Dr Amira Elayouty

  • Honorary Research Fellow (School of Mathematics & Statistics)

email: Amira.Elayouty@glasgow.ac.uk

Mathematics Building

Import to contacts

Research interests

My principal research interests are in the development and application of advanced statistical methods to the environmental sciences. In particular, I am interested in spatio-temporal models, non-parametric regression, additive models and functional data analysis for high-frequency environmental data. My application areas are not only limited to the environment but also to the study of social phenomena across space and time. 

Research groups

Publications

List by: Type | Date

Jump to: 2022 | 2018
Number of items: 2.

2022

Elayouty, A., Scott, M. and Miller, C. (2022) Time-varying functional principal components for non-stationary EpCO2 in freshwater systems. Journal of Agricultural, Biological, and Environmental Statistics, 27(3), pp. 506-522. (doi: 10.1007/s13253-022-00494-2)

2018

Elayouty, A., Scott, M. , Miller, C. and Waldron, S. (2018) Functional Principal Component Analysis for Non-stationary Dynamic Time Series. In: 33rd International Workshop on Statistical Modelling (IWSM 2018), Bristol, UK, 16-20 Jul 2018, pp. 84-89.

This list was generated on Wed Oct 16 01:46:02 2024 BST.
Number of items: 2.

Articles

Elayouty, A., Scott, M. and Miller, C. (2022) Time-varying functional principal components for non-stationary EpCO2 in freshwater systems. Journal of Agricultural, Biological, and Environmental Statistics, 27(3), pp. 506-522. (doi: 10.1007/s13253-022-00494-2)

Conference Proceedings

Elayouty, A., Scott, M. , Miller, C. and Waldron, S. (2018) Functional Principal Component Analysis for Non-stationary Dynamic Time Series. In: 33rd International Workshop on Statistical Modelling (IWSM 2018), Bristol, UK, 16-20 Jul 2018, pp. 84-89.

This list was generated on Wed Oct 16 01:46:02 2024 BST.

Teaching

Previous experience in teaching include: Statistics and Probability, Calculus, Inferential Statistics, Statistical Modelling and Functional Data Analysis.