| Forecasting for COVID-19 has failed JPA Ioannidis, S Cripps, MA Tanner International journal of forecasting 38 (2), 423-438, 2022 | 543 | 2022 |
| Reporting requirements, targets, and quotas for women in leadership VE Sojo, RE Wood, SA Wood, MA Wheeler The Leadership Quarterly 27 (3), 519-536, 2016 | 214 | 2016 |
| The differential impact of major life events on cognitive and affective wellbeing N Kettlewell, RW Morris, N Ho, DA Cobb-Clark, S Cripps, N Glozier SSM-population health 10, 100533, 2020 | 159 | 2020 |
| Variable selection and function estimation in additive nonparametric regression using a data-based prior TS Shively, R Kohn, S Wood Journal of the American Statistical Association 94 (447), 777-794, 1999 | 122 | 1999 |
| Bayesian mixture of splines for spatially adaptive nonparametric regression SA Wood, W Jiang, M Tanner Biometrika 89 (3), 513-528, 2002 | 117 | 2002 |
| A case study in model failure? COVID-19 daily deaths and ICU bed utilisation predictions in New York state: V. Chin et al. V Chin, NI Samia, R Marchant, O Rosen, JPA Ioannidis, MA Tanner, ... European Journal of Epidemiology 35 (8), 733-742, 2020 | 98 | 2020 |
| Bayesian variable selection and model averaging in high-dimensional multinomial nonparametric regression P Yau, R Kohn, S Wood Journal of Computational and Graphical Statistics 12 (1), 23-54, 2003 | 88 | 2003 |
| AdaptSPEC: Adaptive spectral estimation for nonstationary time series O Rosen, S Wood, DS Stoffer Journal of the American Statistical Association 107 (500), 1575-1589, 2012 | 83 | 2012 |
| Effect estimates of COVID-19 non-pharmaceutical interventions are non-robust and highly model-dependent V Chin, JPA Ioannidis, MA Tanner, S Cripps Journal of Clinical Epidemiology 136, 96-132, 2021 | 81* | 2021 |
| A Bayesian approach to robust binary nonparametric regression S Wood, R Kohn Journal of the American Statistical Association 93 (441), 203-213, 1998 | 79 | 1998 |
| Local spectral analysis via a Bayesian mixture of smoothing splines O Rosen, DS Stoffer, S Wood Journal of the American Statistical Association 104 (485), 249-262, 2009 | 65 | 2009 |
| Model selection in spline nonparametric regression S Wood, R Kohn, T Shively, W Jiang Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2002 | 58 | 2002 |
| Langevin-gradient parallel tempering for Bayesian neural learning R Chandra, K Jain, RV Deo, S Cripps Neurocomputing 359, 315-326, 2019 | 50 | 2019 |
| Learning as we go: An examination of the statistical accuracy of COVID19 daily death count predictions R Marchant, NI Samia, O Rosen, MA Tanner, S Cripps arXiv preprint arXiv:2004.04734, 2020 | 48 | 2020 |
| Bayesian mixtures of autoregressive models S Wood, O Rosen, R Kohn Journal of Computational and Graphical Statistics 20 (1), 174-195, 2011 | 38 | 2011 |
| Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0. 1.2: Setting up for success R Scalzo, D Kohn, H Olierook, G Houseman, R Chandra, M Girolami, ... Geoscientific Model Development 12 (7), 2941-2960, 2019 | 37 | 2019 |
| Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression R Marchant, S Haan, G Clancey, S Cripps Security Informatics 7 (1), 1-19, 2018 | 33 | 2018 |
| Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics J Pall, R Chandra, D Azam, T Salles, JM Webster, R Scalzo, S Cripps Environmental Modelling & Software 125, 104610, 2020 | 30 | 2020 |
| BayesLands: A Bayesian inference approach for parameter uncertainty quantification in Badlands R Chandra, D Azam, RD Müller, T Salles, S Cripps Computers & Geosciences 131, 89-101, 2019 | 26 | 2019 |
| Precipitation reconstruction from climate-sensitive lithologies using Bayesian machine learning R Chandra, S Cripps, N Butterworth, RD Muller Environmental Modelling & Software 139, 105002, 2021 | 24 | 2021 |
Roman MarchantAssociate Professor, Human Technology Institute, University of Technology SydneyVerified email at uts.edu.au
