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20212024
most citedHalfspace depth for general measures: The ray basis theorem and its consequences

8 citations · 15 across the 7 of their papers we have counts for

collaborators

7 papers

math.ST2024

Theoretical properties of angular halfspace depth

Stanislav Nagy, Petra Laketa

The angular halfspace depth (ahD) is a natural modification of the celebrated halfspace (or Tukey) depth to the setup of directional data. It allows us to define elements of nonpar…

stat.ME2022★ 4 cited

Another look at halfspace depth: Flag halfspaces with applications

Dušan Pokorný, Petra Laketa, Stanislav Nagy

The halfspace depth is a well studied tool of nonparametric statistics in multivariate spaces, naturally inducing a multivariate generalisation of quantiles. The halfspace depth of…

stat.CO2022

Exact and approximate computation of the scatter halfspace depth

Xiaohui Liu, Yuzi Liu, Petra Laketa +2

The scatter halfspace depth (sHD) is an extension of the location halfspace (also called Tukey) depth that is applicable in the nonparametric analysis of scatter. Using sHD, it is…

math.ST2022

Partial reconstruction of measures from halfspace depth

Petra Laketa, Stanislav Nagy

The halfspace depth of a -dimensional point with respect to a finite (or probability) Borel measure in is defined as the infimum of the -masses of all…

stat.CO2022★ 3 cited

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…

math.MG2021

Integrated shape-sensitive functional metrics

Sami Helander, Petra Laketa, Pauliina Ilmonen +3

This paper develops a new integrated ball (pseudo)metric which provides an intermediary between a chosen starting (pseudo)metric d and the L_p distance in general function spaces.…