Error Probabilities for Halfspace Depth

TL;DR

This paper investigates the convergence rate of halfspace depth, a data centrality measure, providing improved bounds by leveraging geometric properties, which benefits both computational geometry and statistical analysis.

Contribution

The paper offers new explicit bounds on the convergence rate of halfspace depth, enhancing understanding of its asymptotic behavior with larger samples.

Findings

Improved bounds on convergence rate of halfspace depth.

Utilization of geometric properties to refine theoretical estimates.

Enhanced understanding of halfspace depth's asymptotic properties.

Abstract

Data depth functions are a generalization of one-dimensional order statistics and medians to real spaces of dimension greater than one; in particular, a data depth function quantifies the centrality of a point with respect to a data set or a probability distribution. One of the most commonly studied data depth functions is halfspace depth. It is of interest to computational geometers because it is highly geometric, and it is of interest to statisticians because it shares many desirable theoretical properties with the one-dimensional median. As the sample size increases, the halfspace depth for a sample converges to the halfspace depth for the underlying distribution, almost surely. In this paper, we use the geometry of halfspace depth to improve the explicit bounds on the rate of convergence.