Clustering asymmetrical data with outliers: Parsimonious mixtures of contaminated mean-mixture of normal distributions
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Clustering asymmetrical data with outliers : Parsimonious mixtures of contaminated mean-mixture of normal distributions. / Naderi, Mehrdad; Nooghabi, Mehdi Jabbari.
I: Journal of Computational and Applied Mathematics, Bind 437, 115433, 2024.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Clustering asymmetrical data with outliers
T2 - Parsimonious mixtures of contaminated mean-mixture of normal distributions
AU - Naderi, Mehrdad
AU - Nooghabi, Mehdi Jabbari
N1 - Publisher Copyright: © 2023 Elsevier B.V.
PY - 2024
Y1 - 2024
N2 - Mixture modeling has emerged as a statistical tool to perform unsupervised model-based clustering for heterogeneous data. A framework of using contaminated mean-mixture of normal distributions as the components of the mixture model is designed to accommodate asymmetric data with outliers. Fourteen parsimonious variants of the postulated model are introduced by employing an eigenvalue decomposition of the component scale matrices. Simultaneously clustering and outliers detection is an outstanding advantage of the proposed model in analyzing non-normally distributed data. A computationally feasible and flexible EM-type algorithm is outlined for obtaining maximum likelihood parameter estimates. Moreover, the score vector and empirical information matrix for calculating asymptotic standard errors of the parameter estimates are derived by offering an information-based approach. The applicability of the proposed method is demonstrated through the analysis of simulated and real datasets with varying proportions of outliers.
AB - Mixture modeling has emerged as a statistical tool to perform unsupervised model-based clustering for heterogeneous data. A framework of using contaminated mean-mixture of normal distributions as the components of the mixture model is designed to accommodate asymmetric data with outliers. Fourteen parsimonious variants of the postulated model are introduced by employing an eigenvalue decomposition of the component scale matrices. Simultaneously clustering and outliers detection is an outstanding advantage of the proposed model in analyzing non-normally distributed data. A computationally feasible and flexible EM-type algorithm is outlined for obtaining maximum likelihood parameter estimates. Moreover, the score vector and empirical information matrix for calculating asymptotic standard errors of the parameter estimates are derived by offering an information-based approach. The applicability of the proposed method is demonstrated through the analysis of simulated and real datasets with varying proportions of outliers.
KW - Contaminated mean-mixture of normal distributions
KW - Eigenvalue decomposition
KW - EM-type algorithm
KW - Finite mixture model
KW - Outliers detection
U2 - 10.1016/j.cam.2023.115433
DO - 10.1016/j.cam.2023.115433
M3 - Journal article
AN - SCOPUS:85165544855
VL - 437
JO - Journal of Computational and Applied Mathematics
JF - Journal of Computational and Applied Mathematics
SN - 0377-0427
M1 - 115433
ER -
ID: 369175990