20 citations · 53 across the 5 of their papers we have counts for
6 papers
Approximate computation of projection depths
Rainer Dyckerhoff, Pavlo Mozharovskyi, Stanislav Nagy
Data depth is a concept in multivariate statistics that measures the centrality of a point in a given data cloud in $\IR^d$. If the depth of a point can be represented as the minim…
Uniform convergence rates for the approximated halfspace and projection depth
Stanislav Nagy, Rainer Dyckerhoff, Pavlo Mozharovskyi
The computational complexity of some depths that satisfy the projection property, such as the halfspace depth or the projection depth, is known to be high, especially for data of h…
Convergence of depths and depth-trimmed regions
Rainer Dyckerhoff
Depth is a concept that measures the `centrality' of a point in a given data cloud or in a given probability distribution. Every depth defines a family of so-called trimmed regions…
Depth and depth-based classification with R-package ddalpha
Oleksii Pokotylo, Pavlo Mozharovskyi, Rainer Dyckerhoff
Following the seminal idea of Tukey, data depth is a function that measures how close an arbitrary point of the space is located to an implicitly defined center of a data cloud. Ha…
Exact computation of the halfspace depth
Rainer Dyckerhoff, Pavlo Mozharovskyi
For computing the exact value of the halfspace depth of a point w.r.t. a data cloud of points in arbitrary dimension, a theoretical framework is suggested. Based on this framew…
Depth-based Runs Tests for Bivariate Central Symmetry
Rainer Dyckerhoff, Christophe Ley, Davy Paindaveine
McWilliams (1990) introduced a nonparametric procedure based on runs for the problem of testing univariate symmetry about the origin (equivalently, about an arbitrary specified cen…