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pro vyhledávání: '"Laketa, Petra"'
Autor:
Nagy, Stanislav, Laketa, Petra
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 nonparametric inference, such as the median, the inter-quantile regions, or
Externí odkaz:
http://arxiv.org/abs/2402.08285
Publikováno v:
Journal of Nonparametric Statistics, 36(1), 165-181, 2024
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 a point with respect to a measure is defined as the infimum mass of c
Externí odkaz:
http://arxiv.org/abs/2209.11445
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 possible to define minimax optimal robust scatter estimators for multi
Externí odkaz:
http://arxiv.org/abs/2208.05173
Publikováno v:
Journal of Computational and Graphical Statistics, 33(2), 699-713, 2023
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, defined as sets of points in the $d$-dimensional space whose Tukey depth ex
Externí odkaz:
http://arxiv.org/abs/2208.04587
Autor:
Laketa, Petra, Nagy, Stanislav
The halfspace depth of a $d$-dimensional point $x$ with respect to a finite (or probability) Borel measure $\mu$ in $\mathbb{R}^d$ is defined as the infimum of the $\mu$-masses of all closed halfspaces containing $x$. A natural question is whether th
Externí odkaz:
http://arxiv.org/abs/2208.03959
The behavior of a generalized random environment integer-valued autoregressive model of higher order with geometric marginal distribution {and negative binomial thinning operator} (abbrev. $RrNGINAR(\mathcal{M,A,P})$) is dictated by a realization $\{
Externí odkaz:
http://arxiv.org/abs/2109.00476
Autor:
Helander, Sami, Laketa, Petra, Ilmonen, Pauliina, Nagy, Stanislav, Van Bever, Germain, Viitasaari, Lauri
Publikováno v:
J. Multivariate Anal. 189, 104880 (2022)
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. Selecting d as the Hausdorff or Fr\'echet distances, we introduce inte
Externí odkaz:
http://arxiv.org/abs/2107.08917
Autor:
Laketa, Petra, Nagy, Stanislav
Publikováno v:
Stat Papers 63, 849-883 (2022)
The halfspace depth is a prominent tool of nonparametric multivariate analysis. The upper level sets of the depth, termed the trimmed regions of a measure, serve as a natural generalization of the quantiles and inter-quantile regions to higher-dimens
Externí odkaz:
http://arxiv.org/abs/2106.00616
Akademický článek
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Autor:
Laketa, Petra1 (AUTHOR) laketa@karlin.mff.cuni.cz, Nagy, Stanislav1 (AUTHOR)
Publikováno v:
Statistical Analysis & Data Mining. Aug2023, Vol. 16 Issue 4, p358-373. 16p.