@article{10902/32315,
year = {2023},
month = {4},
url = {https://hdl.handle.net/10902/32315},
abstract = {The concept of depth has proved very important for multivariate and functional data analysis, as it essentially acts as a surrogate for the notion of ranking of observations which is absent in more than one dimension. Motivated by the rapid development of technology, in particular the advent of "Big Data", we extend here that concept to general metric spaces, propose a natural depth measure and explore its properties as a statistical depth function. Working in a general metric space allows the depth to be tailored to the data at hand and to the ultimate goal of the analysis, a very desirable property given the polymorphic nature of modern data sets. This flexibility is thoroughly illustrated by several real data analyses},
organization = {Gery Geenens’ research was supported by a Faculty Research Grant from the Faculty of Science, UNSW Sydney, Australia. Alicia Nieto-Reyes’ research was funded by the Spanish Ministerio de Ciencia, Innovación y Universidades Grant Number MTM2017-86061-C2-2-P.},
publisher = {Springer},
publisher = {Statistics and Computing, 2023, 33(2), 46},
title = {Statistical depth in abstract metric spaces},
author = {Geenens, Gery and Nieto Reyes, Alicia and Francisci, Giacomo},
}