Abstract
The occurrence of atypical circular observations on the torus can badly affect parameters estimation of the multivariate von Mises distribution. This paper addresses the problem of robust fitting of the multivariate von Mises model using the weighted likelihood methodology. The key ingredients are non-parametric density estimation for multivariate circular data and the definition of appropriate weighted estimating equations. The finite sample behavior of the proposed weighted likelihood estimator has been investigated by Monte Carlo numerical studies and real data examples.
Giulia Bertagnolli
Junior assistant professor (RTD-a)
I develop statistical methods for the analysis of structured data, such as networks, functional, high-dimensional, and circular data, with a focus on robustness and, possibly, their geometry.