Staff profile

• Applied Statistics
• Biostatistics
• Machine Learning and Causal Inference
• Missing Data Methodology
• Models for Rare Events

Edited book

• Developments in Statistical Modelling
  Einbeck, J., Maeng, H., Ogundimu, E., & Perrakis, K. (Eds.). (2024). Developments in Statistical Modelling. Springer Nature. https://doi.org/10.1007/978-3-031-65723-8

Journal Article

• Understanding Repetitive Behaviours: 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., Kharati, E., 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., Rob, P., …Rodgers, J. (online). Understanding Repetitive Behaviours: A clinical and cost-effectiveness, multi-site randomised controlled trial of a group for parents and carers of young autistic children. Autism, https://doi.org/10.1177/13623613251333175
• 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 scoring
  Ogundimu, 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 model
  Ogundimu, 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 events
  Ogundimu, E. O. (2019). Prediction of default probability by using statistical models for rare events. Journal of the Royal Statistical Society: Series A, 182(4), 1143-1162. https://doi.org/10.1111/rssa.12467
• A robust imputation method for missing responses and covariates in sample selection models
  Ogundimu, 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 outcomes
  Ogundimu, 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 model
  Collins, 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 Distribution
  Ogundimu, 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 study
  Collins, 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 distributions
  Rubio, F., Ogundimu, E., & Hutton, J. (2016). On modelling asymmetric data using two-piece sinh–arcsinh distributions. Brazilian Journal of Probability and Statistics (Impresso), 30(3), https://doi.org/10.1214/15-bjps290
• A unified approach to multilevel sample selection models
  Ogundimu, 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
• Sensitivity analyses for partially observed recurrent event data
  Akacha, M., & Ogundimu, E. O. (2016). Sensitivity analyses for partially observed recurrent event data. Pharmaceutical Statistics, 15(1), https://doi.org/10.1002/pst.1720
• Adequate sample size for developing prediction models is not simply related to events per variable
  Ogundimu, 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
• On the extended two-parameter generalized skew-normal distribution
  Ogundimu, 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