Projects per year
Personal profile
Biography
Personal Website: https://dtfrazier.netlify.com/
Research interests
Simulation-based inference in both the classical and Bayesian paradigm.
Financial Econometrics, with applications to asset pricing.
Nonparametric and semiparametric econometric modeling.
Monash teaching commitment
ETC 1010: Data Modeling and Computing
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Loss-based Bayesian Prediction
Maneesoonthorn, O. (Primary Chief Investigator (PCI)), Martin, G. (Chief Investigator (CI)), Frazier, D. (Chief Investigator (CI)) & Hyndman, R. (Chief Investigator (CI))
19/06/20 → 18/06/25
Project: Research
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Consequences of Model Misspecification in Approximate Bayesian Computation
Frazier, D. (Primary Chief Investigator (PCI))
Australian Research Council (ARC)
1/02/20 → 30/06/25
Project: Research
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Controlling Feedback in Big Multi-Module Statistical and Econometric Models
Smith, M. S. (Primary Chief Investigator (PCI)), Nott, D. J. (Partner Investigator (PI)) & Frazier, D. (Chief Investigator (CI))
Australian Research Council (ARC)
1/07/25 → 30/06/28
Project: Research
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The Validation of Approximate Bayesian Computation: Theory and Practice
Martin, G. (Primary Chief Investigator (PCI)), Frazier, D. (Chief Investigator (CI)), Renault, E. (Chief Investigator (CI)) & Robert, C. (Partner Investigator (PI))
Australian Research Council (ARC), Monash University, Brown University, Université Paris Dauphine (Paris Dauphine University)
1/02/17 → 31/12/21
Project: Research
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ABC-based forecasting in misspecified state space models
Weerasinghe, C., Loaiza-Maya, R., Martin, G. M. & Frazier, D. T., Jan 2025, In: International Journal of Forecasting. 41, 1, p. 270-289 20 p.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile -
Loss-based variational Bayes prediction
Frazier, D. T., Loaiza-Maya, R., Martin, G. M. & Koo, B., 2025, In: Journal of Computational and Graphical Statistics. 34, 1, p. 84-95 12 p.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile1 Citation (Scopus) -
Approximating Bayes in the 21st century
Martin, G. M., Frazier, D. T. & Robert, C. P., 2024, In: Statistical Science. 39, 1, p. 20-45 26 p.Research output: Contribution to journal › Review Article › Research › peer-review
15 Citations (Scopus) -
Bayesian forecasting in economics and finance: a modern review
Martin, G. M., Frazier, D. T., Maneesoonthorn, W., Loaiza-Maya, R., Huber, F., Koop, G., Maheu, J., Nibbering, D. & Panagiotelis, A., Apr 2024, In: International Journal of Forecasting. 40, 2, p. 811-839 29 p.Research output: Contribution to journal › Review Article › Research › peer-review
11 Citations (Scopus) -
Bayesian inference for misspecified generative models
Nott, D. J., Drovandi, C. C. & Frazier, D. T., 22 Apr 2024, In: Annual Review of Statistics and Its Application. 11, 1, p. 179-202 24 p.Research output: Contribution to journal › Review Article › Research › peer-review
5 Citations (Scopus)