Staff profile

About me:

I am an Associate Professor of Statistics in the Department of Mathematical Sciences at Durham University. Previously, I had academic positions at Imperial College, Manchester and KU Leuven. My research mainly focuses on high dimensional statistics, change point analysis for high dimensional data, statistical modelling and inference, longitudinal data analysis, linear and nonlinear mixed models, nonparametric methods, survival analysis and model diagnostics. A major focus of my research has been on developing novel statistical methods and models for the analysis of complex data such as high dimensional data and multilevel/longitudinal data, especially from medical and health research as well as finance and social science.

I serve as Associate Editor for "Statistics & Probability Letters" journal.

I achieved Fellowship of the Higher Education Academy in 2020. This term I am teaching the module "High Dimensional Statistics" for final year maths and statistics students. Last term I taught the MSc module "Models and Methods for Health Data Science". 

If you are interested in doing a PhD in Statistics or Data Science, please get in touch.

• High dimensional statistics
• Change point analysis for high dimensional data
• Statistical modelling and inference
• Longitudinal data analysis
• Biostatistics
• Survival analysis

Conference Paper

• Challenges in high dimensional change point analysis and advanced approaches
  
  Albalawi, S., & Drikvandi, R. (2024). Challenges in high dimensional change point analysis and advanced approaches. In Proceedings of the 6th International Conference on Statistics: Theory and Applications (ICSTA 2024). https://doi.org/10.11159/icsta24.121
• High dimensional change points: challenges and some proposals
  
  Zhang, L., & Drikvandi, R. (2023, August 5). High dimensional change points: challenges and some proposals. Presented at 5th International Conference on Statistics: Theory and Applications (ICSTA 2023). https://doi.org/10.11159/icsta23.142
• A multilevel multivariate response model for data with latent structures
  
  Zhang, Y., Einbeck, J., & Drikvandi, R. (2023, July 21). A multilevel multivariate response model for data with latent structures. Presented at The 37th International Workshop on Statistical Modelling, Dortmund, Germany.
• Diagnostic tools for random effects in general mixed models
  
  Drikvandi, R. (2021). Diagnostic tools for random effects in general mixed models. Presented at The 14th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2021), London, UK.
• Invited session "Recent advances in biostatistics"
  
  Drikvandi, R. (2020). Invited session "Recent advances in biostatistics". Presented at The 13th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2020), London, UK.
• A novel method for analysis of high dimensional data
  
  Drikvandi, R. (2019). A novel method for analysis of high dimensional data. Presented at The Royal Statistical Society International Conference 2019, Belfast, UK.
• Joint modelling of longitudinal data involving time-varying covariates
  
  Drikvandi, R. (2019). Joint modelling of longitudinal data involving time-varying covariates. Presented at The 12th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2019), London, UK.
• A joint mixed model for longitudinal data involving time-varying covariates
  
  Drikvandi, R. (2017). A joint mixed model for longitudinal data involving time-varying covariates. Presented at The 17th Conference of the Applied Stochastic Models and Data Analysis (ASMDA) International Society, London, UK.
• A joint semiparametric mixed model for longitudinal data involving time-varying covariates
  
  Drikvandi, R. (2016). A joint semiparametric mixed model for longitudinal data involving time-varying covariates. Presented at The Royal Statistical Society International Conference 2016, Manchester, UK.
• Assessing the random effects part of mixed models
  
  Drikvandi, R. (2015). Assessing the random effects part of mixed models. Presented at The 8th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2015), London, UK.
• Diagnosing misspecication of the random-effects distribution in mixed models
  
  Drikvandi, R. (2014). Diagnosing misspecication of the random-effects distribution in mixed models. Presented at The 22nd Conference of the Belgian Statistical Society, Louvain-la-Neuve, Belgium.

Conference Proceeding

• Proceedings of the 38th International Workshop on Statistical Modelling
  
  Einbeck, J., Drikvandi, R., Karagiannis, G., Perrakis, K., & Zhang, Q. (Eds.). (2024). Proceedings of the 38th International Workshop on Statistical Modelling. Durham University.

Journal Article

• High dimensional regression with many nuisance parameters: both cases of specified and unspecified parameters of interest
  
  Drikvandi, R. (in press). High dimensional regression with many nuisance parameters: both cases of specified and unspecified parameters of interest. Electronic Journal of Statistics.
• A two-level multivariate response model for data with latent structures
  
  Zhang, Y., Einbeck, J., & Drikvandi, R. (2025). A two-level multivariate response model for data with latent structures. Statistical Modelling. Advance online publication. https://doi.org/10.1177/1471082X241313024
• A distribution-free method for change point detection in non-sparse high dimensional data
  
  Drikvandi, R., & Modarres, R. (2024). A distribution-free method for change point detection in non-sparse high dimensional data. Journal of Computational and Graphical Statistics. Advance online publication. https://doi.org/10.1080/10618600.2024.2365733
• A framework for analysing longitudinal data involving time-varying covariates
  
  Drikvandi, R., Verbeke, G., & Molenberghs, G. (2024). A framework for analysing longitudinal data involving time-varying covariates. Annals of Applied Statistics, 18(2), 1618-1641. https://doi.org/10.1214/23-AOAS1851
• Exploring the relationship between government stringency and preventative social behaviours during the COVID-19 pandemic in the United Kingdom.
  
