activity
20172022
most citedWeighted lens depth: Some applications to supervised classification

1 citations · 1 across the 7 of their papers we have counts for

collaborators

12 papers

math.ST2022

Statistical analysis of measures of non-convexity

Alejandro Cholaquidis, Ricardo Fraiman, Leonardo Moreno +1

Several measures of non-convexity (departures from convexity) have been introduced in the literature, both for sets and functions. Some of them are of geometric nature, while other…

math.GT2022

An -convex set which is not locally contractible

Alejandro Cholaquidis

The study of shape restrictions of subsets of have several applications in many areas, being convexity, -convexity, and positive reach, some of the most famous, a…

math.ST2020

On a general definition of the functional linear model

José R. Berrendero, Alejandro Cholaquidis, Antonio Cuevas

A general formulation of the linear model with functional (random) explanatory variable , and scalar response Y is proposed. It includes the standard functional…

math.ST20201 cited

Weighted lens depth: Some applications to supervised classification

Alejandro Cholaquidis, Ricardo Fraiman, Fabrice Gamboa +1

Starting with Tukey's pioneering work in the 1970's, the notion of depth in statistics has been widely extended especially in the last decade. These extensions include high dimensi…

math.ST2020

Level sets of depth measures in abstract spaces

Alejandro Cholaquidis, Ricardo Fraiman, Leonardo Moreno

The lens depth of a point has been recently extended to general metric spaces, which is not the case for most depths. It is defined as the probability of being included in the inte…

math.ST2020

Level set and density estimation on manifolds

Alejandro Cholaquidis, Ricardo Fraiman, Leonardo Moreno

We tackle the problem of the estimation of the level sets L_f(λ) of the density f of a random vector X supported on a smooth manifold M\subsetR^d , from an iid sample of X. To do t…