Outputs (6)

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.