The problem of outliers in circular data is studied from a Bayesian point of view. Susprising observations are identified by means of a predictive measure. On the basis of Box-Tiao methodology, the mean-shift model and some aspects of the contamination of the concentration parameter for a Von Mises distribution are analyzed. Intuitive aspects of the resultant weights and their applications in some classical examples are included.
@article{urn:eudml:doc:40044,
title = {Outliers in circular data: a Bayesian approach.},
journal = {Q\"uestii\'o},
volume = {10},
year = {1986},
pages = {1-6},
mrnumber = {MR0862407},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:40044}
}
Arnaiz Tovar, Gonzalo; Ruiz Rivas, Carmen. Outliers in circular data: a Bayesian approach.. Qüestiió, Tome 10 (1986) pp. 1-6. http://gdmltest.u-ga.fr/item/urn:eudml:doc:40044/