Abstract
Central tendency of interval-valued random elements has been mainly described in terms of different notions of medians and location M-estimators in the literature, whereas the approach consisting of medians and trimmed means based on a depth function has been rarely considered. Recently, depth-based trimmed means have been adapted to the more general framework of fuzzy number-valued data in terms of the so-called \(D_\theta \)-depth. The aim of this work is to study the empirical behaviour of the particularization of such a location measure when data are interval-valued.
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Acknowledgement
This research has been partially supported by the Spanish Ministry of Science, Innovation and Universities (Grant MTM-PID2019-104486GB-I00) and by Principality of Asturias/FEDER Grants (SV-PA-21-AYUD/2021/50897).
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Sinova, B. (2023). The \(d_\theta \)-Depth-Based Interval Trimmed Mean. In: García-Escudero, L.A., et al. Building Bridges between Soft and Statistical Methodologies for Data Science . SMPS 2022. Advances in Intelligent Systems and Computing, vol 1433. Springer, Cham. https://doi.org/10.1007/978-3-031-15509-3_46
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DOI: https://doi.org/10.1007/978-3-031-15509-3_46
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