Staff profile
Overview
https://apps.dur.ac.uk/biography/image/2549
Affiliation | Telephone |
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Associate Professor in the Department of Mathematical Sciences | |
Fellow of the Wolfson Research Institute for Health and Wellbeing |
Research interests
- Applied Statistics
- Biostatistics
- Machine Learning and Causal Inference
- Missing Data Methodology
- Models for Rare Events
Publications
Edited book
- Developments in Statistical ModellingEinbeck, J., Maeng, H., Ogundimu, E., & Perrakis, K. (Eds.). (2024). Developments in Statistical Modelling. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-65723-8
Journal Article
- Understanding Repetitive behaviours (URB): a clinical and cost effectiveness, multi-site randomised controlled trial of a group for parents and carers of young autistic children.Grahame, V., Kernohan, A., Ehsan Kharati, K., Mathias, A., Butcher, C., Dixon, L., Fletcher-Watson, S., Garland, D., Glod, M., Goodwin, J., Heron, S., Honey, E., Le Couteur, A., Mackie, L., Maxwell, J., Montgomery, L., Ogundimu, E., Probert, H., Riby, D., … Rodgers, J. (in press). Understanding Repetitive behaviours (URB): a clinical and cost effectiveness, multi-site randomised controlled trial of a group for parents and carers of young autistic children. Autism.
- Nationally Automated Colonoscopy Performance Feedback Increases Polyp Detection: the NED APRIQOT Randomised Controlled Trial.Catlow, J., Sharp, L., Wagnild, J., Lu, L., Bhardwaj-Gosling, R., Ogundimu, E., Kasim, A., Brookes, M., Lee, T., McCarthy, S., Gray, J., Sniehotta, F., Valori, R., Westwood, C., McNally, R., Ruwende, J., Sinclair, S., Deane, J., & Rutter, M. (2024). Nationally Automated Colonoscopy Performance Feedback Increases Polyp Detection: the NED APRIQOT Randomised Controlled Trial. Clinical Gastroenterology and Hepatology, 22(9), 1926-1936. https://doi.org/10.1016/j.cgh.2024.03.048
- On Lasso and adaptive Lasso for non-random sample in credit scoringOgundimu, E. O. (2024). On Lasso and adaptive Lasso for non-random sample in credit scoring. Statistical Modelling, 24(2), 115-138. https://doi.org/10.1177/1471082x221092181
- Regularization and variable selection in Heckman selection modelOgundimu, E. O. (2022). Regularization and variable selection in Heckman selection model. Statistical Papers, 63(2), 421-439. https://doi.org/10.1007/s00362-021-01246-z
- Prediction of default probability by using statistical models for rare eventsOgundimu, E. O. (2019). Prediction of default probability by using statistical models for rare events. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(4), 1143-1162. https://doi.org/10.1111/rssa.12467
- A robust imputation method for missing responses and covariates in sample selection modelsOgundimu, E. O., & Collins, G. S. (2019). A robust imputation method for missing responses and covariates in sample selection models. Statistical Methods in Medical Research, 28(1), 102-116. https://doi.org/10.1177/0962280217715663
- Predictive performance of penalized beta regression model for continuous bounded outcomesOgundimu, E. O., & Collins, G. S. (2018). Predictive performance of penalized beta regression model for continuous bounded outcomes. Journal of Applied Statistics, 45(6), 1030-1040. https://doi.org/10.1080/02664763.2017.1339024
- Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic modelCollins, G. S., Ogundimu, E. O., Cook, J. A., Manach, Y. L., & Altman, D. G. (2016). Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model. Statistics in Medicine, 35(23), 4124-4135. https://doi.org/10.1002/sim.6986
- A Sample Selection Model with Skew-normal DistributionOgundimu, E. O., & Hutton, J. L. (2016). A Sample Selection Model with Skew-normal Distribution. Scandinavian Journal of Statistics, 43(1), 172-190. https://doi.org/10.1111/sjos.12171
- Sample size considerations for the external validation of a multivariable prognostic model: a resampling studyCollins, G. S., Ogundimu, E. O., & Altman, D. G. (2016). Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Statistics in Medicine, 35(2), 214-226. https://doi.org/10.1002/sim.6787
- On modelling asymmetric data using two-piece sinh–arcsinh distributionsRubio, F., Ogundimu, E., & Hutton, J. (2016). On modelling asymmetric data using two-piece sinh–arcsinh distributions. Brazilian Journal of Probability and Statistics, 30(3). https://doi.org/10.1214/15-bjps290
- Adequate sample size for developing prediction models is not simply related to events per variableOgundimu, E. O., Altman, D. G., & Collins, G. S. (2016). Adequate sample size for developing prediction models is not simply related to events per variable. Journal of Clinical Epidemiology, 76. https://doi.org/10.1016/j.jclinepi.2016.02.031
- Sensitivity analyses for partially observed recurrent event dataAkacha, M., & Ogundimu, E. O. (2016). Sensitivity analyses for partially observed recurrent event data. Pharmaceutical Statistics, 15(1). https://doi.org/10.1002/pst.1720
- A unified approach to multilevel sample selection modelsOgundimu, E. O., & Hutton, J. L. (2016). A unified approach to multilevel sample selection models. Communications in Statistics - Theory and Methods, 45(9), 2592-2611. https://doi.org/10.1080/03610926.2014.887108
- On the extended two-parameter generalized skew-normal distributionOgundimu, E. O., & Hutton, J. L. (2015). On the extended two-parameter generalized skew-normal distribution. Statistics and Probability Letters, 100, 142-148. https://doi.org/10.1016/j.spl.2015.02.016