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Static finite mixture model of multivariate skew-normal distributions to cluster multivariatetime series based on generalized autoregressive score approach | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 3، دوره 16، شماره 4، تیر 2025، صفحه 27-39 اصل مقاله (938.56 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.23427.2540 | ||
| نویسندگان | ||
| Solmaz Yaghoubi1؛ Rahman Farnoosh* 2 | ||
| 1Science and Research Branch, Islamic Azad University, Tehran, Iran | ||
| 2School of Mathematics, Iran University of Science and Technology, Tehran, Iran | ||
| تاریخ دریافت: 27 اردیبهشت 1400، تاریخ بازنگری: 03 مهر 1400، تاریخ پذیرش: 02 آبان 1400 | ||
| چکیده | ||
| This paper proposes an observation-driven finite mixture model for clustering high-dimension data. A simple algorithm using static hidden variables statically clusters the data into separate model components. The model accommodates normal and skew-normal distributed mixtures with time-varying component means, covariance matrices and skewness coefficient. These parameters are estimated using the EM algorithm and updated with the Generalized Autoregressive Scale (GAS) approach. Our proposed model is preferably clustered using a skew-normal distribution rather than a normal distribution when dealing with real data that may be skewed and asymmetrical. Finally, our proposed model will be evaluated using a simulation study and the results will be discussed using a real data set. | ||
| کلیدواژهها | ||
| clustering؛ finite mixture model؛ skew normal distribution؛ generalized autoregressive score؛ time series | ||
| مراجع | ||
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