People_

Associate Professor Minh-Ngoc Tran

Senior Lecturer

Phone

+61 2 8627 4752

Email

minh-ngoc.tran@sydney.edu.au

Websites

Minh-Ngoc Tran's site

Biographical details

Minh-Ngoc’s main research interests lie in Bayesian methodology and statistical machine learning. He specialises in fast Variational Bayes and simulation-based methods, such as importance sampling and sequential Monte Carlo, for estimating complex models with Big Data, and in Lasso-type variable selection methods.

Teaching and supervision

BUSS1020 Quantitative Business Analysis
BUSS6002 Data Science in Business
BUSS7904 Advanced Analysis for Research
QBUS3820 Data Mining and Data Analysis
QBUS3830 Advanced Analytics
QBUS5001 Quantitative Methods for Business
QBUS6840 Predictive Analytics

Current research students

Project title	Research student
Optimization on the space of probability measures	Peiwen JIANG
Deep learning in Financial Time Series Forecasting	Chen LIU
Research on the Explainability of Machine Learning and Artificial Intelligence in Business Analytics	Hongwei MA
Research on Financial Risk Management Based on Financial Technology	Haoyuan WANG

Selected publications

Data Analytics and Insights

(Wiley, 2021)

Publications

By Type

Expand all

Books

Tran, M., Antic, A., Hassani-Mahmooei, B., Ozmen, M. (2021). Data Analytics and Insights. Melbourne, Australia: Wiley. [More Information]

