# Algorithmic and geometric aspects of data depth with focus on $\beta$-skeleton depth

## TL;DR

This paper explores the geometric and algorithmic properties of the $eta$-skeleton data depth, a multivariate measure of centrality, comparing it with other depth functions like halfspace and simplicial depths.

## Contribution

It provides a detailed analysis of the geometric and algorithmic aspects of $eta$-skeleton depth, a recently introduced data depth measure, and compares it with existing depth functions.

## Key findings

- $eta$-skeleton depth is equivalent to spherical and lens depths for specific $eta$ values.
- The paper develops algorithms for computing $eta$-skeleton depth efficiently.
- Geometric properties of $eta$-skeleton depth are characterized and analyzed.

## Abstract

The statistical rank tests play important roles in univariate non-parametric data analysis. If one attempts to generalize the rank tests to a multivariate case, the problem of defining a multivariate order will occur. It is not clear how to define a multivariate order or statistical rank in a meaningful way. One approach to overcome this problem is to use the notion of data depth which measures the centrality of a point with respect to a given data set. In other words, a data depth can be applied to indicate how deep a point is located with respect to a given data set. Using data depth, a multivariate order can be defined by ordering the data points according to their depth values. Various notions of data depth have been introduced over the last decades. In this thesis, we discuss three depth functions: two well-known depth functions halfspace depth and simplicial depth, and one recently defined depth function named as $\beta$-skeleton depth, $\beta\geq 1$. The $\beta$-skeleton depth is equivalent to the previously defined spherical depth and lens depth when $\beta=1$ and $\beta=2$, respectively. Our main focus in this thesis is to explore the geometric and algorithmic aspects of $\beta$-skeleton depth.