Almasi, F., Stear, M. J., Khansefid, M., Nguyen, H., Desai, A., &
Pryce, J. E. (2024). Innovative use of sensor
technology to study grazing behaviour and its associations with
parasitic resistance in sheep. Small Ruminant Research,
232(107223).
Forbes, F., Nguyen, H. D., & Nguyen, T. T. (2024). Bayesian likelihood free inference using mixtures of
experts. Proceedings of the International Joint Conference on
Neural Networks.
Goh, P. K. T., Pulemotov, A., Nguyen, H., Pinto, N., & Olive, R.
(2024). Treatment duration by morphology and
location of impacted maxillary canines: a CBCT investigation.
American Journal of Orthodontics and Dentofacial
Orthopedics, to appear.
Grot, S., Smine, S., Potvin, S., Darcey, M., Pavlov, V., Genon, S.,
Nguyen, H., & Orban, P. (2024). Label-based
meta-analysis of functional brain dysconnectivity across mood and
psychotic disorders. Progress in Neuro-Psychopharmacology and
Biological Psychiatry, 131(110950).
Nguyen, H. (2024). PanIC: consistent information
criteria for general model selection problems. Australian and
New Zealand Journal of Statistics, to appear.
Truong, L., Weir, T., Nguyen, H., Freer, E., & Ong, D. (2024). Mesiodistal tip expression of lower anterior teeth in
lower incisor extraction cases treated with Invisalign
aligners. American Journal of Orthodontics and
Dentofacial Orthopedics, to appear.
Westerhout, J., Nguyen, T. T., Guo, X., & Nguyen, H. D. (2024). On
the asymptotic distribution of the minimum empirical risk. Forty-first International Conference on Machine
Learning.
Almasi, F., Stear, M., Khansefid, M., Nguyen, H., Desai, A., &
Pryce, J. E. (2023). The repeatability and
heritability of traits derived from accelerometer sensors associated
with grazing and rumination time in an extensive sheep farming
system. Frontiers in Animal Science,
4(1154797).
Arbel, J., Girard, S., Nguyen, H. D., & Usseglio-Carleve, A. (2023).
Multiple expectile-based distribution: properties,
Bayesian inference and applications. Journal of Statistical
Planning and Inference, 225, 146–170.
Fryer, D., Lowing, D., Strümke, I., & Nguyen, H. (2023). Multi-choice Explanations: a new cooperative game
structure for XAI. IJCNN Workshop on Trustworthy and
Responsible AI: Theory, Applications and Challanges.
Navarathna, R., Le, D. T., Hamann, A. R., Nguyen, H. D., Stace, T. M.,
& Fedorov, A. (2023). Passive superconducting
circulator on a chip. Physical Review Letters,
130, 037001.
Nguyen, H. D., Fryer, D., & McLachlan, G. J. (2023). Order selection with confidence for finite mixture
models. Journal of the Korean Statistical Society,
52, 154–184.
Nguyen, H. D., & Gupta, M. (2023). Finite
sample inference for empirical Bayesian methods. Scandinavian
Journal of Statistics, 50, 1616–1640.
Nguyen, T., Nguyen, D. N., Nguyen, H. D., & Chamroukhi, F. (2023).
A non-asymptotic risk bound for model selection in
high-dimensional mixture of experts via joint rank and variable
selection. In Proceedings of the
Australasian Jount Conference on Artificial Intelligence
(AJCAI). Springer.
Roohi, S., Skarbez, R., & Nguyen, H. (2023). Reliable emotion
recognition in conversation: Quantifying and communicating uncertainty.
IJCNN Workshop on Trustworthy and Responsible AI: Theory,
Applications and Challanges.
Wallis, T. P., Jiang, A., Young, K., Hou, H., Kudo, K., McCann, A. J.,
Durisic, N., Joensuu, M., Oelz, D., Nguyen, H., Gormal, R. S., &
Meunier, F. A. (2023). Super-resolved
trajectory-derived nanoclustering analysis using spatiotemporal
indexing. Nature Communications, 14, 3353.
Almasi, F., Khansefid, M., Nguyen, H., Desai, A., Pryce, J. E., &
Stear, M. (2022). Repeatability estimates of
grazing and rumination activity of Merino sheep measured using wearable
sensors. Proceedings of the World
Congress on Genetics Applied to Livestock Production.
Almasi, F., Nguyen, H., Heydarian, D., Sohi, R., Nikbin, S., Jenvey, C.
