Minimum distance method for directional data and outlier detection

SAU, MERCEDES FERNANDEZ; RODRIGUEZ, DANIELA

Advances in Data Analysis and Classification

Springer Verlag

Lugar: berlin; Año: 2017 p. 1 - 17

1862-5347

In this paper, we propose estimators based on the minimum distance for the unknown parameters of a parametric density on the unit sphere. We show that these estimators are consistent and asymptotically normally distributed. Also, we apply our proposal to develop a method that allows us to detect potential atypical values. The behavior under small samples of the proposed estimators is studied using Monte Carlo simulations. Two applications of our procedure are illustrated with real data sets.