Circular Expectiles

In this work, we introduce circular expectiles as minimizers of an asymmetric circular loss function based on chord distance.

In contrast to the linear expectile criterion, the resulting circular optimization problem is non-convex, so existence and uniqueness require a separate analysis.

The construction extends linear expectiles to directional data while preserving the circular mean as the symmetric case corresponding to $\alpha=1/2$.

We derive basic representations of the objective function and the associated identification function, and give a geometric interpretation that generalizes the corresponding representation for the circular mean.

Furthermore, we prove the existence and uniqueness of the minimizers for distributions with positive density on the circle.

The empirical circular expectile is defined by using the sample circular mean as reference direction for the induced linear order on the circle.

We prove the uniqueness of the empirical expectile, as well as its consistency and finite-dimensional asymptotic normality.

Finally, we indicate possible applications to circular measures of dispersion, skewness, and symmetry diagnostics.