J., Halliwell, E., Ponnampalam, E. N., Desai, A., Jois, M., & Stear,
M. J. (2022). Quantification of behavioural
variation among sheep grazing on pasture using accelerometer
sensors. Animal Production Science, 62,
1527–1538.
Durand, J.-B., Forbes, F., Phan, C. D., Truong, L., Nguyen, H. D., &
Dama, F. (2022). Bayesian nonparametric spatial
prior for traffic crash risk mapping: a case study of Victoria,
Australia. Australian and New Zealand Journal of
Statistics, 64, 171–204.
Forbes, F., Nguyen, H. D., Nguyen, T. T., & Arbel, J. (2022). Approximate Bayesian computation with surrogate
posteriors. Statistics and Computing, 32, 85.
Gao, J., Burgard, D. A., Tscharke, B. J., Lai, F. Y., O’Brien, J. W.,
Nguyen, H. D., Zheng, Q., Li, J., Du, P., Li, X., Wang, D., Castiglioni,
S., Cruz-Cruz, C., Baz-Lomba, J. A., Yargeau, V., Emke, E., Thomas, K.
V., Mueller, J. F., & Thai, P. K. (2022). Refining the estimation of amphetamine consumption by
wastewater-based epidemiology. Water Research,
225, 119182.
Nguyen, H., Forbes, F., Fort, G., & Cappe, O. (2022). An online Minorization–Maximization Algorithm.
Proceedings of the International Federation of Classification
Societies.
Nguyen, H., Lee, S., & Forbes, F. (2022). A
Festschrift for Geoff McLachlan. Australian and New Zealand
Journal of Statistics, 64, 111–116.
Nguyen, T. T., Chamroukhi, F., Nguyen, H. D., & Forbes, F. (2022).
Model selection by penalization in mixture of
experts models with a non-asymptotic approach. 53emes
Journees de Statistique de La Societe Française de
Statistique (SFdS).
Nguyen, T. T., Nguyen, H. D., Chamroukhi, F., & Forbes, F. (2022).
A non-asymptotic approach for model selection via
penalization in high-dimensional mixture of experts.
Electronic Journal of Statistics, 16, 4742–4822.
Phan, D. C., Truong, L. T., Nguyen, H. D., & Tay, R. (2022). Modelling the safety effects of train commuters’ access
modes. Journal of Advanced Transportation,
2022, 3473397.
Sohi, R., Carroll, A., Nguyen, H., Almasi, Z., Miller, J., Trompf, J.,
Bervan, A., Godoy, B. I., Stear, M., Desai, A., & Jois, M. (2022).
Determination of ewe behaviour around lambing time
and prediction of parturition seven days prior to lambing by tri-axial
accelerometer sensors in an extensive farming system. Animal
Production Science, 62, 1729–1738.
Urchs, S., Tam, A., Orban, P., Moreau, C., Benhajali, Y., Nguyen, H. D.,
Evans, A. C., & Bellec, P. (2022). Subtypes of
functional connectivity associate robustly with ASD diagnosis.
eLife, 11, e56257.
Fryer, D. V., Strümke, I., & Nguyen, H. (2021). Explaining the data or explaining a model? Shapley values
that uncover non-linear dependencies. PeerJ Computer
Science, 7(e582).
Fryer, D., Strümke, I., & Nguyen, H. (2021). Shapley values for feature selection: the good, the bad,
and the axioms. IEEE Access, 9, 144352–144360.
McLachlan, G. J., Ng, S. K., & Nguyen, H. D. (2021). EM
Algorithm. In Wiley StatsRef: Statistics reference
online. Wiley.
Nguyen, H. D. (2021). Finite sample inference for
generic autoregressive models. Proceedings of FMfI 2021.
Nguyen, H. D., Bagnall-Guerreiro, J., & Jones, A. T. (2021). Universal inference with composite likelihoods.
Proceedings of the 63rd ISI World Statistics Congress.
Nguyen, H. D., Nguyen, T. T., Chamroukhi, F., & McLachlan, G.
(2021). Approximations of conditional probability
density functions in Lesbegue spaces via mixture of experts
models. Journal of Statistical Distributions and
Applications, 8(13).
Phan, D. C., Truong, L. T., Nguyen, H. D., & Tay, R. (2021). Can walking and cycling for train access improve road
safety? Australian Road Safety Conference
(ARSC2021).
Arbel, J., Marchal, O., & Nguyen, H. D. (2020). On strict sub-Gaussianity, optimal proxy variance and
symmetry for bounded random variables. ESAIM: Probability and
Statistics, 24, 39–55.
