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All Outputs (28)

Object oriented data analysis of surface motion time series in peatland landscapes (2024)
Journal Article
Mitchell, E., Ian, D., Christopher, F., Roxanne, A., Bradley, A., David, L., & Andrew, S. (in press). Object oriented data analysis of surface motion time series in peatland landscapes. Journal of the Royal Statistical Society: Series C,

Peatlands account for 10% of UK land area, 80% of which are degraded to some degree, emitting carbon at a similar magnitude to oil refineries or landfill sites. A lack of tools for rapid and reliable assessment of peatland condition has limited monit... Read More about Object oriented data analysis of surface motion time series in peatland landscapes.

Manifold valued data analysis of samples of networks, with applications in corpus linguistics (2022)
Journal Article
Severn, K. E., Dryden, I. L., & Preston, S. P. (2022). Manifold valued data analysis of samples of networks, with applications in corpus linguistics. Annals of Applied Statistics, 16(1), 368-390. https://doi.org/10.1214/21-aoas1480

Networks arise in many applications, such as in the analysis of text documents, social interactions and brain activity. We develop a general framework for extrinsic statistical analysis of samples of networks, motivated by networks representing text... Read More about Manifold valued data analysis of samples of networks, with applications in corpus linguistics.

The Bayesian Spatial Bradley–Terry model: Urban deprivation modelling in Tanzania (2022)
Journal Article
Seymour, R. G., Sirl, D., Preston, S. P., Dryden, I. L., Ellis, M. J., Perrat, B., & Goulding, J. (2022). The Bayesian Spatial Bradley–Terry model: Urban deprivation modelling in Tanzania. Journal of the Royal Statistical Society: Series C, 71(2), 288-308. https://doi.org/10.1111/rssc.12532

Identifying the most deprived regions of any country or city is key if policy makers are to design successful interventions. However, locating areas with the greatest need is often surprisingly challenging in developing countries. Due to the logistic... Read More about The Bayesian Spatial Bradley–Terry model: Urban deprivation modelling in Tanzania.

GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling (2021)
Journal Article
Louison, K. A., Louison, K. A., Dryden, I. L., & Laughton, C. A. (2021). GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling. Journal of Chemical Theory and Computation, 17(12), 7930-7937. https://doi.org/10.1021/acs.jctc.1c00735

We describe a general approach to transforming molecular models between different levels of resolution, based on machine learning methods. The approach uses a matched set of models at both levels of resolution for training, but requires only the coor... Read More about GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling.

Non‐parametric regression for networks (2021)
Journal Article
Severn, K. E., Dryden, I. L., & Preston, S. P. (2021). Non‐parametric regression for networks. Stat, 10(1), Article e373. https://doi.org/10.1002/sta4.373

Network data are becoming increasingly available, and so there is a need to develop suitable methodology for statistical analysis. Networks can be represented as graph Laplacian matrices, which are a type of manifold-valued data. Our main objective i... Read More about Non‐parametric regression for networks.

Regression modelling for size-and-shape data based on a Gaussian model for landmarks (2020)
Journal Article
Dryden, I. L., Kume, A., Paine, P. J., & Wood, A. T. A. (2021). Regression modelling for size-and-shape data based on a Gaussian model for landmarks. Journal of the American Statistical Association, 116(534), 1011-1022. https://doi.org/10.1080/01621459.2020.1724115

In this paper we propose a regression model for size-and-shape response data. So far as we are aware, few such models have been explored in the literature to date. We assume a Gaussian model for labelled landmarks; these landmarks are used to represe... Read More about Regression modelling for size-and-shape data based on a Gaussian model for landmarks.

Principal nested shape space analysis of molecular dynamics data (2019)
Journal Article
Dryden, I. L., Kim, K.-R., Laughton, C. A., & Le, H. (2019). Principal nested shape space analysis of molecular dynamics data. Annals of Applied Statistics, 13(4), 2213-2234. https://doi.org/10.1214/19-AOAS1277

Molecular dynamics simulations produce huge datasets of temporal sequences of molecules. It is of interest to summarize the shape evolution of the molecules in a succinct, low-dimensional representation. However, Euclidean techniques such as principa... Read More about Principal nested shape space analysis of molecular dynamics data.