  Al-Zubaidy, N., Fernandez Crespo, R., Jones, S., Drikvandi, R., Gould, L., Leis, M., Maheswaran, H., Neves, A. L., & Darzi, A. (2023). Exploring the relationship between government stringency and preventative social behaviours during the COVID-19 pandemic in the United Kingdom. Health Informatics Journal, 29(4), Article 14604582231215867. https://doi.org/10.1177/14604582231215867
• Sparse principal component analysis for natural language processing
  
  Drikvandi, R., & Lawal, O. (2023). Sparse principal component analysis for natural language processing. Annals of Data Science, 10(1), 25-41. https://doi.org/10.1007/s40745-020-00277-x
• MEGH: A parametric class of general hazard models for clustered survival data
  
  Rubio, J., & Drikvandi, R. (2022). MEGH: A parametric class of general hazard models for clustered survival data. Statistical Methods in Medical Research, 31(8), 1603-1616. https://doi.org/10.1177/09622802221102620
• Nonlinear mixed-effects models with misspecified random-effects distribution
  
  Drikvandi, R. (2020). Nonlinear mixed-effects models with misspecified random-effects distribution. Pharmaceutical Statistics, 19(3), 187-201. https://doi.org/10.1002/pst.1981
• On regularisation methods for analysis of high dimensional data
  
  Sirimongkolkasem, T., & Drikvandi, R. (2019). On regularisation methods for analysis of high dimensional data. Annals of Data Science, 6(4), 737-763. https://doi.org/10.1007/s40745-019-00209-4
• Permutation and Bayesian tests for testing random effects in linear mixed-effects models
  
  Rao, K., Drikvandi, R., & Saville, B. (2019). Permutation and Bayesian tests for testing random effects in linear mixed-effects models. Statistics in Medicine, 38(25), 5034-5047. https://doi.org/10.1002/sim.8350
• Testing random effects in linear mixed-effects models with serially correlated errors
  
  Drikvandi, R., & Noorian, S. (2019). Testing random effects in linear mixed-effects models with serially correlated errors. Biometrical Journal, 61(4), 802-812. https://doi.org/10.1002/bimj.201700203
• Nonlinear mixed-effects models for pharmacokinetic data analysis: assessment of the random-effects distribution
  
  Drikvandi, R. (2017). Nonlinear mixed-effects models for pharmacokinetic data analysis: assessment of the random-effects distribution. Journal of Pharmacokinetics and Pharmacodynamics, 44(3), 223-232. https://doi.org/10.1007/s10928-017-9510-8
• A goodness-of-fit test for the random-effects distribution in mixed models
  
  Efendi, A., Drikvandi, R., Verbeke, G., & Molenberghs, G. (2017). A goodness-of-fit test for the random-effects distribution in mixed models. Statistical Methods in Medical Research, 26(2), 970-983. https://doi.org/10.1177/0962280214564721
• Diagnosing misspecification of the random-effects distribution in mixed models
  
  Drikvandi, R., Verbeke, G., & Molenberghs, G. (2017). Diagnosing misspecification of the random-effects distribution in mixed models. Biometrics, 73(1), Article 63-71. https://doi.org/10.1111/biom.12551
• Supplementary materials for: Diagnosing misspecification of the random-effects distribution in mixed models
  
  Drikvandi, R., Verbeke, G., & Molenberghs, G. (2017). Supplementary materials for: Diagnosing misspecification of the random-effects distribution in mixed models. Biometrics: Journal of the International Biometric Society, 73(1), 63-71. https://doi.org/10.1111/biom.12551
• Testing multiple variance components in linear mixed-effects models
  
  Drikvandi, R., Verbeke, G., Khodadadi, A., & Partovi Nia, V. (2013). Testing multiple variance components in linear mixed-effects models. Biostatistics, 14(1), 144-159. https://doi.org/10.1093/biostatistics/kxs028
• Testing variance components in balanced linear growth curve models
  
  Drikvandi, R., Khodadadi, A., & Verbeke, G. (2012). Testing variance components in balanced linear growth curve models. Journal of Applied Statistics, 39(3), 563-572. https://doi.org/10.1080/02664763.2011.603294
• A bootstrap test for symmetry based on ranked set samples
  
  Drikvandi, R., Modarres, R., & Jalilian, A. H. (2011). A bootstrap test for symmetry based on ranked set samples. Computational Statistics & Data Analysis, 55(4), 1807-1814. https://doi.org/10.1016/j.csda.2010.11.012

Report

• Using big data analytics to explore the relationship between government stringency and preventative social behaviour during the COVID-19 pandemic in the United Kingdom [Preprint]
  
  Al-Zubaidy, N., Crespo, R., Jones, S., Drikvandi, R., Gould, L., Leis, M., Maheswaran, H., Neves, A. L., & Darzi, A. (2021). Using big data analytics to explore the relationship between government stringency and preventative social behaviour during the COVID-19 pandemic in the United Kingdom [Preprint].
• CodeCheck: How do our food choices affect climate change?
  
  Drikvandi, R., Williams, A., Boustati, A., Ezer, D., Arenas, D., de Wiljes, J.-H., Chang, M., Varga, M., Groves, M., & Ceritli, T. (2018). CodeCheck: How do our food choices affect climate change?. https://doi.org/10.5281/zenodo.1415344