| Auxiliary likelihood-based approximate Bayesian computation in state space models GM Martin, BPM McCabe, DT Frazier, W Maneesoonthorn, CP Robert Journal of Computational and Graphical Statistics 28 (3), 508-522, 2019 | 50 | 2019 |
| Approximate bayesian forecasting DT Frazier, W Maneesoonthorn, GM Martin, BPM McCabe International Journal of Forecasting 35 (2), 521-539, 2019 | 49 | 2019 |
| Inference on self‐exciting jumps in prices and volatility using high‐frequency measures W Maneesoonthorn, CS Forbes, GM Martin Journal of Applied Econometrics 32 (3), 504-532, 2017 | 44 | 2017 |
| Time series copulas for heteroskedastic data R Loaiza‐Maya, MS Smith, W Maneesoonthorn Journal of Applied Econometrics 33 (3), 332-354, 2018 | 43 | 2018 |
| High-frequency jump tests: Which test should we use? W Maneesoonthorn, GM Martin, CS Forbes Journal of Econometrics 219 (2), 478-487, 2020 | 30 | 2020 |
| Bayesian forecasting in economics and finance: A modern review GM Martin, DT Frazier, W Maneesoonthorn, R Loaiza-Maya, F Huber, ... International Journal of Forecasting 40 (2), 811-839, 2024 | 26 | 2024 |
| Probabilistic forecasts of volatility and its risk premia W Maneesoonthorn, GM Martin, CS Forbes, SD Grose Journal of Econometrics 171 (2), 217-236, 2012 | 24 | 2012 |
| Optimal probabilistic forecasts: When do they work? GM Martin, R Loaiza-Maya, W Maneesoonthorn, DT Frazier, ... International Journal of Forecasting 38 (1), 384-406, 2022 | 21 | 2022 |
| Inversion copulas from nonlinear state space models with an application to inflation forecasting MS Smith, W Maneesoonthorn International Journal of Forecasting 34 (3), 389-407, 2018 | 21 | 2018 |
| Approximate Bayesian computation in state space models GM Martin, BPM McCabe, W Maneesoonthorn, CP Robert arXiv preprint arXiv:1409.8363, 2014 | 20 | 2014 |
| ABC of the future H Pesonen, U Simola, A Köhn‐Luque, H Vuollekoski, X Lai, A Frigessi, ... International Statistical Review 91 (2), 243-268, 2023 | 11 | 2023 |
| Inversion copulas from nonlinear state space models MS Smith, W Maneesoonthorn arXiv preprint arXiv:1606.05022, 2016 | 6 | 2016 |
| Large skew-t copula models and asymmetric dependence in intraday equity returns L Deng, MS Smith, W Maneesoonthorn Journal of Business & Economic Statistics, 1-17, 2024 | 3 | 2024 |
| Efficient Variational Inference for Large Skew-t Copulas with Application to Intraday Equity Returns L Deng, MS Smith, W Maneesoonthorn arXiv preprint arXiv:2308.05564, 2023 | 2 | 2023 |
| Bayesian Forecasting in the 21st Century: A Modern Review GM Martin, DT Frazier, R Loaiza-Maya, F Huber, G Koop, J Maheu, ... arXiv preprint arXiv:2212.03471, 2022 | 2 | 2022 |
| The predictive ability of quarterly financial statements H Zhou, WO Maneesoonthorn, XB Chen International Journal of Financial Studies 9 (3), 50, 2021 | 2 | 2021 |
| Natural gradient hybrid variational inference with application to deep mixed models W Zhang, M Smith, W Maneesoonthorn, R Loaiza-Maya Statistics and Computing 34 (6), 185, 2024 | 1 | 2024 |
| Discussion of ‘Deep learning for finance: deep portfolios’ CS Forbes, W Maneesoonthorn Applied Stochastic Models in Business and Industry 33 (1), 13-15, 2017 | 1 | 2017 |
| Improved Density Forecasts Using Mixed Frequency Data: A Focused Bayesian Approach R Loaiza Maya, WO Maneesoonthorn, AJ Patton Worapree Ole and Patton, Andrew J, 2024 | | 2024 |
| Probabilistic Predictions of Option Prices Using Multiple Sources of Data W Maneesoonthorn, DT Frazier, GM Martin arXiv preprint arXiv:2412.00658, 2024 | | 2024 |
Gael M. MartinEmeritus Professor, Monash UniversityPatvirtintas el. paštas monash.edu
