Staff profile
Overview
Dr Jonathan Cumming
Director of SMCU, Associate Professor, Statistics
Affiliation | Telephone |
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Director of SMCU, Associate Professor, Statistics in the Department of Mathematical Sciences | +44 (0) 191 33 43124 |
Research interests
- Statistics
- Applied Statistics
- Uncertainty Analysis
- Statistical Computation
- Variable Selection
Publications
Chapter in book
- Hasan, M. M., & Cumming, J. A. (2021). Bayes Linear Emulation of Simulated Crop Yield. In Y. P. Chaubey, S. Lahmiri, F. Nebebe, & A. Sen (Eds.), Applied Statistics and Data Science:Proceedings of Statistics 2021 Canada, Selected Contributions (145-151). Springer Verlag. https://doi.org/10.1007/978-3-030-86133-9_7
- Errington, A., Einbeck, J., & Cumming, J. (2021). Estimating Exposure Fraction from Radiation Biomarkers: A Comparison of Frequentist and Bayesian Approaches. In M. Vasile, & D. Quagliarella (Eds.), Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications (393-405). Springer Verlag. https://doi.org/10.1007/978-3-030-80542-5_24
- Cumming, J., & Goldstein, M. (2010). Bayes linear Uncertainty Analysis for Oil Reservoirs Based on Multiscale Computer Experiments. In A. O'Hagan, & M. West (Eds.), The Oxford handbook of applied Bayesian analysis (241-270). Oxford University Press
Conference Paper
- Jaffrezic, V., Razminia, K., Cumming, J., & Gringarten, A. (in press). Field Applications of Constrained Multiwell Deconvolution. . https://doi.org/10.2118/195516-ms
- Aluko, L., Cumming, J., & Gringarten, A. (in press). Using Deconvolution to Estimate Unknown Well Production from Scarce Wellhead Pressure Data. . https://doi.org/10.2118/201667-ms
- Cumming, J., Botsas, T., Jermyn, I., & Gringarten, A. (2020). Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach. In SPE Virtual Europec 2020 ; proceedings (SPE-200617-MS). https://doi.org/10.2118/200617-ms
- Cumming, J., Jaffrezic, V., Whittle, T., & Gringarten, A. (2019). Constrained Least-Squares Multiwell Deconvolution. In Proceedings of the SPE Western Regional Meeting 2019. https://doi.org/10.2118/195271-ms
- Tung, Y., Virues, C., Cumming, J., & Gringarten, A. (2016). Multiwell Deconvolution for Shale Gas. . https://doi.org/10.2118/180158-ms
- Thornton, E., Mazloom, J., Gringarten, A., & Cumming, J. (2015). Application of Multiple Well Deconvolution Method in a North Sea Field. . https://doi.org/10.2118/174353-ms
- Cumming, J., Wooff, D., Whittle, T., Crossman, R., & Gringarten, A. (2013). Assessing the Non-Uniqueness of the Well Test Interpretation Model Using Deconvolution. . https://doi.org/10.2118/164870-ms
- Cumming, J., Wooff, D., Whittle, T., & Gringarten, A. (2013). Multiple Well Deconvolution. . https://doi.org/10.2118/166458-ms
Doctoral Thesis
Journal Article
- Botsas, T., Cumming, J., & Jermyn, I. (2022). A Bayesian multi-region radial composite reservoir model for deconvolution in well test analysis. Journal of the Royal Statistical Society: Series C, 71(4), 951-968. https://doi.org/10.1111/rssc.12562
- Errington, A., Einbeck, J., Cumming, J., Rössler, U., & Endesfelder, D. (2022). The effect of data aggregation on dispersion estimates in count data models. International Journal of Biostatistics, 18(1), 183-202. https://doi.org/10.1515/ijb-2020-0079
- Goldie, S. J., Bush, S., Cumming, J. A., & Coleman, K. S. (2020). Statistical Approach to Raman Analysis of Graphene-Related Materials: Implications for Quality Control. ACS Applied Nano Material, 3(11), 11229-11239. https://doi.org/10.1021/acsanm.0c02361
- Vernon, I., Jackson, S., & Cumming, J. (2019). Known Boundary Emulation of Complex Computer Models. SIAM/ASA Journal on Uncertainty Quantification, 7(3), 838-876. https://doi.org/10.1137/18m1164457
- Cumming, J., Wooff, D., Whittle, T., & Gringarten, A. (2014). Multiwell Deconvolution. SPE Reservoir Evaluation & Engineering, 17(04), 457-465. https://doi.org/10.2118/166458-pa
- Cumming, J., & Goldstein, M. (2009). Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations. Technometrics, 51(4), 377-388. https://doi.org/10.1198/tech.2009.08015
- Cumming, J., & Wooff, D. (2007). Dimension reduction via principal variables. Computational Statistics & Data Analysis, 52(1), 550-565. https://doi.org/10.1016/j.csda.2007.02.012
Presentation
Report