Journals

Liu, C., Wang, C., Tran, M., Kohn, R. (2025). A long short-term memory enhanced realized conditional heteroskedasticity model. Economic Modelling, 142, 106922. [More Information]
Gunawan, D., Kohn, R., Tran, M. (2025). Flexible and Robust Particle Tempering for State Space Models. Econometrics and Statistics, 33, 35-55. [More Information]
Lopatnikova, A., Tran, M., Sisson, S. (2024). An Introduction to Quantum Computing for Statisticians and Data Scientists. Foundations of Data Science, 6(3), 278-307. [More Information]
Dao, V., Gunawan, D., Kohn, R., Tran, M., Hawkins, G., Brown, S. (2024). Bayesian Inference for Evidence Accumulation Models with Regressors. Psychological Methods, in press. [More Information]
Nguyen, H., Nguyen, H., Tran, M. (2024). Deep learning enhanced volatility modeling with covariates. Finance Research Letters, 69, 106145. [More Information]
Dao, V., Gunawan, D., Tran, M., Kohn, R., Hawkins, G., Brown, S. (2024). Efficient selection between hierarchical cognitive models: Cross-validation with variational Bayes. Psychological Methods, 29(1), 219-241. [More Information]
Godichon-Baggioni, A., Nguyen, D., Tran, M. (2024). Natural Gradient Variational Bayes Without Fisher Matrix Analytic Calculation and Its Inversion. Journal of the American Statistical Association, Published online: 26 September 2024. [More Information]
Peiris, R., Wang, C., Gerlach, R., Tran, M. (2024). Semi-parametric financial risk forecasting incorporating multiple realized measures. Quantitative Finance, 24(12), 1823-1837. [More Information]
Nguyen, N., Tran, M., Chandra, R. (2024). Sequential reversible jump MCMC for dynamic Bayesian neural networks. Neurocomputing, 564, 126960. [More Information]
Nguyen, N., Tran, M., Drovandi, C., Nott, D. (2024). Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood. Journal of Computational and Graphical Statistics, in press. [More Information]
Nguyen, H., Nguyen, N., Tran, M. (2023). A dynamic leverage stochastic volatility model. Applied Economics Letters, 30(1), 97-102. [More Information]
Nguyen, N., Tran, M., Gunawan, D., Kohn, R. (2023). A Statistical Recurrent Stochastic Volatility Model for Stock Markets. Journal of Business and Economic Statistics, 41(2), 414-428. [More Information]
Virbickaite, A., Nguyen, H., Tran, M. (2023). Bayesian predictive distributions of oil returns using mixed data sampling volatility models. Resources Policy, 86, 104167. [More Information]
Jie, R., Gao, J., Vasnev, A., Tran, M. (2022). Adaptive hierarchical hyper-gradient descent. International Journal of Machine Learning and Cybernetics, 13(12), 3785-3805. [More Information]
Nguyen, N., Tran, M., Kohn, R. (2022). Recurrent Conditional Heteroskedasticity. Journal of Applied Econometrics, 37(5), 1031-1054. [More Information]
Gunawan, D., Hawkins, G., Tran, M., Kohn, R., Brown, S. (2022). Time-evolving psychological processes over repeated decisions. Psychological Review, 129(3), 438-456. [More Information]
Yu, X., Nott, D., Tran, M., Klein, N. (2021). Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance. Journal of Computational and Graphical Statistics, 30(4), 977-990. [More Information]
Tung, D., Tran, M. (2021). Flexible multivariate regression density estimation. Communications in Statistics - Theory and Methods, 50(20), 4703-4717. [More Information]
Wall, L., Gunawan, D., Brown, S., Tran, M., Kohn, R., Hawkins, G. (2021). Identifying relationships between cognitive processes across tasks, contexts, and time. Behavior Research Methods, 53(1), 78-95. [More Information]
Zhou, B., Gao, J., Tran, M., Gerlach, R. (2021). Manifold optimization Assisted Gaussian Variational Approximation. Journal of Computational and Graphical Statistics, 30(4), 946-957. [More Information]
Tran, M., Scharth, M., Gunawan, D., Kohn, R., Brown, S., Hawkins, G. (2021). Robustly estimating the marginal likelihood for cognitive models via importance sampling. Behavior Research Methods, 53(3), 1148-1165. [More Information]
Quiroz, M., Tran, M., Villani, M., Kohn, R., Dang, K. (2021). The Block-Poisson Estimator for Optimally Tuned Exact Subsampling MCMC. Journal of Computational and Graphical Statistics, 30(4), 877-888. [More Information]
Tran, M., Nguyen, D., Nguyen, D. (2021). Variational Bayes on manifolds. Statistics and Computing, 31(6), 71. [More Information]
Tran, M., Nguyen, N., Nott, D., Kohn, R. (2020). Bayesian Deep Net GLM and GLMM. Journal of Computational and Graphical Statistics, 29(1), 97-113. [More Information]
Jie, R., Gao, J., Vasnev, A., Tran, M. (2020). HyperTube: A Framework for Population-Based Online Hyperparameter Optimization with Resource Constraints. IEEE Access, 8, 69038-69057. [More Information]
Gunawan, D., Hawkins, G., Tran, M., Kohn, R., Brown, S. (2020). New estimation approaches for the hierarchical Linear Ballistic Accumulator model. Journal of Mathematical Psychology, 96, 102368. [More Information]