Bagnall, J., Jones, A., Karavarsamis, N., & Nguyen, H. (2020). The fully-visible Boltzmann machine and the Senate of the
45th Australian Parliament in 2016. Journal of Computational
Social Science, 3, 55–81.
Fryer, D., Nguyen, H., & Castellazzi, P. (2020). \(k\)-means on positive
definite matrices, and an application to clustering in radar image
sequences. Proceedings of the IEEE Symposium Series on
Computational Intelligence.
Nguyen, H. D., Arbel, J., Lü, H., & Forbes, F. (2020). Approximate Bayesian computation via the energy
statistic. IEEE Access, 8, 131683–131698.
Nguyen, H. D., Forbes, F., & McLachlan, G. J. (2020). Mini-batch learning of exponential family finite mixture
models. Statistics and Computing, 30, 731–748.
Nguyen, T. T., Nguyen, H. D., Chamroukhi, F., & McLachlan, G. J.
(2020). Approximation by finite mixtures of
continuous density functions that vanish at infinity. Cogent
Mathematics and Statistics, 7, 1750861.
Redivo, E., Nguyen, H., & Gupta, M. (2020). Bayesian clustering of skewed and multimodal data using
geometric skew normal distributions. Computational Statistics
and Data Analysis, 152, 107044.
Vladimirova, M., Girard, S., Nguyen, H., & Arbel, J. (2020). Sub-Weibull distributions: generalizing sub-Gaussian and
sub-Exponential properties to heavier-tailed distributions.
Stat, 9, e318.
Chamroukhi, F., Lecocq, F., & Nguyen, H. D. (2019). Regularized estimation and feature selection in mixtures
of Gaussian-gated experts models. Proceedings of the Research School on Statistics and Data
Science (RSSDS).
Chamroukhi, F., & Nguyen, H. D. (2019). Model-based clustering and classification of functional
data. WIREs Data Mining and Knowledge Discovery,
e1298.
Fryer, D., Nguyen, H., & Orban, P. (2019).
studentlife: tidy handling and navigation of a valuable
mobile-health dataset.
Journal of Open Source Software,
4(40).
10.21105/joss.01587
Jones, A. T., Bagnall, J. J., & Nguyen, H. D. (2019). BoltzMM: an R package for maximum pseudolikelihood
estimation of fully-visible Boltzmann machines. Journal of
Open Source Software, 4, 1193.
Jones, A. T., Nguyen, H. D., & Bagnall, J. J. (2019). BoltzMM: Boltzmann Machines with MM
Algorithms. Software published in the Comprehensive R
Archive Network.
Nguyen, H. (Ed.). (2019). Statistics and Data
Science: Proceedings of the 2019 Research School on Statistics and Data
Science (RSSDS). Springer.
Nguyen, H. D. (2019). An introduction to
approximate Bayesian computation. Proceedings of the Research School on Statistics and Data
Science (RSSDS).
Nguyen, H. D. (2019). Asymptotic normality of the
time-domain generalized least squares estimator for linear regression
models. Stat, 8(e248).
Nguyen, H. D., Chamroukhi, F., & Forbes, F. (2019). Approximation results regarding the multiple-output
mixture of linear experts model. Neurocomputing,
366, 208–214.
Nguyen, H. D., & McLachlan, G. J. (2019). On
approximation via convolution-defined mixture models.
Communications in Statistics - Theory and Methods, 48,
3945–3955.
Nguyen, H. D., Yee, Y., McLachlan, G. J., & Lerch, J. P. (2019).
False discovery rate control for grouped or
discretely supported p-values with application to a neuroimaging
study. SORT, 43, 1–22.
Truong, L., Nguyen, H., Nguyen, H., & Vu, H. (2019). Pedestrian overpass use and its relationship with digital
and social distractions, and overpass characteristics.
Accident Analysis and Prevention, 131, 234–238.
Bagnall, J., Jones, A. T., & Nguyen, H. (2018). Analysing the voting patterns of the Senate of the 45th
Australian Parliament via fully-visible Boltzmann machines.
Poster presented at UseR! 2018.
Jones, A. T., Nguyen, H. D., & McLachlan, G. J. (2018). logKDE: log-transformed kernel density estimation.
Journal of Open Source Software, 3, 870.
Lloyd-Jones, L. R., Nguyen, H. D., & McLachlan, G. J. (2018). A globally convergent algorithm for lasso-penalized
mixture of linear regression models. Computational Statistics
and Data Analysis, 119, 19–38.
