Publications

1 Grants

1.  SSA funding fellowship award, $3000, 2021. 

2.  Bayesian Inference for Evidence Accumulation Models with Intractable Likelihoods, ARC Discovery Project (DP210103873), 2021-2023, $378500, Chief Investigator, led by UNSW. 

3.  ARC Research Hub for Transforming Energy Infrastructure through Digital Engineering, ARC industrial Transformation Research Hub (IH200100009), 2020-2024, $5000000, Chief Investigator, led by UWA.

4. ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) of Big Data, Big Models, New Insights (CE140100049), 2017-2021, $20000000, Associate Investigator, led by the University of Melbourne. 

5. Bayesian Inference for the Measurement of Socioeconomic Inequalities and Deprivations in Mental Health in Australia, RevITAlising (RITA) UOW Research Grants, 2021-2022, $17821.79, Chief Investigator (Individual Grant). 

6. Bayesian Inference for Cognitive Models with Intractable Likelihood, ACEMS Research Sprint Schemes, 2021, $27337.51, Chief Investigator.

7.  UoW internal start-up grant, 2020 - 2022, $10000, Chief Investigator (Individual Grant). 

8.  Research EIS Vouchers (REV), 2021, $1000, Chief Investigator (Individual Grant). 

9. Conference travel funding 2018, $3000 (UNSW) and 2019, $3000 (UNSW). 

10. International Research Collaboration Travel Funds (UNSW), 2019: $7000. 

11. National Computational Infrastructure (NCI via UNSW) funding 800 kSU (approximately 18K AUD) in 2018. 

12.  National Computational Infrastructure (NCI via UNSW) funding 1000 kSU (approximately 22K AUD) in 2019.

13. Awarded a scholarship to attend summer school on multidimensional poverty measurement organised by OPHI (Oxford Poverty and Human Development Initiative) in George Washington University in July 2013 ($750 for tuition fee).

2 Refereed Publications

1. Dao, V. H., Gunawan, D., Brown, S. D., Kohn, R., Tran, M. N., and Hawkins, G. .E. Bayesian inference for evidence accumulation models with regressors. In press with Psychological Methods.

2. Pearse, A., Cressie, N., and Gunawan, D. Optimal prediction of positive-values spatial processes: asymmetric power divergence loss. In press with Spatial Statistics.

3. Vu, B. A., Gunawan, D., and Zammit-Mangion, A. R-VGAL: a sequential variational Bayes algorithm for generalised linear mixed models. In press with Statistics and Computing.

4. Gunawan, D., Carter, C., and Kohn, R. The correlated particle hybrid sampler for state space models. Accepted at the Journal of Econometrics.

5. Chen, J., Milne, I., Taylor, P., Gunawan, D., Zhao, W. (2023). Weakly nonlinear surface wave prediction using a data-driven method with the help of physical understanding. In press with  Offshore, Mechanics, and Arctic Engineering Journal. 

6. Chatterjee, P., Gunawan, D., and Kohn, R. The interaction between credit constraints and uncertainty shocks. In press with the Journal of Money, Credit, and Banking.

7. Gunawan, D. Chatterjee, P., and Kohn, R. The block correlated pseudo marginal sampler for state  space models. Accepted at the Journal of Business and Economic Statistics. 

8. Gunawan, D., Kohn, R., and Nott, D., Flexible Variational Bayes based on a copula of a mixture. In press with Journal of Computational and Graphical Statistics.

9. Chen, J.,  Taylor, P., Milne, I, Gunawan, D., and Zhao, W. Wave-by-wave prediction for spread seas using a machine learning model with physical understanding. In press with Ocean Engineering.

10. Chen, J., Milne, I., Taylor, P., Gunawan, D., and Zhao, W. Forward prediction of surface wave elevations and motions of offshore floating structures using a data-driven model. In press with Ocean Engineering.

11. Gunawan, D., Griffiths, W. E., and Chotikapanich, D. A comparison of Australian Mental Health Distribution. In press with Australian and New Zealand Journal of Statistics.

