Depth statistics

TL;DR

Data depth provides a robust, nonparametric way to measure centrality and shape in multivariate data, with applications extending to functional spaces and various data shapes.

Contribution

This paper reviews the development of data depth concepts, their properties, computational aspects, and extensions to broader data types and distributions.

Findings

Multiple notions of data depth have been introduced, each suited to different applications.

Depth-based methods offer robust measures of location, scale, and shape.

Extensions to functional data and general probability distributions have been established.

Abstract

In 1975 John Tukey proposed a multivariate median which is the 'deepest' point in a given data cloud in R^d. Later, in measuring the depth of an arbitrary point z with respect to the data, David Donoho and Miriam Gasko considered hyperplanes through z and determined its 'depth' by the smallest portion of data that are separated by such a hyperplane. Since then, these ideas has proved extremely fruitful. A rich statistical methodology has developed that is based on data depth and, more general, nonparametric depth statistics. General notions of data depth have been introduced as well as many special ones. These notions vary regarding their computability and robustness and their sensitivity to reflect asymmetric shapes of the data. According to their different properties they fit to particular applications. The upper level sets of a depth statistic provide a family of set-valued statistics, named depth-trimmed or central regions. They describe the distribution regarding its location, scale and shape. The most central region serves as a median. The notion of depth has been extended from data clouds, that is empirical distributions, to general probability distributions on R^d, thus allowing for laws of large numbers and consistency results. It has also been extended from d-variate data to data in functional spaces.