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

As in the previous meetings, the theory of copulas has arelevant place in this book, as well as promising research on the more general concept of quasi- copulas. Other papers are devoted to the theory and compatibility of distri- butions, models for survival distributions and other well-known distributions. As was traditional in the previous meetings, several papers treat the problem of measuring dependence, monotonicity and ordering. A new set of papers is devoted to proposing some statistical models in aspects such as goodness of fit assessment, testing independence, estimating association parameters, etc.

Mastering the Language of Data: From Distributions to Predictive Models