Local and Global Energies for Shape Analysis in Medical Imaging (2019)
Journal Article
Varano, V., Piras, P., Gabriele, S., Teresi, L., Nardinocchi, P., Dryden, I. L., …Puddu, P. E. (2020). Local and Global Energies for Shape Analysis in Medical Imaging. International Journal for Numerical Methods in Biomedical Engineering, 36(2), Article e3252. https://doi.org/10.1002/cnm.3252

In a previous contribution a new Riemannian shape space, named TPS space, was introduced to perform statistics on shape data. This space was endowed with a Rie-mannian metric and a flat connection, with torsion, compatible with the given metric. This... Read More about Local and Global Energies for Shape Analysis in Medical Imaging.

Modelling Emerging Pollutants in Wastewater Treatment: A Case Study using the Pharmaceutical 17α−ethinylestradiol (2019)
Journal Article
Acheampong, E., Dryden, I. L., Wattis, J. A., Twycross, J., Scrimshaw, M. D., & Gomes, R. L. (2019). Modelling Emerging Pollutants in Wastewater Treatment: A Case Study using the Pharmaceutical 17α−ethinylestradiol. Computers and Chemical Engineering, 128, 477-487. https://doi.org/10.1016/j.compchemeng.2019.06.020

Mathematical modelling can play a key role in understanding as well as quantifying uncertainties surrounding the presence and fate of emerging pollutants in wastewater treatment processes (WWTPs). This paper presents for the first time a simplified e... Read More about Modelling Emerging Pollutants in Wastewater Treatment: A Case Study using the Pharmaceutical 17α−ethinylestradiol.

Bayesian linear size-and-shape regression with applications to face data (2018)
Journal Article
Dryden, I. L., Le, H., & Kim, K.-R. (2019). Bayesian linear size-and-shape regression with applications to face data. Sankhya A, 81(1), 83–103. https://doi.org/10.1007/s13171-018-0136-8

Regression models for size-and-shape analysis are developed, where the model is specified in the Euclidean space of the landmark coordinates. Statistical models in this space (which is known as the top space or ambient space) are often easier for pra... Read More about Bayesian linear size-and-shape regression with applications to face data.

Peptide refinement using a stochastic search (2018)
Journal Article
Lewis, N. H., Hitchcock, D. B., Dryden, I. L., & Rose, J. R. (in press). Peptide refinement using a stochastic search. Journal of the Royal Statistical Society: Series C, https://doi.org/10.1111/rssc.12280

Identifying a peptide based on a scan from a mass spectrometer is an important yet highly challenging problem. To identify peptides, we present a Bayesian approach which uses prior information about the average relative abundances of bond cleavages a... Read More about Peptide refinement using a stochastic search.

Multiple linear regression modelling to predict the stability of polymer-drug solid dispersions: comparison of the effects of polymers and manufacturing methods on solid dispersion stability (2018)
Journal Article
Fridgeirsdottir, G., Harris, R., Dryden, I. L., Fischer, P. M., & Roberts, C. J. (2018). Multiple linear regression modelling to predict the stability of polymer-drug solid dispersions: comparison of the effects of polymers and manufacturing methods on solid dispersion stability. Molecular Pharmaceutics, 15(5), https://doi.org/10.1021/acs.molpharmaceut.8b00021

Solid dispersions can be a successful way to enhance the bioavailability of poorly soluble drugs. Here 60 solid dispersion formulations were produced using ten chemically diverse, neutral, poorly soluble drugs, three commonly used polymers, and two m... Read More about Multiple linear regression modelling to predict the stability of polymer-drug solid dispersions: comparison of the effects of polymers and manufacturing methods on solid dispersion stability.

Journeys in big data statistics (2018)
Journal Article
Dryden, I. L., & Hodge, D. J. (2018). Journeys in big data statistics. Statistics and Probability Letters, 136, https://doi.org/10.1016/j.spl.2018.02.013

The realm of big data is a very wide and varied one. We discuss old, new, small and big data, with some of the important challenges including dealing with highly-structured and object-oriented data. In many applications the objective is to discern pa... Read More about Journeys in big data statistics.

