TY - JOUR
T1 - Model-based clustering using a new multivariate skew distribution
AU - Tomarchio, Salvatore D.
AU - Bagnato, Luca
AU - Punzo, Antonio
PY - 2023
Y1 - 2023
N2 - Quite often real data exhibit non-normal features, such as asymmetry and heavy tails, and present a latent group structure. In this paper, we first propose the multivariate skew shifted exponential normal distribution that can account for these non-normal characteristics. Then, we use this distribution in a finite mixture modeling framework. An EM algorithm is illustrated for maximum-likelihood parameter estimation. We provide a simulation study that compares the fitting performance of our model with those of several alternative models. The comparison is also conducted on a real dataset concerning the log returns of four cryptocurrencies.
AB - Quite often real data exhibit non-normal features, such as asymmetry and heavy tails, and present a latent group structure. In this paper, we first propose the multivariate skew shifted exponential normal distribution that can account for these non-normal characteristics. Then, we use this distribution in a finite mixture modeling framework. An EM algorithm is illustrated for maximum-likelihood parameter estimation. We provide a simulation study that compares the fitting performance of our model with those of several alternative models. The comparison is also conducted on a real dataset concerning the log returns of four cryptocurrencies.
KW - Clustering
KW - Multivariate skew distribution
KW - Clustering
KW - Multivariate skew distribution
UR - http://hdl.handle.net/10807/252214
U2 - 10.1007/s11634-023-00552-8
DO - 10.1007/s11634-023-00552-8
M3 - Article
SN - 1862-5347
SP - 1
EP - 23
JO - Advances in Data Analysis and Classification
JF - Advances in Data Analysis and Classification
ER -