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Metric Oja depth, new statistical tool for estimating the most central objects; 
Tekijät: Zamanifarizhandi, Vida; Virta, Joni
Kustantaja: Elsevier BV
Julkaisuvuosi: 2026
Lehti: Computational Statistics and Data Analysis
Artikkelin numero: 108447
Vuosikerta: 224
ISSN: 0167-9473
eISSN: 1872-7352
DOI: https://doi.org/10.1016/j.csda.2026.108447
Julkaisun avoimuus kirjaamishetkellä: Avoimesti saatavilla
Julkaisukanavan avoimuus : Osittain avoin julkaisukanava
Verkko-osoite: https://doi.org/10.1016/j.csda.2026.108447
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/526966556
Rinnakkaistallenteen lisenssi: CC BY
Rinnakkaistallennetun julkaisun versio: Kustantajan versio
The Oja depth (simplicial volume depth) is one of the classical statistical techniques for measuring the central tendency of data in multivariate space. Despite the widespread emergence of object data like images, texts, matrices or graphs, a well-developed and suitable version of Oja depth for object data is lacking. To address this shortcoming, a novel measure of statistical depth, the metric Oja depth applicable to any object data, is proposed. Two competing strategies are used for optimizing metric depth functions, i.e., for finding the deepest objects among the sample. The performance of the metric Oja depth is compared with three other depth functions (half-space, lens, and spatial) in diverse data scenarios.
Avainsanat:
metric Oja depth, metric statistics, object data, optimisation, statistical depth
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
The work of VZ was supported by the Finnish Doctoral Program Network in Artificial Intelligence, AI-DOC (decision number VN/3137/2024-OKM-6). The work of VZ and JV was supported by the Research Council of Finland (Grants 347501, 353769, 368494).
