Yang, X.; Dong, L.; Zhu, Z.; Wu, W. Statistical Inference for Finite Mixture of Matrix Variate t-distribution. Preprints2023, 2023010524. https://doi.org/10.20944/preprints202301.0524.v1
Yang, X., Dong, L., Zhu, Z., & Wu, W. (2023). Statistical Inference for Finite Mixture of Matrix Variate t-distribution. Preprints. https://doi.org/10.20944/preprints202301.0524.v1
Yang, X., Zhichuan Zhu and Weisan Wu. 2023 "Statistical Inference for Finite Mixture of Matrix Variate t-distribution" Preprints. https://doi.org/10.20944/preprints202301.0524.v1
In the era of big data with increasingly complex data structures and ever-larger data scales, matrix-type data are becoming highly valued and their applications in the fields of medicine, industry, education, geography, and astronomy are growing in extent. In recent years, significant progress has been made in the practical use of matrix variable t-distribution finite mixture models for handling data in order to address the issues of multi-subgroup structures and long data tails. In this paper, the expectation-maximization (EM) algorithm with penalized maximum likelihood is proposed to resolve the problem of the unbounded nature of the likelihood function applied to the model by considering the degeneracy of the variance-covariance matrix of this model. Our data were analyzed through simulations and real data, and the results demonstrate that our model is effective in both preventing the likelihood function from being unbounded and in ensuring the accuracy of the estimated parameters of the EM algorithm.
Computer Science and Mathematics, Probability and Statistics
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.