27 Feb 2024 12:15

Detecting stable modes using stochastic gradients

Sala Riunioni B, Edificio ZETA - Campus Scientifico via Torino

Speaker: Federico Ferraccioli, Università degli Studi di Padova

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In the framework of density based clustering, modes represent a crucial concept as they identify high-density regions that can be associated to different groups of observations. The possibility to conduct inference on the resulting clusters remains nonetheless an intriguing yet quite intricate problem. To tackle this challenge, we propose a procedure that leverages on the connection between mean-shift clustering and stochastic gradient methods. This enables the definition of a sampling procedure, which can be used to construct confidence regions for the modes of a density. We investigate the asymptotic properties of the proposed method and evaluate its performances across different scenarios.

Bio Sketch:
Federico Ferraccioli is a junior Assistant professor (RTD-a) in Statistics at the Department of Statistical Sciences, University of Padova. previously he has been a postdoctoral researcher at JRC and at the Department of Statistical Sciences, University of Padova. His research interest cover high dimensional clustering, spatial statistics, functional data and semi-parametric methods.


L'evento si terrà in italiano


Dipartimento di Scienze Ambientali, Informatica e Statistica, Gruppo Statistica (Prosdocimi)

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