Biased estimators on Quotient spaces

Nina Miolane 1 Xavier Pennec 1
1 ASCLEPIOS - Analysis and Simulation of Biomedical Images
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Usual statistics are defined, studied and implemented on Euclidean spaces. But what about statistics on other mathematical spaces, like manifolds with additional properties: Lie groups, Quotient spaces, Stratified spaces etc. How can we describe the interaction between statistics and geometry? The structure of Quotient space in particular is widely used to model data, for example every time one deals with shape data. These can be shapes of constellations in Astronomy, shapes of human organs in Computational Anatomy, shapes of skulls in Palaeontology, etc. Given this broad field of applications, statistics on shapes -and more generally on observations belonging to quotient spaces- have been studied since the 1980's. However, most theories model the variability in the shapes but do not take into account the noise on the observations themselves. In this paper, we show that statistics on quotient spaces are biased and even inconsistent when one takes into account the noise. In particular, some algorithms of template estimation in Computational Anatomy are biased and inconsistent. Our development thus gives a first theoretical geometric explanation of an experimentally observed phenomenon. A biased estimator is not necessarily a problem. In statistics, it is a general rule of thumb that a bias can be neglected for example when it represents less than 0.25 of the variance of the estimator. We can also think about neglecting the bias when it is low compared to the signal we estimate. In view of the applications, we thus characterize geometrically the situations when the bias can be neglected with respect to the situations when it must be corrected.
Type de document :
Communication dans un congrès
Geometric Science of Information. Second International Conference, GSI 2015., Oct 2015, Palaiseau, France. Springer, 9389, pp.130-139, Lecture notes in computer science (LNCS). 〈10.1007/978-3-319-25040-3_15〉
Liste complète des métadonnées

Littérature citée [12 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01203805
Contributeur : Project-Team Asclepios <>
Soumis le : mercredi 23 septembre 2015 - 18:36:50
Dernière modification le : jeudi 11 janvier 2018 - 16:48:44
Document(s) archivé(s) le : mardi 29 décembre 2015 - 09:41:27

Fichier

Miolane_Pennec_GSI2015.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Nina Miolane, Xavier Pennec. Biased estimators on Quotient spaces. Geometric Science of Information. Second International Conference, GSI 2015., Oct 2015, Palaiseau, France. Springer, 9389, pp.130-139, Lecture notes in computer science (LNCS). 〈10.1007/978-3-319-25040-3_15〉. 〈hal-01203805〉

Partager

Métriques

Consultations de la notice

510

Téléchargements de fichiers

167