Add to ORKGThree different concepts of depth in a point set are considered and compared: Convex depth, location depth and Delaunay depth. As a notion of weight is naturally associated to each depth definition, we also present results on minimum weight structures (like spanning trees, poligonizations and triangulations) with respect to the three variations.DURSYMinisterio de Ciencia y Tecnologí
Data depth is a central topic in order statistics and data analysis. However, the increasing needs o...
Classical multivariate statistics measures the outlyingness of a point by its Mahalanobis distance f...
Abstract. Several measures of data depth have been proposed, each attempting to maintain certain rob...
Every notion of depth induces a stratification of the plane in regions of points with the same depth...
The concept of location depth was introduced in statistics as a way to extend the univariate notion ...
Abstract. This paper describes the software DEpthLAUNAY. The main goal of the application is to comp...
Given a set P = {p1,..., pn} of points and a point q in the plane, we define a function ψ(q) that pr...
The concept of location depth was introduced as a way to extend the univariate notion of ranking to ...
International audienceGiven a set P of n points in the plane, the Oja depth of a point x is defined ...
A collection of n hyperplanes in R d forms a hyperplane arrangement. The depth of a point ` 2 R d...
The concept of location depth was introduced as a way to extend the univariate notion of ranking to ...
Given a set S = , the depth #(Q) of a point Q is the minimum number of points of S that ...
Little known relations of the renown concept of the halfspace depth for multivariate data with notio...
In this short article, we consider the notion of data depth which generalizes the me-ian to higher d...
Regression depth, introduced by Rousseeuw and Hubert in 1999, is a notion that measures how good of ...
Data depth is a central topic in order statistics and data analysis. However, the increasing needs o...
Classical multivariate statistics measures the outlyingness of a point by its Mahalanobis distance f...
Abstract. Several measures of data depth have been proposed, each attempting to maintain certain rob...
Every notion of depth induces a stratification of the plane in regions of points with the same depth...
The concept of location depth was introduced in statistics as a way to extend the univariate notion ...
Abstract. This paper describes the software DEpthLAUNAY. The main goal of the application is to comp...
Given a set P = {p1,..., pn} of points and a point q in the plane, we define a function ψ(q) that pr...
The concept of location depth was introduced as a way to extend the univariate notion of ranking to ...
International audienceGiven a set P of n points in the plane, the Oja depth of a point x is defined ...
A collection of n hyperplanes in R d forms a hyperplane arrangement. The depth of a point ` 2 R d...
The concept of location depth was introduced as a way to extend the univariate notion of ranking to ...
Given a set S = , the depth #(Q) of a point Q is the minimum number of points of S that ...
Little known relations of the renown concept of the halfspace depth for multivariate data with notio...
In this short article, we consider the notion of data depth which generalizes the me-ian to higher d...
Regression depth, introduced by Rousseeuw and Hubert in 1999, is a notion that measures how good of ...
Data depth is a central topic in order statistics and data analysis. However, the increasing needs o...
Classical multivariate statistics measures the outlyingness of a point by its Mahalanobis distance f...
Abstract. Several measures of data depth have been proposed, each attempting to maintain certain rob...