Statistical depth in abstract metric spaces
G Geenens, A Nieto-Reyes, G Francisci - Statistics and Computing, 2023 - Springer
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 …
as it essentially acts as a surrogate for the notion of ranking of observations which is absent …
[HTML][HTML] Projection depth and Lr-type depths for fuzzy random variables
L González-De La Fuente, A Nieto-Reyes… - Fuzzy Sets and …, 2024 - Elsevier
Statistical depth functions are a standard tool in nonparametric statistics to extend order-
based univariate methods to the multivariate setting. Since there is no universally accepted …
based univariate methods to the multivariate setting. Since there is no universally accepted …
Metric statistics: Exploration and inference for random objects with distance profiles
The Supplement contains proofs and auxiliary results, additional simulations for the two-
sample test, additional simulations for distance profiles and transport ranks for multimodal …
sample test, additional simulations for distance profiles and transport ranks for multimodal …
Conformal inference for regression on Riemannian Manifolds
Regression on manifolds, and, more broadly, statistics on manifolds, has garnered
significant importance in recent years due to the vast number of applications for this type of …
significant importance in recent years due to the vast number of applications for this type of …
Projection depth and -type depths for fuzzy random variables
LGD La Fuente, A Nieto-Reyes, P Terán - arXiv preprint arXiv:2401.01894, 2023 - arxiv.org
Statistical depth functions are a standard tool in nonparametric statistics to extend order-
based univariate methods to the multivariate setting. Since there is no universally accepted …
based univariate methods to the multivariate setting. Since there is no universally accepted …