Nguyen, H. D. (2018). Near universal consistency of
the maximum pseudolikelihood estimator for discrete models.
Journal of the Korean Statistical Society, 47, 90–98.
Nguyen, H. D., & Chamroukhi, F. (2018). An
introduction to the practical and theoretical aspects of
mixture-of-experts modeling. WIREs Data Mining and Knowledge
Discovery, e1246.
Nguyen, H. D., & Jones, A. T. (2018). Big
data-appropriate clustering via stochastic approximation and Gaussian
mixture models. In Data Analytics:
Concepts, Techniques, and Applications. CRC Press.
Nguyen, H. D., Jones, A. T., & McLachlan, G. J. (2018). logKDE: Computing log-transformed kernel density estmates
for positive data. Software published in the Comprehensive R
Archive Network.
Nguyen, H. D., Jones, A. T., & McLachlan, G. J. (2018). Positive data kernel density estimation via the logKDE
package for R. Proceedings of the
Sixteenth Australasian Data Mining Conference.
Nguyen, H. D., Jones, A. T., & McLachlan, G. J. (2018). Stream-suitable optimization algorithms for some
soft-margin support vector machine variants. Japanese Journal
of Statistics and Data Science, 1, 81–108.
Nguyen, H. D., & McLachlan, G. J. (2018). Chunked-and-averaged estimators for vector
parameters. Statistics and Probability Letters,
137, 336–342.
Nguyen, H. D., & McLachlan, G. J. (2018). Some
theoretical results regarding the polygonal distribution.
Communications in Statistics - Theory and Methods, 47,
5083–5095.
Nguyen, H. D., McLachlan, G. J., Ullmann, J. F. P., Voleti, V., Li, W.,
Hillman, E. M. C., Reutens, D. C., & Janke, A. L. (2018). Whole-volume clustering of time series data from
zebrafish brain calcium images via mxiture modeling.
Statistical Analysis and Data Mining, 11, 5–16.
Nguyen, H. D., Wang, D. H., & McLachlan, G. J. (2018). Randomized mixture models for probability density
approximation and estimation. Information Sciences,
467, 135–148.
Orban, P., Dansereau, C., Desbois, L., Mongeau-Perusse, V., Giguere,
C.-E., Nguyen, H., Mendrek, A., Stip, E., & Bellec, P. (2018). Multisite generalizability of schizophrenia diagnosis
classification based on functional brain connectivity.
Schizophrenia Research, 192, 167–171.
McLachlan, G. J., & Nguyen, H. D. (2017). Contribution to the discussion of paper by M. Drton and
M. Plummer. Journal of the Royal Statistical Society B,
79, 365.
Nguyen, H. D. (2017). A novel algorithm for
clustering of data on the unit sphere via mixture models. JSM
Proceedings: Statistical Computing Section.
Nguyen, H. D. (2017). An introduction to MM
algorithms for machine learning and statistical estimation.
WIREs Data Mining and Knowledge Discovery, 7(e1198).
Nguyen, H. D., & McLachlan, G. J. (2017). Iteratively-reweighted least-squares fitting of support
vector machines: a majorization-minimization algorithm approach.
Proceedings of the 2017 Future Technologies Conference (FTC).
Nguyen, H. D., & McLachlan, G. J. (2017). Progress on a conjecture regarding the triangular
distribution. Communications in Statistics - Theory and
Methods, 46, 11261–11271.
Nguyen, H. D., McLachlan, G. J., & Hill, M. M. (2017). Permutation tests with false discovery corrections for
comparative-profiling proteomics experiments. In Methods in
molecular biology: Proteomics bioinformatics. Springer.
Nguyen, H. D., McLachlan, G. J., Orban, P., Bellec, P., & Janke, A.
L. (2017). Maximum pseudolikelihood estimation for
a model-based clustering of time series data. Neural
Computation, 29, 990–1020.
Oyarzun, C., Sanjurjo, A., & Nguyen, H. (2017). Response functions. European Economic
Review, 98, 1–31.
Jones, A. T., & Nguyen, H. D. (2016). lowmemtkmeans: Low memory use trimmed
k-means. Software published in the Comprehensive R Archive
Network.
Lloyd-Jones, L. R., Nguyen, H. D., McLachlan, G. J., Sumpton, W., &
Wang, Y.-G. (2016). Mixture of time dependent
growth models with an application to blue swimmer crab length-frequency
data. Biometrics, 72, 1255–1265.
Nguyen, H. D., Lloyd-Jones, L. R., & McLachlan, G. J. (2016). A block minorization-maximization algorithm for
heteroscedastic regression. IEEE Signal Processing
Letters, 23, 1031–1135.
