Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Tortora, Cristina"'
Autor:
Wallace, Meredith L., McTeague, Lisa, Graves, Jessica L., Kissel, Nicholas, Tortora, Cristina, Wheeler, Bradley, Iyengar, Satish
Finite mixture models that allow for a broad range of potentially non-elliptical cluster distributions is an emerging methodological field. Such methods allow for the shape of the clusters to match the natural heterogeneity of the data, rather than f
Externí odkaz:
http://arxiv.org/abs/2206.11465
Publikováno v:
In Computational Statistics and Data Analysis April 2024 192
Autor:
Tong, Hung, Tortora, Cristina
A mixture of multivariate contaminated normal (MCN) distributions is a useful model-based clustering technique to accommodate data sets with mild outliers. However, this model only works when fitted to complete data sets, which is often not the case
Externí odkaz:
http://arxiv.org/abs/2012.05394
Autor:
Punzo, Antonio, Tortora, Cristina
The multivariate contaminated normal (MCN) distribution represents a simple heavy-tailed generalization of the multivariate normal (MN) distribution to model elliptical contoured scatters in the presence of mild outliers, referred to as "bad" points.
Externí odkaz:
http://arxiv.org/abs/1810.08918
Autor:
Tortora, Cristina, Palumbo, Francesco
Publikováno v:
In Applied Soft Computing Journal November 2022 130
Publikováno v:
Journal of Developmental Education, 2021 Jan 01. 44(2), 18-25.
Externí odkaz:
https://www.jstor.org/stable/45381104
Akademický článek
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Publikováno v:
In Research in Transportation Business & Management September 2021 40
Publikováno v:
Pattern Recognition Letters, 58, 69-76 (2015)
Mixture models whose components have skewed hypercube contours are developed via a generalization of the multivariate shifted asymmetric Laplace density. Specifically, we develop mixtures of multiple scaled shifted asymmetric Laplace distributions. T
Externí odkaz:
http://arxiv.org/abs/1403.2285
A mixture of multiple scaled generalized hyperbolic distributions (MMSGHDs) is introduced. Then, a coalesced generalized hyperbolic distribution (CGHD) is developed by joining a generalized hyperbolic distribution with a multiple scaled generalized h
Externí odkaz:
http://arxiv.org/abs/1403.2332