12. Gunawan, D., Kohn, R., and Tran, M. N. Flexible and Robust Particle Density Tempering for State Space Models.  In press with Econometrics and Statistics.

13. Gunawan, D., Griffiths, W. E., and Chotikapanich, D. Inequality in Education: A Comparison of Australian Indigenous and Nonindigenous Populations. In press with Statistics, Politics, and Policy.

14. Nguyen, N., Tran, M. N., Gunawan, D., and Kohn, R. A Statistical Recurrent Stochastic Volatility Model for Stock Markets. In press with Journal of Business and Economic Statistics.

15. Gunawan, D., Hawkins, G., Tran, M. N., Kohn, R., and Brown, S. Time-evolving psychological processes over repeated decisions. In press with Psychological Review. 

16. Dao, V. H., Gunawan, D., Tran, M. N., Kohn, R., Hawkins, G.E., and Brown, S., Efficient Selection between Hierarchical Cognitive Models: Cross-validation with Variational Bayes. In press with Psychological Methods.

17. Gunawan, D. , Griffiths, W. E. , and Chotikapanich, D. Posterior probabilities for Lorenz and stochastic dominance of Australian income distributions. In press with  Economic Record.

18. Gunawan, D., Kohn R., and Nott, D. J. (2021),   Variational approximation for factor stochastic volatility models. International Journal of Forecasting, 37(4), 1355-1375.

19. Gunawan, D., Dang, K. D. , Quiroz, M., Kohn, R.  and Tran, M. N. (2020).  Subsampling sequential Monte Carlo for static Bayesian models. Statistics and Computing, 30(6), 1741-1758.

20. Gunawan, D., Khaled M., and Kohn, R. (2020). Mixed marginal copula modeling. Journal of Business and Economic Statistics, 38(1), 137-147.

21. Gunawan, D., Hawkins G., Tran M. N., Kohn, R., and Brown, S. (2020). New estimation approaches for the hierarchical linear ballistic accumulator model. Journal of Mathematical Psychology, 96.

22. Lander, D., Gunawan, D., Griffiths, W. E., and Chotikapanich, D. (2020). Bayesian assessment of Lorenz and stochastic dominance. Canadian Journal of Economics, 53(2), 767-799

23. Gunawan, D., Panagiotelis, A., Griffiths, W. E., and Chotikapanich, D. (2020). Bayesian weighted inference from surveys. Australian and New Zealand Journal of Statistics, 62(1), 71-94.

24. Mendes, E., Carter, C., Gunawan, D., and Kohn, R. (2020). A flexible particle Markov chain Monte Carlo method. Statistics and Computing, 30, 783-798.

25. Chin, V., Gunawan, D., Fiebig, D. G., Kohn, R., and Sisson, S. (2020). Efficient data augmentation for multivariate probit models with panel data: an application to general practitioner decision making about contraceptives. Journal of Royal Statistical Society Series C, 69(2), 277-300.

26. Tran, M. N., Scharth, M., Gunawan, D., Kohn, R., Brown, S., and Hawkins, G. (2020). Estimating marginal likelihood for cognitive models via importance sampling. Behavior Research Methods, 53(3), 1148-1165.

27. Wall, L., Gunawan, D., Brown, S., Tran, M. N., Kohn, R., and Hawkins, G. E. (2020). Identifying relationships between cognitive processes across tasks, contexts, and time. Behavior Research Methods, 53(1), 78-95.

28. Gunawan, D., Tran, M. N., Suzuki, K., Dick, J., and Kohn, R. (2019). Computationally efficient Bayesian estimation of high dimensional Archimedian copulas with discrete and mixed margins. Statistics and Computing, 29, 933-946.

29. Gunawan, D., Griffiths, W. E., and Chotikapanich, D. (2018). Bayesian inference for health inequality and welfare using qualitative data. Economics Letters, 162, 76-80. 