Nguyen, H. D., Lloyd-Jones, L. R., & McLachlan, G. J. (2016). A universal approximation theorem for mixture-of-experts
models. Neural Computation, 28, 2585–2593.
Nguyen, H. D., & McLachlan, G. J. (2016). Laplace mixture of linear experts.
Computational Statistics and Data Analysis, 93,
177–191.
Nguyen, H. D., & McLachlan, G. J. (2016). Linear mixed models with marginally symmetric
nonparametric random-effects. Computational Statistics and
Data Analysis, 106, 151–169.
Nguyen, H. D., & McLachlan, G. J. (2016). Maximum likelihood estimation of triangular and polygonal
distributions. Computational Statistics and Data
Analysis, 106, 23–36.
Nguyen, H. D., McLachlan, G. J., Ullmann, J. F. P., & Janke, A. L.
(2016). Laplace mixture autoregressive
models. Statistics and Probability Letters,
110, 18–24.
Nguyen, H. D., McLachlan, G. J., Ullmann, J. F. P., & Janke, A. L.
(2016). Spatial clustering of time-series via
mixture of autoregressions models and Markov Random Fields.
Statistica Neerlandica, 70, 414–439.
Nguyen, H. D., McLachlan, G. J., & Wood, I. A. (2016). Mixtures of spatial spline regressions for clustering and
classification. Computational Statistics and Data
Analysis, 93, 76–85.
Nguyen, H. D., & Wood, I. A. (2016). A block
successive lower-bound maximization algorithm for the maximum
pseudolikelihood estimation of fully visible Boltzmann machines.
Neural Computation, 28, 485–492.
Nguyen, H. D., & Wood, I. A. (2016). Asymptotic
normality of the maximum pseudolikelihood estimator for fully visible
Boltzmann machines. IEEE Transactions on Neural Networks and
Learning Systems, 27, 897–902.
Nguyen, H. D. (2015). Finite mixture models for
regression problems [PhD thesis]. University of Queensland.
Nguyen, H. D. (2015). NostalgiR: Advanced
text-based plots. Software published in the Comprehensive R
Archive Network.
Nguyen, H. D., & McLachlan, G. J. (2015). Maximum likelihood estimation of Gaussian mixture models
without matrix operations. Advances in Data Analysis and
Classification, 9, 371–394.
Chen, D., Shah, A., Nguyen, H., Loo, D., Inder, K., & Hill, M.
(2014). Online quantitative proteomics p-value
calculator for permutation-based statistical testing of peptide
ratios. Journal of Proteomics Research, 13,
4184–4191.
Lloyd-Jones, L. R., Nguyen, H. D., Wang, Y.-G., & O’Neill, M. F.
(2014). Improved estimation of size-transition
matrices using tag-recapture data. Canadian Journal of
Fisheries and Aquatic Sciences, 71, 1385–1394.
Nguyen, H. D., & McLachlan, G. J. (2014). Asymptotic inference for hidden process regression
models. Proceedings of the IEEE
Statistical Signal Processing Workshop.
Nguyen, H. D., McLachlan, G. J., Cherbuin, N., & Janke, A. L.
(2014). False discovery rate control in magnetic
resonance imaging studies via Markov random fields. IEEE
Transactions on Medical Imaging, 33, 1735–1748.
Nguyen, H. D., Janke, A. L., Cherbuin, N., McLachlan, G. J., Sachdev,
P., & Anstey, K. J. (2013). Spatial false
discovery rate control for magnetic resonance imaging studies.
Proceedings of the 2013 Digital Imaging:
Techniques and Applications (DICTA) Conference.
Inder, K. L., Zheng, Y. Z., Davis, M. J., Moon, H., Loo, D., Nguyen, H.,
Clements, J. A., Parton, R. G., Foster, L. J., & Hill, M. M. (2012).
Expression of PRTF in PC-3 cells modulated
cholesterol dynamics and actin cytoskeleton impacting secretion
pathways. Molecular and Cellular Proteomics,
11(M111.012245).
Nguyen, H. D., Hill, M. M., & Wood, I. A. (2012). A robust permutation test for quantitative SILAC
proteomics experiments. Journal of Integrated OMICS,
2(80-93).
Nguyen, H. D., & Wood, I. A. (2012). Variable
selection in statistical models using population-based incremental
learning with applications to genome-wide association studies.
Proceedings of the 2012 IEEE Congress on
Evolutionary Computation (CEC).