Research

By Nicola Rennie in Research

My research interests lie in statistics, data science, and operational research - with a focus on applications to health data. I’m also interested in research into the way we teach statistics and data science, particularly on aspects related to accessibility and reproducibility of teaching materials.

My previous research focused on the development and use of functional analysis for detecting outliers in time series data. We have performed case studies on multiple empirical data sets including railway booking data and bike-sharing usage data.

A blog post giving an overview of my PhD thesis is available on my website.

Publications

Journal publications

Outlier detection in network revenue management.
N. Rennie, C. Cleophas, A. M. Syksulski, F. Dost.
OR Spectrum. 2023.
doi: doi.org/10.1007/s00291-023-00714-2.

Analysing and visualising bike sharing demand with outliers.
N. Rennie, C. Cleophas, A. M. Syksulski, F. Dost.
Discover Data. Volume 1, Issue 1. March 2023.
doi: doi.org/10.1007/s44248-023-00001-z

Identifying and responding to outlier demand in revenue management.
N. Rennie, C. Cleophas, A. M. Syksulski, F. Dost.
European Journal of Operational Research. Volume 293, Issue 3, 16 September 2021, 1015-1030.
doi: doi.org/10.1016/j.ejor.2021.01.002.

Other publications

Creating Christmas cards with R
N. Rennie.
Real World Data Science (online). 2023.
Link: realworlddatascience.net/ideas/tutorials/posts/2023/12/12/xmas-cards.html.

Best practices for data visualisation
A. Krause, N. Rennie. B. Tarran.
Royal Statistical Society (online). 2023.
Link: royal-statistical-society.github.io/datavisguide.

We’re not getting any younger! Or should that be older?
N. Rennie.
Significance (online). 2022.
Link: www.significancemagazine.com/science/723-we-re-not-getting-any-younger-or-should-that-be-older.

Detecting demand outliers in transport systems
N. Rennie.
PhD Thesis. 2021.
doi: doi.org/10.17635/lancaster/thesis/1448.

Preprint publications

Prevalence of radiological cerebral small vessel disease: an insight from routinely collected data.
M. P. Maskery, N. Rennie, S. Mathur, J. Knight, H. C. A. Emsley.
Preprint. November 2023.
doi: doi.org/10.21203/rs.3.rs-3625684/v1