English

Quantiles and depth for directional data from elliptically symmetric distributions

Statistics Theory 2022-10-13 v1 Statistics Theory

Abstract

We present canonical quantiles and depths for directional data following a distribution which is elliptically symmetric about a direction μ\mu on the sphere Sd1\mathcal{S}^{d-1}. Our approach extends the concept of Ley et al. [1], which provides valuable geometric properties of the depth contours (such as convexity and rotational equivariance) and a Bahadur-type representation of the quantiles. Their concept is canonical for rotationally symmetric depth contours. However, it also produces rotationally symmetric depth contours when the underlying distribution is not rotationally symmetric. We solve this lack of flexibility for distributions with elliptical depth contours. The basic idea is to deform the elliptic contours by a diffeomorphic mapping to rotationally symmetric contours, thus reverting to the canonical case in Ley et al. [1]. A Monte Carlo simulation study confirms our results. We use our method to evaluate the ellipticity of depth contours and for trimming of directional data. The analysis of fibre directions in fibre-reinforced concrete underlines the practical relevance.

Keywords

Cite

@article{arxiv.2210.06098,
  title  = {Quantiles and depth for directional data from elliptically symmetric distributions},
  author = {Konstantin Hauch and Claudia Redenbach},
  journal= {arXiv preprint arXiv:2210.06098},
  year   = {2022}
}

Comments

The paper is submitted to the Electronic Journal of Statistics with manuscript ID EJS2210-011

R2 v1 2026-06-28T03:25:39.217Z