Statistical Models of Shape

Authors: Brombin, C., Salmaso, L., Fontanella, L., Ippoliti, L., Fusilli, C.
Explores specific inferential issues arising from the analysis of dynamic shapes with the attempt to solve the problems at hand using probability models and nonparametric testsUses the Expectation Maximization (EM) algorithm to give essential results for a likelihood-based approach to statistical inference in shape analysisCovers the theory of NonParametric Combination (NPC) tests as well as the applications of the methodology to the Face and Gesture Recognition Research Network (FG-NET) data

Learn to think statistically through real world examples

This book presents recent developments in multivariate and robust statistical methods. Featuring contributions by leading experts in the field it covers various topics, including multivariate and high-dimensional methods, time series, graphical models, robust estimation, supervised learning and normal extremes. It will appeal to statistics and data science researchers, PhD students and practitioners who are interested in modern multivariate and robust statistics. The book is dedicated to David E. Tyler on the occasion of his pending retirement and also includes a review contribution on the popular Tyler’s shape matrix.