24 citations · 157 across the 42 of their papers we have counts for
7 papers · 1 filter
Massive parallelization of projection-based depths
Leonardo Leone, Pavlo Mozharovskyi, David Bounie
This article introduces a novel methodology for the massive parallelization of projection-based depths, addressing the computational challenges of data depth in high-dimensional sp…
On exact computation of Tukey depth central regions
Vít Fojtík, Petra Laketa, Pavlo Mozharovskyi +1
The Tukey (or halfspace) depth extends nonparametric methods toward multivariate data. The multivariate analogues of the quantiles are the central regions of the Tukey depth, defin…
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…
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…
Tukey depth: linear programming and applications
Pavlo Mozharovskyi
Determining the representativeness of a point within a data cloud has recently become a desirable task in multivariate analysis. The concept of statistical depth function, which re…
Fast computation of Tukey trimmed regions and median in dimension
Xiaohui Liu, Karl Mosler, Pavlo Mozharovskyi
Given data in , a Tukey -trimmed region is the set of all points that have at least Tukey depth w.r.t. the data. As they are visual, affine equivariant and r…
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…