RR-classifier: a nonparametric classification procedure in multidimensional space based on relative ranks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F21%3A73610108" target="_blank" >RIV/61989592:15310/21:73610108 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/content/pdf/10.1007/s10182-021-00423-7.pdf" target="_blank" >https://link.springer.com/content/pdf/10.1007/s10182-021-00423-7.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10182-021-00423-7" target="_blank" >10.1007/s10182-021-00423-7</a>

Result language
angličtina
Original language name
RR-classifier: a nonparametric classification procedure in multidimensional space based on relative ranks
Original language description
Notions of data depth have motivated nonparametric multivariate analysis, especially in supervised learning. Maximum depth classifiers, classifiers based on depth-depth plots and depth distribution classifiers are nonparametric classification methodologies based on the notions of data depth and are Bayes-optimal rule under certain conditions. This paper proposes rank-rank plot for classification. Theoretical properties of the suggested classifier are investigated in some particular cases given by specific distributional assumptions. The performance of the proposed classification method is further investigated using simulated datasets.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability

Project
<a href="/en/project/EF17_049%2F0008408" target="_blank" >EF17_049/0008408: Hydrodynamic design of pumps</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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