[Forum SIS] Seminario Micheal Fop - 1 Giugno

[Ilaria Prosdocimi]

Cari colleghi e colleghe,

Con grande piacere, vi segnalo il prossimo seminario del gruppo Statistica
al DAIS, Ca' Foscari (tutti i futuri seminari sono elencati alla pagina del
gruppo https://www.unive.it/pag/16818):

Data: 1 Giugno, ore 14:00-15:00
Relatore: Michael Fop (University College Dublin)
Titolo:  A composite likelihood approach for model-based clustering of
high-dimensional data

Il seminario si potrà seguire tramite la piattaforma Zoom:
https://unive.zoom.us/j/82776377762
Meeting ID: 827 7637 7762 - Passcode: SanMarco1

Abstract:
The use of finite Gaussian mixture models (GMMs) is a well established
approach to performing model-based clustering. Despite the popularity of
GMMs, their widespread use is hindered by their inability to transfer to
high-dimensional data settings. Difficulties related to dealing with
high-dimensional covariance matrices and highly correlated data often makes
the use of GMMs impractical. The composite likelihood (CL) approach uses
smaller dimensional marginal and/or conditional pseudo-likelihoods to
estimate the parameters of a model, avoiding the need to fully specify the
underlying joint distribution. Such an approximation is very helpful when
the full model is difficult to specify or manipulate, overcoming the
computational problems often arising when dealing with a multi-dimensional
joint distribution. In addition, the specification of appropriate
conditional likelihoods allows the modelling of the dependence structure by
means of lower dimensional terms.
This talk presents a framework that exploits the idea of embedding CL in
the area of GMMs for clustering high-dimensional data. The framework
explores the use of approximations to the likelihood of a GMM by means of
block-pairwise and block-conditional composite likelihoods, which allow the
decomposition of the potentially high-dimensional density into terms of
smaller dimensions. Estimation is based on a computationally efficient
expectation-maximization algorithm, enabling the use of GMMs for clustering
high-dimensional data. The approach is demonstrated through simulated and
real data examples.
Talk based on joint work with Claire Gormley (University College Dublin),
Adrian O'Hagan (University College Dublin), Ioannis Kosmidis (University of
Warwick), Dimitris Karlis (Athens University of Economics and Business),
and Caitriona Ryan (Maynooth University).

Cordiali saluti

Ilaria Prosdocimi

----
Ilaria Prosdocimi
Assistant Professor in Statistics
Ca' Foscari University of Venice
Department of Environmental Sciences, Informatics and Statistic
prosdocimi.ilaria a gmail.com
ilaria.prosdocimi a unive.it
-------------- parte successiva --------------
Un allegato HTML è stato rimosso...
URL: <http://www.stat.unipg.it/pipermail/sis/attachments/20210527/19df2e61/attachment.html>