2. S. Ejaz Ahmed and Farouk S. Nathoo. "Big Data Analytics
and Information Science for Business and Biomedical
Applications Volume II (Co-Edited Book), MDPI Publishing,
2022".ISBN 978-3-0365-5549-2 (Hbk); ISBN 978-3-0365-5550-8
(PDF).
https://doi.org/10.3390/books978-3-0365-5550-8 (registering
DOI). Open Access:
https://www.mdpi.com/books/pdfview/book/6388
51. Nancy Guo*, Aijun Yang*,
Fabiha Binte Farooq*, Shreena
Kalaria*, Elena Moss, Lindsay DeVorkin, Mary Lesperance,
Francois Benard, Don
Wilson, Anna V. Tinker, Farouk S. Nathoo, Phineas T.
Hamilton, Julian J. Lum.
(2023). CD8+ T cell infiltration is associated with improved
survival and
negatively correlates with hypoxia in clear cell ovarian
cancer. Scientific
Reports, doi: 10.1038/s41598-023-30655-3.
50. Beaulac*, C., Wu, S.,
Gibson, E., Cao,
J., Miranda, M., Rocha*, L., Beg, M.F.,
Nathoo, F.S. (2023). Neural
Network Disease Classification Based Feature Extraction
for Imaging Genetics. BMC
Bioinformatics,
https://doi.org/10.1186/s12859-023-05394-x.
49. Wei*, Zhengxiao, Farouk S.
Nathoo, and Michael EJ
Masson. (2023). "Investigating the relationship between
the Bayes factor
and the separation of credible intervals." Psychonomic
Bulletin
& Review: DOI:
10.3758/s13423-023-02295-1.
48. L. Barnhardt,
F.S.Nathoo, J. Rauw. (2022).
Improving Rates of Germline BRCA Mutation Testing for
Ovarian Cancer Patients
on Vancouver Island, British Columbia, Canada:
Implementation of a Local
Consenting Seminar- a Quality Improvement Project. Journal
of Oncology
Practice, DOI: 10.1200/OP.22.00341.
47. Wei*, Z., Yang*, A., Rocha*,
L., Miranda,
M., Nathoo, F.S. A review of Bayesian
hypothesis testing and its practical implementation. Entropy 2022, 24(2),
161; https://doi.org/10.3390/e24020161.
46. Ge, S., Wang, S., Nathoo,
F.S., Wang, L. Online Bayesian
Learning for Mixtures of Spatial Spline Regressions with
Mixed-Effects. Journal
of Statistical Computation and Simulation, https://doi.org/10.1080/00949655.2021.2002329.
45. Eugene A. Opoku*, Farouk
Nathoo, S. Ahmed. Sparse
Estimation
Strategies in Linear Mixed Effect Models for
High-dimensional Data
Application (2021) Entropy,
DOI: 10.3390/e23101348.
44. Nathoo, F.S. Bayesian
Methods for Imaging Genetics.
(2021) Journal of Brain, Behaviour and Cognitive
Sciences, 4(4),1.
43. Nathoo, F.S., Kong, L.,
Yi, G.Y. (2021)
Special Issue on Neuroimaging data analysis: Guest Editors
Introduction. Canadian
Journal of Statistics,
https://doi.org/10.1002/cjs.11613.
42. Eugene A. Opoku*, S.
Ahmed, Yin Song*,
Farouk Nathoo. (2021). Ant Colony System Optimization for
Spatiotemporal
Modelling of Combined EEG and MEG Data. Entropy,
23(3), 329. This
paper was highlighted on the main
page of Entropy as a title story.
41. *Song, Y., Ge, S., Cao,
J., Wang, L.,
Nathoo, F.S. (2021). A Bayesian Spatial Model for Imaging
Genetics. Biometrics.
;https://doi.org/10.1111/biom.13460 . An R package bgsmtr
implementing
the methods from this paper is available on CRAN here.
40. Yunlong Nie, *Eugene
Opoku, *Laila Yasmin,
*Yin Song, John Wang, Sidi Wu, Vanessa Scarapicchia, Jodie
Gawryluk, Liangliang
Wang, Jiguo Cao, Farouk S. Nathoo. (2020). Spectral
Dynamic Causal Modelling of
Resting-State fMRI: Relating Effective Brain Connectivity
in the Default Mode
Network to Genetics. Statistical
Applications in Genetics and
Molecular Biology, DOI:
https://doi.org/10.1515/sagmb-2019-0058.
39. *Opoku, E., Ahmed,
E., Nelson, T.,
Nathoo, F.S. (2020). Parameter and Mixture Component
Estimation in Spatial
Hidden Markov Models: A Comparative Analysis of
Computational Methods. In
International Conference on Management Science and
Engineering Management (pp.
340-355). Springer, Cham.
38. *Song, Y., Nathoo, F.S.,
Babul A. (2019), A
Potts-Mixture Spatiotemporal Joint Model for Combined MEG
and EEG Data. Canadian
Journal of Statistics, DOI:
10.1002/cjs.11519.
