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

UR - http://www.scopus.com/inward/record.url?scp=85165544855&partnerID=8YFLogxK

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 -