Regression Modeling for Size-and-Shape Data Based on a Gaussian Model for Landmarks

DOI10.1080/01621459.2020.1724115zbMath1464.62287OpenAlexW3006553300MaRDI QIDQ4999177

Ian L. Dryden, P. J. Paine, Andrew T. A. Wood, Alfred Kume

Publication date: 6 July 2021

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://kar.kent.ac.uk/79862/1/Unblind_with_Apx_wothout_SM.pdf

zbMATH Keywords

EM algorithmmean shapesize-and-shape analysisoffset-normal shape distributionsshape of mean

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11)

Related Items (1)

Bayesian size-and-shape regression modelling

Cites Work

• On the derivatives of the normalising constant of the Bingham distribution
• On the convergence properties of the EM algorithm
• Wishart and pseudo-Wishart distributions and some applications to shape theory
• Shape, Procrustes tangent projections and bilateral symmetry
• Shape curves and geodesic modelling
• ESTIMATION OF THE CONCENTRATION PARAMETERS OF THE FISHER MATRIX DISTRIBUTION ON 50(3) AND THE BINGHAM DISTRIBUTION ON S_q, q≥ 2
• On the consistency of procrustean mean shapes
• The Fréchet mean shape and the shape of the means
• Asymptotic Statistics
• Shape-space smoothing splines for planar landmark data
• Saddlepoint approximations for the Bingham and Fisher–Bingham normalising constants
• The bootstrap and Edgeworth expansion
• Calculating the normalising constant of the Bingham distribution on the sphere using the holonomic gradient method
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