Gunawan, D., Dang, K., Quiroz, M., Kohn, R., Tran, M. (2020). Subsampling sequential Monte Carlo for static Bayesian models. Statistics and Computing, 30(6), 1741-1758. [More Information]
Tung, D., Tran, M., Cuong, T. (2019). Bayesian adaptive lasso with variational Bayes for variable selection in high-dimensional generalized linear mixed models. Communications in Statistics: Simulation and Computation, 48(2), 530-543. [More Information]
Gunawan, D., Tran, M., Suzuki, K., Dick, J., Kohn, R. (2019). Computationally Efficient Bayesian Estimation of High Dimensional Archimedian Copulas with Discrete and Mixed Margins. Statistics and Computing, 29(5), 933-946. [More Information]
Dang, K., Quiroz, M., Kohn, R., Tran, M., Villani, M. (2019). Hamiltonian Monte Carlo with Energy Conserving Subsampling. Journal of Machine Learning Research, 20, 1-31. [More Information]
Quiroz, M., Kohn, R., Villani, M., Tran, M. (2019). Speeding up MCMC by efficient data subsampling. Journal of the American Statistical Association, 114(526), 831-843. [More Information]
Drovandi, C., Tran, M. (2018). Improving the Efficiency of Fully Bayesian Optimal Design of Experiments Using Randomised Quasi-Monte Carlo. Bayesian Analysis, 13(1), 139-162. [More Information]
Ong, V., Nott, D., Tran, M., Sisson, S., Drovandi, C. (2018). Likelihood-free inference in high dimensions with synthetic likelihood. Computational Statistics and Data Analysis, 128, 271-291. [More Information]
Quiroz, M., Tran, M., Villani, M., Kohn, R. (2018). Speeding up MCMC by delayed acceptance and data subsampling. Journal of Computational and Graphical Statistics, 27(1), 12-22. [More Information]
Quiroz, M., Kohn, R., Tran, M., Villani, M., Dang, K. (2018). Subsampling MCMC - an Introduction for the Survey Statistician. Sankhya: The Indian Journal of Statistics, 80-A (Supplement 1), 33-69. [More Information]
Ong, V., Nott, D., Tran, M., Sisson, S., Drovandi, C. (2018). Variational Bayes with Synthetic Likelihood. Statistics and Computing, 28(4), 971-988. [More Information]
Tran, M., Nott, D., Kohn, R. (2017). Variational Bayes with Intractable Likelihood. Journal of Computational and Graphical Statistics, 26(4), 873-882. [More Information]
Tran, M., Pitt, M., Kohn, R. (2016). Adaptive Metropolis-Hastings sampling using reversible dependent mixture proposals. Statistics and Computing, 26(1), 361-381. [More Information]
Tran, M., Nott, D., Kuk, A., Kohn, R. (2016). Parallel Variational Bayes for Large Datasets With an Application to Generalized Linear Mixed Models. Journal of Computational and Graphical Statistics, 25(2), 626-646. [More Information]
Leng, C., Tran, M., Nott, D. (2014). Bayesian adaptive Lasso. Annals of the Institute of Statistical Mathematics, 66(2), 221-244. [More Information]
Tran, M., Giordani, P., Mun, X., Kohn, R., Pitt, M. (2014). Copula-Type Estimators for Flexible Multivariate Density Modeling Using Mixtures. Journal of Computational and Graphical Statistics, 23(4), 1163-1178. [More Information]
Giordani, P., Mun, X., Tran, M., Kohn, R. (2013). Flexible Multivariate Density Estimation with Marginal Adaptation. Journal of Computational and Graphical Statistics, 22(4), 814-829. [More Information]
Zhang, W., Xiaoxia, C., Tran, M. (2013). The structural features and the deliberative quality of online discussions. Telematics and Informatics, 30(2), 74-86. [More Information]
Tran, M., Giordani, P., Kohn, R. (2012). Discussion of "Fast sparse regression and classification" by Jerome Friedman. International Journal of Forecasting, 28(3), 749-750. [More Information]
Tran, M., Nott, D. (2012). Simultaneous variable selection and component selection for regression density estimation with mixtures of heteroscedastic experts. Electronic Journal of Statistics, 6, 1170-1199. [More Information]
Nott, D., Marshall, L., Tran, M. (2012). The ensemble Kalman filter is an ABC algorithm. Statistics and Computing, 22(6), 1273-1276. [More Information]
Tran, M., Nott, D., Leng, C. (2012). The predictive Lasso. Statistics and Computing, 22(5), 1069-1084. [More Information]
Nott, D., Tran, M., Leng, C. (2012). Variational approximation for heteroscedastic linear models and matching pursuit algorithms. Statistics and Computing, 22(2), 497-512. [More Information]
Tran, M. (2011). A criterion for optimal predictive model selection. Communications in Statistics - Theory and Methods, 40(5), 893-906. [More Information]
Tran, M. (2011). The loss rank criterion for variable selection in linear regression analysis. Scandinavian Journal of Statistics: theory and applications, 38(3), 466-479. [More Information]
Hutter, M., Tran, M. (2010). Model selection with the Loss Rank Principle. Computational Statistics and Data Analysis, 54(5), 1288-1306. [More Information]
Tran, M. (2009). Penalized Maximum Likelihood Principle for Choosing Ridge Parameter. Communications in Statistics: Simulation and Computation, 38(8), 1610-1624. [More Information]

