Covariance weighted procrustes analysis

Brignell, Christopher J.; Dryden, Ian L.; Browne, William J.

doi:10.1007/978-3-319-22957-7_9

All Outputs (4)

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.

Long-term amorphous drug stability predictions using easily calculated, predicted, and measured parameters (2015)
Journal Article
Nurzy?ska, K., Booth, J., Roberts, C., McCabe, J., Dryden, I., & Fischer, P. M. (2015). Long-term amorphous drug stability predictions using easily calculated, predicted, and measured parameters. Molecular Pharmaceutics, 12(9), 3389-3398. doi:10.1021/acs.molpharmaceut.5b00409

Bayesian registration of functions and curves (2015)
Journal Article
Cheng, W., Dryden, I. L., & Huang, X. (2015). Bayesian registration of functions and curves. Bayesian Analysis, 2015, https://doi.org/10.1214/15-BA957

Bayesian analysis of functions and curves is considered, where warping and other geometrical transformations are often required for meaningful comparisons. The functions and curves of interest are represented using the recently introduced square root... Read More about Bayesian registration of functions and curves.

Covariance weighted procrustes analysis (2015)
Book Chapter
Brignell, C. J., Dryden, I. L., & Browne, W. J. (2015). Covariance weighted procrustes analysis. In P. K. Turaga, & A. Srivastava (Eds.), Riemannian Computing in Computer Vision (189-209). https://doi.org/10.1007/978-3-319-22957-7_9

© Springer International Publishing Switzerland 2016. We revisit the popular Procrustes matching procedure of landmark shape analysis and consider the situation where the landmark coordinates have a completely general covariance matrix, extending pre... Read More about Covariance weighted procrustes analysis.