The decomposition of deformation: new metrics to enhance shape analysis in medical imaging (2018)
Journal Article
Varano, V., Piras, P., Gabriele, S., Teresi, L., Nardinocchi, P., Dryden, I. L., Torromeo, C., & Puddu, P. E. (in press). The decomposition of deformation: new metrics to enhance shape analysis in medical imaging. Medical Image Analysis, 46, https://doi.org/10.1016/j.media.2018.02.005

In landmarks-based Shape Analysis size is measured, in most cases, with Centroid Size. Changes in shape are decomposed in affine and non affine components. Furthermore the non affine component can be in turn decomposed in a series of local deformatio... Read More about The decomposition of deformation: new metrics to enhance shape analysis in medical imaging.

Penalised Euclidean distance regression (2018)
Journal Article
Vasiliu, D., Dey, T., & Dryden, I. L. (2018). Penalised Euclidean distance regression. Stat, 7(1), Article e175. https://doi.org/10.1002/sta4.175

A method is introduced for variable selection and prediction in linear regression problems where the number of predictors can be much larger than the number of observations. The methodology involves minimising a penalised Euclidean distance, where th... Read More about Penalised Euclidean distance regression.

Decoding fMRI events in sensorimotor motor network using sparse paradigm free mapping and activation likelihood estimates: Decoding fMRI Events in SMN (2017)
Journal Article
Tan, F. M., Caballero-Gaudes, C., Mullinger, K. J., Cho, S.-Y., Zhang, Y., Dryden, I. L., …Gowland, P. A. (2017). Decoding fMRI events in sensorimotor motor network using sparse paradigm free mapping and activation likelihood estimates: Decoding fMRI Events in SMN. Human Brain Mapping, 38(11), 5778-5794. https://doi.org/10.1002/hbm.23767

Most functional MRI (fMRI) studies map task-driven brain activity using a block or event-related paradigm. Sparse paradigm free mapping (SPFM) can detect the onset and spatial distribution of BOLD events in the brain without prior timing information,... Read More about Decoding fMRI events in sensorimotor motor network using sparse paradigm free mapping and activation likelihood estimates: Decoding fMRI Events in SMN.

The TPS Direct Transport: a new method for transporting deformations in the Size-and-shape Space (2017)
Journal Article
Varano, V., Gabriele, S., Teresi, L., Dryden, I. L., Puddu, P. E., Torromeo, C., & Piras, P. (in press). The TPS Direct Transport: a new method for transporting deformations in the Size-and-shape Space. International Journal of Computer Vision, https://doi.org/10.1007/s11263-017-1031-9

Modern shape analysis allows the fine comparison of shape changes occurring between different objects. Very often the classic machineries of Generalized Procrustes Analysis and Principal Component Analysis are used in order to contrast the shape chan... Read More about The TPS Direct Transport: a new method for transporting deformations in the Size-and-shape Space.

Covariance analysis for temporal data, with applications to DNA modelling (2017)
Journal Article
Dryden, I. L., Hill, B. C., Wang, H., & Laughton, C. A. (2017). Covariance analysis for temporal data, with applications to DNA modelling. Stat, 6(1), 218-230. https://doi.org/10.1002/sta4.149

We introduce methodology for analysing the mean size-and-shape and covariance matrix of landmark data that are collected over time. Motivated by a study of DNA damage, we study some permutation based tests for investigating significant differences in... Read More about Covariance analysis for temporal data, with applications to DNA modelling.

Regularisation, interpolation and visualisation of diffusion tensor images using non-Euclidean statistics (2015)
Journal Article
Zhou, D., Dryden, I. L., Koloydenko, A. A., Audenaert, K. M., & Bai, L. (2016). Regularisation, interpolation and visualisation of diffusion tensor images using non-Euclidean statistics. Journal of Applied Statistics, 43(5), 943-978. https://doi.org/10.1080/02664763.2015.1080671

Practical statistical analysis of diffusion tensor images is considered, and we focus primarily on methods that use metrics based on Euclidean distances between powers of diffusion tensors. First we describe a family of anisotropy measures based on a... Read More about Regularisation, interpolation and visualisation of diffusion tensor images using non-Euclidean statistics.