show 49 more

Conferences

Lopatnikova, A., Tran, M. (2023). Quantum Variational Bayes on Manifolds. 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), United States: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Jie, R., Gao, J., Vasnev, A., Tran, M. (2021). Regularized flexible activation function combination for deep neural networks. 25th International Conference on Pattern Recognition, ICPR 2020, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Salomone, R., Quiroz, M., Kohn, R., Villani, M., Tran, M. (2020). Spectral Subsampling MCMC for Stationary Time Series. 37th International Conference on Machine Learning (ICML 2020), Vienna: International Machine Learning Society.
Tran, M., Nott, D., Kohn, R. (2015). Variational Bayes with Intractable Likelihood. 5th Vietnam National Congress in Probability and Statistics, Da Nang, Vietnam: Vietnam Institute for Advanced Study in Mathematics.
Tran, M. (2013). Adaptive Metropolis-Hastings sampling using reversible dependent mixture proposals. 8th Vietnamese Mathematical Conference, Nha Trang, Vietnam: Vietnam Institute for Advanced Study in Mathematics.

show 2 more

By Year

Expand all

2025

Liu, C., Wang, C., Tran, M., Kohn, R. (2025). A long short-term memory enhanced realized conditional heteroskedasticity model. Economic Modelling, 142, 106922. [More Information]
Gunawan, D., Kohn, R., Tran, M. (2025). Flexible and Robust Particle Tempering for State Space Models. Econometrics and Statistics, 33, 35-55. [More Information]

2024

Lopatnikova, A., Tran, M., Sisson, S. (2024). An Introduction to Quantum Computing for Statisticians and Data Scientists. Foundations of Data Science, 6(3), 278-307. [More Information]
Dao, V., Gunawan, D., Kohn, R., Tran, M., Hawkins, G., Brown, S. (2024). Bayesian Inference for Evidence Accumulation Models with Regressors. Psychological Methods, in press. [More Information]
Nguyen, H., Nguyen, H., Tran, M. (2024). Deep learning enhanced volatility modeling with covariates. Finance Research Letters, 69, 106145. [More Information]
Dao, V., Gunawan, D., Tran, M., Kohn, R., Hawkins, G., Brown, S. (2024). Efficient selection between hierarchical cognitive models: Cross-validation with variational Bayes. Psychological Methods, 29(1), 219-241. [More Information]
Godichon-Baggioni, A., Nguyen, D., Tran, M. (2024). Natural Gradient Variational Bayes Without Fisher Matrix Analytic Calculation and Its Inversion. Journal of the American Statistical Association, Published online: 26 September 2024. [More Information]
Peiris, R., Wang, C., Gerlach, R., Tran, M. (2024). Semi-parametric financial risk forecasting incorporating multiple realized measures. Quantitative Finance, 24(12), 1823-1837. [More Information]
Nguyen, N., Tran, M., Chandra, R. (2024). Sequential reversible jump MCMC for dynamic Bayesian neural networks. Neurocomputing, 564, 126960. [More Information]
Nguyen, N., Tran, M., Drovandi, C., Nott, D. (2024). Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood. Journal of Computational and Graphical Statistics, in press. [More Information]

show 5 more

2023

Nguyen, H., Nguyen, N., Tran, M. (2023). A dynamic leverage stochastic volatility model. Applied Economics Letters, 30(1), 97-102. [More Information]
Nguyen, N., Tran, M., Gunawan, D., Kohn, R. (2023). A Statistical Recurrent Stochastic Volatility Model for Stock Markets. Journal of Business and Economic Statistics, 41(2), 414-428. [More Information]
Virbickaite, A., Nguyen, H., Tran, M. (2023). Bayesian predictive distributions of oil returns using mixed data sampling volatility models. Resources Policy, 86, 104167. [More Information]
Lopatnikova, A., Tran, M. (2023). Quantum Variational Bayes on Manifolds. 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), United States: Institute of Electrical and Electronics Engineers (IEEE). [More Information]

show 1 more

2022

Jie, R., Gao, J., Vasnev, A., Tran, M. (2022). Adaptive hierarchical hyper-gradient descent. International Journal of Machine Learning and Cybernetics, 13(12), 3785-3805. [More Information]
Nguyen, N., Tran, M., Kohn, R. (2022). Recurrent Conditional Heteroskedasticity. Journal of Applied Econometrics, 37(5), 1031-1054. [More Information]
Gunawan, D., Hawkins, G., Tran, M., Kohn, R., Brown, S. (2022). Time-evolving psychological processes over repeated decisions. Psychological Review, 129(3), 438-456. [More Information]