37. Nathoo, F.S., Kong, L.,
Zhu, H. (2018). A
Review of Statistical Methods in Imaging Genetics. Canadian
Journal of Statistics,
DOI: 10.1002/cjs.11487.
36. *Teng, M., Nathoo, F.S.,
Johnson, T.D. (2018). Bayesian
Analysis of fMRI data with Spatially-Varying Autoregressive
Orders. Journal
of the Royal Statistical Society: Series C,
DOI https://doi.org/10.1111/rssc.12320.
35. Nathoo,
F.S., *Kilshaw, R.E., Masson, M.E.J.
(2018). A Better (Bayesian) Interval Estimate
for Within-Subject
Designs. Journal of
Mathematical Psychology, https://doi.org/10.1016/j.jmp.2018.07.005.
34. *Teng, M., Johnson,
T.D., Nathoo, F.S. (2018). Time
Series Analysis of fMRI Data: Spatial Modeling and Bayesian
Computation. Statistics in Medicine, DOI:
10.1002/sim.7680.
33. *Shi S. and Nathoo,
F.S., Feature Learning and
Classification in Neuroimaging: Predicting Cognitive
Impairment from Magnetic
Resonance Imaging (2018). Proceedings of the
4th International
Conference on Big Data and Information Analytics.
32. *Greenlaw, K., Szefer,
E., Graham, J., Lesperance, M.L.,
Nathoo, F.S. (2017). A Bayesian Group Sparse
Mutli-Task Regression
Model for Imaging Genetics. Bioinformatics, DOI:
10.1093/bioinformatics/btx215. An
R package bgsmtr
implementing the
methods from this paper is available on CRAN here.
31. Szefer, E., Lu, D.,
Nathoo, F.S., M.F. Beg, Graham, J.,
(2017). Multivariate association between single-nucleotide
polymorphisms in
Alzgene linkage regions and structural changes in the brain:
discovery,
refinement and validation. Statistical Applications in Genetics
and Molecular Biology,
DOI:
https://doi.org/10.1515/sagmb-2016-0077.
30. *Teng, M., Nathoo, F.S.,
Johnson, T.D. (2017). Bayesian
Computation for Log Gaussian Cox Processes: A Comparative
Analysis of
Methods. Journal
of Statistical
Computation and Simulation, DOI:
10.1080/00949655.2017.1326117.
29. Bourbonnais ML, Nelson
TA, Stenhouse GB, Wulder MA,
White JC, Hobart GW,Hermosilla T, Coops NC, Nathoo F.S.,
Darimont C. (2017).
Characterizing spatial-temporal patterns of landscape
disturbance and recovery
in western Alberta, Canada using a functional data analysis
approach. Ecological Informatics, DOI:
10.1016/j.ecoinf.2017.04.010.
28. *Song, Y., Nathoo, F.S.,
Masson, M.E.J. (2017). A
Bayesian approach to the mixed effects analysis of repeated
measures accuracy
studies. Journal of Memory and Language, DOI:
10.1016/j.jml.2017.05.002. In 2017 this paper appeared
on the list of 'Most
Downloaded Journal of Memory and Language Articles.
27. Moss, A.,
Juarez-Colunga, E., Nathoo, F.S., Wagner, B.,
Sagel, S. (2016) Comparison of Change Point Models: A
Simulation and Case Study
in Modeling Lung Function in Children with Cystic
Fibrosis. Statistics in Medicine, DOI:
10.1002/sim.6845.
26. *Croteau, N., Nathoo,
F.S., Cao, J., Budney, R. (2016)
High-dimensional classification for brain decoding. Big and Complex Data Analysis:
Statistical Methodologies
and Applications, Springer,
Edited Volume.
25. Nathoo, F.S., *Greenlaw,
K., Lesperance, M.L. (2016).
Regularization parameter selection for a Bayesian
multi-level group lasso
regression model with application to imaging
genomics. Pattern Recognition in Neuroimaging
(PRNI), 2016 International
Workshop on. IEEE, 2016. DOI:
10.1109/PRNI.2016.7552328.
24. Lesperance, M.L.,
*Sabelnykova V., Nathoo F.S., Lau, F.,
Downing, G.M. (2015) A Joint Model for Interval-Censored
Functional Decline
Trajectories Under Informative Observation. Statistics in Medicine, DOI:
10.1002/sim.6582.
23. Nathoo, F.S. and Masson,
E.J. M. (2015), Bayesian
Alternatives to Null-Hypothesis Significance Testing for
Repeated Measures
Designs. Journal of Mathematical
Psychology, http://dx.doi.org/10.1016/j.jmp.2015.03.003. As
of
September, 2015, this paper is on the list of 'Most
Downloaded Journal of
Mathematical Psychology Articles'. This paper is also one of the most cited one
of the most cited Journal of Mathematical
Psychology articles (most
cited articles published since 2015). The list of
most cited articles is here.
22. Fitterer, J., Nelson,
T.A., and Nathoo, F.S.
(2014). Predictive crime mapping. Police
Practice and
Research, DOI:
10.1080/15614263.2014.972618.
