Staff profile

• Statistics
• Applied Statistics
• Uncertainty Analysis
• Statistical Computation
• Variable Selection

Chapter in book

• Bayes Linear Emulation of Simulated Crop Yield
  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
• Estimating Exposure Fraction from Radiation Biomarkers: A Comparison of Frequentist and Bayesian Approaches
  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
• Bayes linear Uncertainty Analysis for Oil Reservoirs Based on Multiscale Computer Experiments
  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

• Using Deconvolution to Estimate Unknown Well Production from Scarce Wellhead Pressure Data
  Aluko, L., Cumming, J., & Gringarten, A. (2020, December). Using Deconvolution to Estimate Unknown Well Production from Scarce Wellhead Pressure Data. Presented at SPE Annual Technical Conference and Exhibition, Virtual
• Field Applications of Constrained Multiwell Deconvolution
  Jaffrezic, V., Razminia, K., Cumming, J., & Gringarten, A. (2019, December). Field Applications of Constrained Multiwell Deconvolution. Presented at SPE Europec featured at 81st EAGE Conference and Exhibition, London, UK
• A Bayesian non-linear hierarchical framework for crop models based on big data outputs
  Hasan, M. M., & Cumming, J. (2020, December). A Bayesian non-linear hierarchical framework for crop models based on big data outputs. Paper presented at 13th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2020), King's College London, England
• Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach
  Cumming, J., Botsas, T., Jermyn, I., & Gringarten, A. (2020, December). Assessing the Non-Uniqueness of a Well Test Interpretation Model Using a Bayesian Approach. Presented at SPE Virtual Europec 2020
• Constrained Least-Squares Multiwell Deconvolution
  Cumming, J., Jaffrezic, V., Whittle, T., & Gringarten, A. (2019, August). Constrained Least-Squares Multiwell Deconvolution. Presented at SPE Western Regional Meeting, San Jose, California, USA
• Multiwell Deconvolution for Shale Gas
  Tung, Y., Virues, C., Cumming, J., & Gringarten, A. (2016, May). Multiwell Deconvolution for Shale Gas. Presented at SPE Europec featured at 78th EAGE Conference and Exhibition, Vienna, Austria
• Application of Multiple Well Deconvolution Method in a North Sea Field
  Thornton, E., Mazloom, J., Gringarten, A., & Cumming, J. (2015, December). Application of Multiple Well Deconvolution Method in a North Sea Field. Presented at EUROPEC 2015, Madrid, Spain
• Multiple Well Deconvolution
  Cumming, J., Wooff, D., Whittle, T., & Gringarten, A. (2013, December). Multiple Well Deconvolution. Presented at 2013 SPE Annual Technical Conference & Exhibition, New Orleans, USA
• Assessing the Non-Uniqueness of the Well Test Interpretation Model Using Deconvolution
  Cumming, J., Wooff, D., Whittle, T., Crossman, R., & Gringarten, A. (2013, December). Assessing the Non-Uniqueness of the Well Test Interpretation Model Using Deconvolution. Presented at 75th EAGE Annual Conference & Exhibition, 10–13 June 2013, London, United Kingdom

Doctoral Thesis

• Clinical Decision Support
  Cumming, J. Clinical Decision Support. (Thesis). Department of Mathematical Sciences, Durham University. https://durham-repository.worktribe.com/output/1642189

Journal Article

• Systematic structural discrepancy assessment for computer models
  Goldstein, M., Vernon, I., & Cumming, J. A. (2025). Systematic structural discrepancy assessment for computer models. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 383(2293), Article 20240214. https://doi.org/10.1098/rsta.2024.0214
• A Bayesian multi-region radial composite reservoir model for deconvolution in well test analysis
  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
• The effect of data aggregation on dispersion estimates in count data models
  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
• Statistical Approach to Raman Analysis of Graphene-Related Materials: Implications for Quality Control
  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
• Known Boundary Emulation of Complex Computer Models
  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
• Multiwell Deconvolution
  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
• Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations
  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
• Dimension reduction via principal variables
  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

Report

• Understanding the accuracy of pre-symptomatic diagnosis of sepsis
  Cumming, J., Riseth, A., & Williams, J. (2016). Understanding the accuracy of pre-symptomatic diagnosis of sepsis. [No known commissioning body]