2021

Yu, X., Nott, D., Tran, M., Klein, N. (2021). Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance. Journal of Computational and Graphical Statistics, 30(4), 977-990. [More Information]
Tran, M., Antic, A., Hassani-Mahmooei, B., Ozmen, M. (2021). Data Analytics and Insights. Melbourne, Australia: Wiley. [More Information]
Tung, D., Tran, M. (2021). Flexible multivariate regression density estimation. Communications in Statistics - Theory and Methods, 50(20), 4703-4717. [More Information]
Wall, L., Gunawan, D., Brown, S., Tran, M., Kohn, R., Hawkins, G. (2021). Identifying relationships between cognitive processes across tasks, contexts, and time. Behavior Research Methods, 53(1), 78-95. [More Information]
Zhou, B., Gao, J., Tran, M., Gerlach, R. (2021). Manifold optimization Assisted Gaussian Variational Approximation. Journal of Computational and Graphical Statistics, 30(4), 946-957. [More Information]
Jie, R., Gao, J., Vasnev, A., Tran, M. (2021). Regularized flexible activation function combination for deep neural networks. 25th International Conference on Pattern Recognition, ICPR 2020, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Tran, M., Scharth, M., Gunawan, D., Kohn, R., Brown, S., Hawkins, G. (2021). Robustly estimating the marginal likelihood for cognitive models via importance sampling. Behavior Research Methods, 53(3), 1148-1165. [More Information]
Quiroz, M., Tran, M., Villani, M., Kohn, R., Dang, K. (2021). The Block-Poisson Estimator for Optimally Tuned Exact Subsampling MCMC. Journal of Computational and Graphical Statistics, 30(4), 877-888. [More Information]
Tran, M., Nguyen, D., Nguyen, D. (2021). Variational Bayes on manifolds. Statistics and Computing, 31(6), 71. [More Information]

show 6 more

2020

Tran, M., Nguyen, N., Nott, D., Kohn, R. (2020). Bayesian Deep Net GLM and GLMM. Journal of Computational and Graphical Statistics, 29(1), 97-113. [More Information]
Jie, R., Gao, J., Vasnev, A., Tran, M. (2020). HyperTube: A Framework for Population-Based Online Hyperparameter Optimization with Resource Constraints. IEEE Access, 8, 69038-69057. [More Information]
Gunawan, D., Hawkins, G., Tran, M., Kohn, R., Brown, S. (2020). New estimation approaches for the hierarchical Linear Ballistic Accumulator model. Journal of Mathematical Psychology, 96, 102368. [More Information]
Salomone, R., Quiroz, M., Kohn, R., Villani, M., Tran, M. (2020). Spectral Subsampling MCMC for Stationary Time Series. 37th International Conference on Machine Learning (ICML 2020), Vienna: International Machine Learning Society.
Gunawan, D., Dang, K., Quiroz, M., Kohn, R., Tran, M. (2020). Subsampling sequential Monte Carlo for static Bayesian models. Statistics and Computing, 30(6), 1741-1758. [More Information]

show 2 more

2019

Tung, D., Tran, M., Cuong, T. (2019). Bayesian adaptive lasso with variational Bayes for variable selection in high-dimensional generalized linear mixed models. Communications in Statistics: Simulation and Computation, 48(2), 530-543. [More Information]
Gunawan, D., Tran, M., Suzuki, K., Dick, J., Kohn, R. (2019). Computationally Efficient Bayesian Estimation of High Dimensional Archimedian Copulas with Discrete and Mixed Margins. Statistics and Computing, 29(5), 933-946. [More Information]
Dang, K., Quiroz, M., Kohn, R., Tran, M., Villani, M. (2019). Hamiltonian Monte Carlo with Energy Conserving Subsampling. Journal of Machine Learning Research, 20, 1-31. [More Information]
Quiroz, M., Kohn, R., Villani, M., Tran, M. (2019). Speeding up MCMC by efficient data subsampling. Journal of the American Statistical Association, 114(526), 831-843. [More Information]

show 1 more

2018

Drovandi, C., Tran, M. (2018). Improving the Efficiency of Fully Bayesian Optimal Design of Experiments Using Randomised Quasi-Monte Carlo. Bayesian Analysis, 13(1), 139-162. [More Information]
Ong, V., Nott, D., Tran, M., Sisson, S., Drovandi, C. (2018). Likelihood-free inference in high dimensions with synthetic likelihood. Computational Statistics and Data Analysis, 128, 271-291. [More Information]
Quiroz, M., Tran, M., Villani, M., Kohn, R. (2018). Speeding up MCMC by delayed acceptance and data subsampling. Journal of Computational and Graphical Statistics, 27(1), 12-22. [More Information]
Quiroz, M., Kohn, R., Tran, M., Villani, M., Dang, K. (2018). Subsampling MCMC - an Introduction for the Survey Statistician. Sankhya: The Indian Journal of Statistics, 80-A (Supplement 1), 33-69. [More Information]
Ong, V., Nott, D., Tran, M., Sisson, S., Drovandi, C. (2018). Variational Bayes with Synthetic Likelihood. Statistics and Computing, 28(4), 971-988. [More Information]

show 2 more

2017

Tran, M., Nott, D., Kohn, R. (2017). Variational Bayes with Intractable Likelihood. Journal of Computational and Graphical Statistics, 26(4), 873-882. [More Information]

