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Ian L. Dryden
Researcher at University of Nottingham
Publications - 153
Citations - 5595
Ian L. Dryden is an academic researcher from University of Nottingham. The author has contributed to research in topics: Shape analysis (digital geometry) & Markov chain Monte Carlo. The author has an hindex of 30, co-authored 151 publications receiving 5184 citations. Previous affiliations of Ian L. Dryden include University of Leeds & University of South Carolina.
Papers
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Book
Statistical Shape Analysis: With Applications in R
Ian L. Dryden,Kanti V. Mardia +1 more
TL;DR: In this article, the authors proposed a planar procrustes analysis for two-dimensional data and showed that it is possible to estimate the size and shape of a shape in images.
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Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging
TL;DR: In this article, a statistical analysis of covariance matrix data is considered and, in particular, methodology is discussed which takes into account the non-Euclidean nature of the space of positive semi-definite symmetric matrices.
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Analysis of principal nested spheres
TL;DR: Analysis of principal nested spheres provides an intuitive and flexible decomposition of the high-dimensional sphere and an interesting special case of the analysis results in finding principal geodesics, similar to those from previous approaches to manifold principal component analysis.
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Serum Proteomic Fingerprinting Discriminates Between Clinical Stages and Predicts Disease Progression in Melanoma Patients
Shahid Mian,Selma Ugurel,Erika Parkinson,Iris Schlenzka,Ian L. Dryden,Lee Lancashire,Graham Ball,Colin S. Creaser,Robert C. Rees,Dirk Schadendorf +9 more
TL;DR: Validation of these findings may enable proteomic profiling to become a valuable tool for identifying high-risk melanoma patients eligible for adjuvant therapeutic interventions.
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Periods of rest in fMRI contain individual spontaneous events which are related to slowly fluctuating spontaneous activity.
Natalia Petridou,Natalia Petridou,Cesar Caballero Gaudes,Ian L. Dryden,Susan T. Francis,Penny A. Gowland +5 more
TL;DR: By regressing spontaneous events out of the fMRI signal, it is shown that both the correlation strength and the power in spectral frequencies <0.1 Hz decreased, indicating that spontaneous activation events contribute to low‐frequency fluctuations observed in resting state networks with fMRI.