21. Robertson, C., Long,
J.A., Nathoo, F.S., Nelson, T.A.,
and Plouffe, C.C.F. (2014). Assessing quality of spatial
models using the
structural similarity index and posterior predictive
checks. Geographical Analysis, 46,
53-74.
20. Nathoo, F.S. and Babul,
A. (2014), Statistical Modeling
of Electromagnetic Neuroimaging data (invited note). Notes of
the Canadian
Mathematical Society, 46, no 2, 13-14.
19. Nathoo, F.S., Babul, A.,
Moiseev, A. Virji-Babul, N.,
Beg, M.F. (2013). A Variational Bayes Spatiotemporal Model
for Electromagnetic
Brain Mapping. Biometrics, DOI:
10.1111/biom.12126.
18. Long, JA, Nelson, TA,
Nathoo, F.S. (2013). Towards a
kinetic based probabilistic time geography. International
Journal of
Geographical Information Science. DOI:
10.1080/13658816.2013.818151.
17. Nathoo, F.S., Lawson,
A.B., Dean, C.B. (2012). Guest
editors' introduction to the special issue on spatial
statistics for
neuroimaging. Statistical Methods in Medical Research, DOI:
10.1177/0962280212448971.
16. Nathoo, F.S.,
Lesperance, M.L., Lawson, A.B., Dean,
C.B. (2012). "Comparing Variational Bayes with Markov Chain
Monte Carlo
for Bayesian Computation in Neuroimaging." Statistical Methods in Medical Research, DOI:
10.1177/0962280212448973.
15. *Ismail, S., Sun, W.,
Nathoo, F.S., Babul, A., Moiseev,
A., Beg, M.F. Virji-Babul, N. (2012). "A Skew-t
Space-Varying Regression
Model for the Spectral Analysis of Resting State Brain
Activity." Statistical Methods in Medical Research, DOI:
10.1177/0962280212448974.
14. Nathoo, F.S., and Ghosh,
P. (2012). "Skew-Elliptical
Spatial Random Effect Modeling for Areal Data with
Application to Mapping
Health Utilization Rates." Statistics in Medicine, DOI:
10.1002/sim.5504.
13. Long, J.A., Robertson,
C., Nathoo, F.S., and Nelson,
T.A. (2012). "A Bayesian Space-Time Model for Discrete
Spread Processes on
a Lattice." Spatial and Spatio-Temporal
Epidemiology, DOI:
10.1016/j.sste.2012.04.008.
12. Morrison, K.T., Nelson,
T.A., Nathoo, F.S., Ostry
A.S. (2011). "Application of Bayesian spatial smoothing
models to assess
agricultural self-sufficiency." International
Journal of
Geographical Information Science, DOI:10.1080/13658816.2011.633491.
11. Robertson, C., Sawford,
K., Gunawardena, S., Nelson,
T.A., Nathoo, F.S., Stephen, C. (2011). "A hidden Markov
model for
analysis of frontline veterinary data for emerging zoonotic
disease
surveillance." PLoS
ONE 6(9):
e24833.
doi:10.1371/journal.pone.0024833.
10. Swartz, T. B.,
Tennakoon, A., Nathoo, F.S., Tsao, M.,
*Sarohia, P. (2011). "Playoff Series: Psychological Ups and
Downs." Journal of Quantitative Analysis in
Sports, Vol. 7:
Iss. 4, Article
3. DOI: 10.2202/1559-0410.1372.
9. Virji-Babul, N., Watt,
K., Nathoo, F.S. and Johnson, P
(2011). "Dynamic facial expression recognition in Down
syndrome." Physical and Occupational Therapy in
Pediatrics, DOI:
10.3109/01942638.2011.653626.
8. Ghosh, P., Nathoo F.S.,
Gonenn, M.,
and Tiwari, R.C. (2010). "Assessing noninferiority in a
three-arm
trial using the Bayesian approach." Statistics in Medicine, 30,
1795-1808.
7. Nathoo, F.S. (2010).
"Joint spatial modeling of
recurrent infection and growth with processes under
intermittent
observation." Biometrics, 66,
336-346.
6. Nathoo, F.S. (2010).
"Space-time regression modeling
of tree growth using the skew-t distribution." Environmetrics, DOI: 10.1002/env.1057.
5. Nathoo, F.S. and Dean,
C.B. (2008). "Spatial
multi-state transitional models for longitudinal event
data." Biometrics, 64,
271-279.
4. Dean, C.B., Nathoo, F.S.
and Nielson, J.D. (2007).
"Spatial and mixture models for recurrent event
processes." Enivronmetrics, 18,
713-725.
3. Nathoo, F.S. and Dean,
C.B. (2007). "A mixed
mover-stayer model for spatio-temporal two-state
processes." Biometrics, 63,
881-891.
2. Nathoo, F.S., Ainsworth,
L., Gill, P. and Dean, C.B.
(2006). "Codling moth incidence in Okanagan orchards." Canadian Journal of Statistics, 34,
493-530.
1. Nathoo, F.S. and Dean,
C.B. (2005). "Spatial
multi-state models with application to revascularization
intervention in
Quebec." Geomatica, 59,
335-343.