod, J.T.:
Grid Based Variational Approximations,
Computational Statistics and Data Analysis,
55
(2011),
45–56.
40.
Ormerod JT, Wand MP
Ormerod, J.T. ans Wand, M.P.:
Explaining Variational Approximations,
The American Statistician,
64
(2010),
140–153.
41.
Kauermann G, Ormerod JT, Wand MP
Kauermann, G., Ormerod, J.T. and Wand, M.P.:
Parsimonious Classification via Generalised Linear Mixed Models.,
Journal of Classification,
27
(2010),
89–110.
42.
Ormerod JT, Wand MP
Ormerod, J.T. and Wand, M.P.:
Discussion of "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations",
Journal of the Royal Statistical Society B,
71
(2009),
377–378.
43.
Wand MP, Ormerod JT
Wand, M.P. and Ormerod, J.T.:
On O'Sullivan penalised splines and semiparametric regression.,
Australian and New Zealand Journal of Statistics,
50
(2008),
179–198.
44.
Ormerod JT, Wand MP, Koch I
Ormerod, J.T., Wand, M.P. and Koch, I.:
Penalised spline support vector classifiers: computational issues.,
Computational Statistics,
23
(2008),
623–641.
45.
Jeyakumar V, Ormerod JT, Womersley RS
Jeyakumar, V., Ormerod, J.T. and Womersley, R.S.:
Knowledge-based Semidefinite linear programming classifiers.,
Optimization Methods and Software,
21
(2006),
693–706.
46.
Ormerod JT, Wand MP, Koch I
Ormerod, J.T., Wand, M.P. and Koch, I.:
Penalised spline support vector classifiers: computational issues.,
Statistical Solutions to Modern Problems,
20th International Workshop on Statistical Modelling.,
A.R. Francis, K.M. Matawie, A. Oshlack, G.K. Smyth (eds.),
University of Western Sydney,
Sydney, Australia,
(2005),
33–48.
ISBN 1 74108 101 7.
| Number of matches: 46 |