Title: Bayesian Linear Size-and-Shape Regression with Applications to Face Data

Author(s): Huiling Le, Ian L. Dryden and Kwang-Rae Kim
Issue: Volume 81 Series A Part 1 Year 2019
Pages: 83 -- 103
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 practitioners to understand than alternative models in the quotient space of size-and-shapes. We consider a Bayesian linear size-andshape regression model in which the response variable is given by labelled configuration matrix, and the covariates represent quantities such as gender and age. It is important to parameterize the model so that it is identifiable, and we use the LQ decomposition in the intercept term in the model for this purpose. Gamma priors for the inverse variance of the error term, matrix Fisher priors for the random rotation matrix, and flat priors for the regression coefficients are used. Markov chain Monte Carlo algorithms are used for sampling from the posterior distribution, in particular by using combinations of Metropolis-Hastings updates and a Gibbs sampler. The proposed Bayesian methodology is illustrated with an application to forensic facial data in three dimensions, where we investigate the main changes in growth by describing relative movements of landmarks for each gender over time.
Primary 62H99; Secondary 62F15.
Keywords and phrases: Bayesian, Markov chain Monte Carlo, Principal components analysis, Size-and-shape regression.