Shape Changes via Hotelling ' S T 2 Fields

Limit Theorems for Large Dimensional Sample Means , Sample Covariance Matrices and Hotelling ’ S T 2 Statistics

In this paper, we prove the central limit theorem for Hotelling’s T 2 statistics when the dimension of the random vectors is proportional to the sample size via investigating asymptotic independence and random quadratic forms involving sample means and sample covariance matrices.

The Detection of Local Shape Changes via the Geometry of Hotelling’s T

This paper is motivated by the problem of detecting local changes or differences in shape between two samples of objects via the non-linear deformations required to map each object to an atlas standard. Local shape changes are then detected by high values of the random field of Hotelling’s T 2 statistics for detecting a change in mean of the vector deformations at each point in the object. To c...

Statistical Shape Analysis of Brain Structures using SPHARM-PDM

Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employe...

Framework for the Statistical Shape Analysis of Brain Structures using SPHARM-PDM.

Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employe...

Hotelling ' s T 2 VS . The Rank Transform With Real Likert Data