2016

Tran, M., Pitt, M., Kohn, R. (2016). Adaptive Metropolis-Hastings sampling using reversible dependent mixture proposals. Statistics and Computing, 26(1), 361-381. [More Information]
Tran, M., Nott, D., Kuk, A., Kohn, R. (2016). Parallel Variational Bayes for Large Datasets With an Application to Generalized Linear Mixed Models. Journal of Computational and Graphical Statistics, 25(2), 626-646. [More Information]

2015

Tran, M., Nott, D., Kohn, R. (2015). Variational Bayes with Intractable Likelihood. 5th Vietnam National Congress in Probability and Statistics, Da Nang, Vietnam: Vietnam Institute for Advanced Study in Mathematics.

2014

Leng, C., Tran, M., Nott, D. (2014). Bayesian adaptive Lasso. Annals of the Institute of Statistical Mathematics, 66(2), 221-244. [More Information]
Tran, M., Giordani, P., Mun, X., Kohn, R., Pitt, M. (2014). Copula-Type Estimators for Flexible Multivariate Density Modeling Using Mixtures. Journal of Computational and Graphical Statistics, 23(4), 1163-1178. [More Information]

2013

Tran, M. (2013). Adaptive Metropolis-Hastings sampling using reversible dependent mixture proposals. 8th Vietnamese Mathematical Conference, Nha Trang, Vietnam: Vietnam Institute for Advanced Study in Mathematics.
Giordani, P., Mun, X., Tran, M., Kohn, R. (2013). Flexible Multivariate Density Estimation with Marginal Adaptation. Journal of Computational and Graphical Statistics, 22(4), 814-829. [More Information]
Zhang, W., Xiaoxia, C., Tran, M. (2013). The structural features and the deliberative quality of online discussions. Telematics and Informatics, 30(2), 74-86. [More Information]

2012

Tran, M., Giordani, P., Kohn, R. (2012). Discussion of "Fast sparse regression and classification" by Jerome Friedman. International Journal of Forecasting, 28(3), 749-750. [More Information]
Tran, M., Nott, D. (2012). Simultaneous variable selection and component selection for regression density estimation with mixtures of heteroscedastic experts. Electronic Journal of Statistics, 6, 1170-1199. [More Information]
Nott, D., Marshall, L., Tran, M. (2012). The ensemble Kalman filter is an ABC algorithm. Statistics and Computing, 22(6), 1273-1276. [More Information]
Tran, M., Nott, D., Leng, C. (2012). The predictive Lasso. Statistics and Computing, 22(5), 1069-1084. [More Information]
Nott, D., Tran, M., Leng, C. (2012). Variational approximation for heteroscedastic linear models and matching pursuit algorithms. Statistics and Computing, 22(2), 497-512. [More Information]

show 2 more

2011

Tran, M. (2011). A criterion for optimal predictive model selection. Communications in Statistics - Theory and Methods, 40(5), 893-906. [More Information]
Tran, M. (2011). The loss rank criterion for variable selection in linear regression analysis. Scandinavian Journal of Statistics: theory and applications, 38(3), 466-479. [More Information]

2010

Hutter, M., Tran, M. (2010). Model selection with the Loss Rank Principle. Computational Statistics and Data Analysis, 54(5), 1288-1306. [More Information]

2009

Tran, M. (2009). Penalized Maximum Likelihood Principle for Choosing Ridge Parameter. Communications in Statistics: Simulation and Computation, 38(8), 1610-1624. [More Information]

Selected Grants

2022

ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights, Tran M, Australian Research Council (ARC)/ARC Centres of Excellence

2021

Quantum Computation for Business Analytics and Finance, Tran M, Sydney Business School/Business School Pilot Research Grant

2020

Bayesian inference for psychological theories with intractable likelihood, Kohn R, Tran M, Australian Research Council (ARC)/Discovery Projects (DP)
Deep learning based time series modeling and financial forecasting, Tran M, Gao J, Gerlach R, Australian Research Council (ARC)/Discovery Projects (DP)

2017

Flexible models and methods for cognitive model-based decision-making, Kohn R, Tran M, Australian Research Council (ARC)/Discovery Projects (DP)

The University of Sydney Business School

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Associate Professor Minh-Ngoc Tran

Selected publications

Publications

By Type

By Year

Selected Grants

2022

2021

2020

2017