3 Refereed Book Chapters

1. Cressie, N., Pearse, A. R., and Gunawan, D (2021).  Optimal spatial prediction for non-negative spatial processes using a phi-divergence loss function.  in Balakrishnan, N. Gil, M., Martin, N., Morales, D., & del Carmen Pardo, M. (eds.), Trends in Mathematical, Information, and Data Sciences, Springer, New York. 

4 Refereed Conference Papers

1. Chen, J., Milne, I., Taylor, P., Gunawan, D., Zhao, W. (2023). Weakly nonlinear surface wave prediction using a data-driven method with the help of physical understanding. Proceedings of the ASME 2023 42nd International Conference on Ocean, Offshore, and Arctic Engineering OMAE2023.

2. Chen, J., Hlophe, T., Zhao, W., Milne, I., Gunawan, D., et al. Comparison of physics-based and machine learning methods for phase-resolved prediction of waves measured in the field. Proceedings of the 15th European Wave and Tidal Energy Conference. 

3. Chen, J., Gunawan, D., Zhao, W., Taylor, P., Chen, Y., Milne, I. An attention based deep learning model for phase resolved wave prediction. Proceedings of the ASME 2024 43rd International Conference on Ocean, Offshore, and Arctic Engineering OMAE2023

5 Submitted Papers

• Pearse, A., Gunawan, D., and Cressie, N. Bayesian inference from copula-based spatial hierarchical models. 

• Frazier, D., Kohn, R., Drovandi, C., and Gunawan, D. Reliable Bayesian inference in misspecified models.

• Gunawan, D., Griffiths, W. E., and Chotikapanich, D. Bayesian inference on the measurement of multidimensional welfare, inequality, and deprivation using copula. Submitted to the Journal of Business and Economic Statistics.

• Chen, J., Hlophe, T., Gunawan, D., Taylor, P. H., Milne, I. A., and Zhao, W. Local phase-resolved prediction of ocean waves: comparison between physics-based and machine-learning based methods. Submitted to Ocean Engineering journal.

• Chen, J., Gunawan, D., Taylor, P. H., Chen, Y., Milne, I. A., Zhao, W. An attention based deep learning model for phased resolved wave prediction. Submitted to Offshore, Mechanic, and Arctic Engineering journal. 

• Wijayawardhana, A., Suesse, T., and Gunawan, D. Statistical inference on hierarchical simultaneous autoregressive models with missing data. Submitted to Computational Statistics. 

• Gunawan, D., Nott, D., and Kohn, R. Fast variational boosting for latent variable models. 

• Gunawan, D., Nguyen, A., Kohn, R., Hawkins, G., and Brown, S. Dynamic Decision Making Models. 

6 Working Papers

• Gunawan, D., Tran, M. N., and Kohn, R., Fast inference for intractable likelihood problems using variational Bayes. 

• Choppala, P., Gunawan, D., Chen,  J., Tran, M. N., and Kohn, R.  Bayesian inference for state space models using block and correlated pseudo marginal Metropolis-Hastings. 

• Gunawan, D., Carter, C., Fiebig, D., and Kohn, R. Efficient Bayesian estimation for flexible panel models for multivariate outcomes: impact of life events on mental health and excessive alcohol consumption. 

• Francis, A., Gunawan, D., and Kohn, R. A variational Bayes approach to copula estimation.

7 Work in Progress

• Gunawan, D., Tran, M. N., Scharth, M., and Kohn, R. Efficient Bayesian inference for latent variable models using correlated and blocked pseudo marginal and efficient importance sampling.

• Gunawan, D., Pitt, M. K. and Kohn, R. Bayesian latent factor GARCH Model.

• Gunawan, D., and Kohn, R. On flexible particle Markov chain Monte Carlo for general time-varying multivariate factor model.

• Gunawan, D., Khaled, M., and Kohn, R. Approximate Bayesian computation for estimating high dimensional Archimedian copula with discrete marginals.