All Outputs (7)

A Bayesian multi-region radial composite reservoir model for deconvolution in well test analysis (2022)
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

In petroleum well test analysis, deconvolution is used to obtain information about the reservoir system. This information is contained in the response function, which can be estimated by solving an inverse problem in the pressure and flow rate measur... Read More about A Bayesian multi-region radial composite reservoir model for deconvolution in well test analysis.

The effect of data aggregation on dispersion estimates in count data models (2021)
Journal Article
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

For the modelling of count data, aggregation of the raw data over certain subgroups or predictor configurations is common practice. This is, for instance, the case for count data biomarkers of radiation exposure. Under the Poisson law, count data can... Read More about The effect of data aggregation on dispersion estimates in count data models.

Statistical Approach to Raman Analysis of Graphene-Related Materials: Implications for Quality Control (2020)
Journal Article
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

A statistical method to determine the number of measurements required from nanomaterials to ensure reliable and robust analysis is described. Commercial products utilizing graphene are in their infancy and recent investigations of commercial graphene... Read More about Statistical Approach to Raman Analysis of Graphene-Related Materials: Implications for Quality Control.

Known Boundary Emulation of Complex Computer Models (2019)
Journal Article
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

Computer models are now widely used across a range of scientific disciplines to describe various complex physical systems, however to perform full uncertainty quantification we often need to employ emulators. An emulator is a fast statistical constru... Read More about Known Boundary Emulation of Complex Computer Models.

Multiwell Deconvolution (2014)
Journal Article
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

In well-test analysis, deconvolution is used to transform variablerate-pressure data into a single constant-rate drawdown suitable for interpretation. It is becoming part of a standard workflow for exploration and appraisal well-test analyses and in... Read More about Multiwell Deconvolution.

Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations (2009)
Journal Article
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

We consider the problem of designing for complex high-dimensional computer models that can be evaluated at different levels of accuracy. Ordinarily, this requires performing many expensive evaluations of the most accurate version of the computer mode... Read More about Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations.

Dimension reduction via principal variables (2007)
Journal Article
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

For many large-scale datasets it is necessary to reduce dimensionality to the point where further exploration and analysis can take place. Principal variables are a subset of the original variables and preserve, to some extent, the structure and info... Read More about Dimension reduction via principal variables.