Skip to main content

Research Repository

Advanced Search

Outputs (31)

A two-level multivariate response model for data with latent structures (2025)
Journal Article
Zhang, Y., Einbeck, J., & Drikvandi, R. (online). A two-level multivariate response model for data with latent structures. Statistical Modelling, https://doi.org/10.1177/1471082X241313024

A novel approach is proposed for analysing multilevel multivariate response data. The approach is based on identifying a one-dimensional latent variable spanning the space of responses, which then induces correlation between upper-level units. The la... Read More about A two-level multivariate response model for data with latent structures.

Challenges in high dimensional change point analysis and advanced approaches (2024)
Presentation / Conference Contribution
Albalawi, S., & Drikvandi, R. (2024, August). Challenges in high dimensional change point analysis and advanced approaches. Presented at 6th International Conference on Statistics: Theory and Applications (ICSTA'24), Barcelona, Spain

Change point analysis aims at identifying significant distributional shifts and changes in data sequences. While the problem has been extensively studied for standard low dimensional data, the transition to high dimensional data imposes several chall... Read More about Challenges in high dimensional change point analysis and advanced approaches.

A distribution-free method for change point detection in non-sparse high dimensional data (2024)
Journal Article
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, https://doi.org/10.1080/10618600.2024.2365733

We propose a distribution-free distance-based method for high dimensional change points that can address challenging situations when the sample size is very small compared to the dimension as in the so-called HDLSS data or when non-sparse changes may... Read More about A distribution-free method for change point detection in non-sparse high dimensional data.

A framework for analysing longitudinal data involving time-varying covariates (2024)
Journal Article
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

Standard models for longitudinal data ignore the stochastic nature of time-varying covariates and their stochastic evolution over time by treating them as fixed variables. There have been recent methods for modelling time-varying covariates; however,... Read More about A framework for analysing longitudinal data involving time-varying covariates.

Exploring the relationship between government stringency and preventative social behaviours during the COVID-19 pandemic in the United Kingdom. (2023)
Journal Article
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

We constructed a preventive social behaviours (PSB) Index using survey questions that were aligned with WHO recommendations, and used linear regression to assess the impact of reported COVID-19 deaths (RCD), people's confidence of government handling... Read More about Exploring the relationship between government stringency and preventative social behaviours during the COVID-19 pandemic in the United Kingdom..

High dimensional change points: challenges and some proposals (2023)
Presentation / Conference Contribution
Zhang, L., & Drikvandi, R. (2023, August). High dimensional change points: challenges and some proposals. Presented at 5th International Conference on Statistics: Theory and Applications (ICSTA 2023)

MEGH: A parametric class of general hazard models for clustered survival data (2022)
Journal Article
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

In many applications of survival data analysis, the individuals are treated in different medical centres or belong to different clusters defined by geographical or administrative regions. The analysis of such data requires accounting for between-clus... Read More about MEGH: A parametric class of general hazard models for clustered survival data.

Diagnostic tools for random effects in general mixed models (2021)
Presentation / Conference Contribution
Drikvandi, R. (2021, December). 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