论文标题
偏斜次高斯稳定分布的有限混合物
Finite mixture of skewed sub-Gaussian stable distributions
论文作者
论文摘要
我们提出了偏斜次高斯稳定分布的有限混合物。提出的有限混合模型参数的最大似然估计器是通过预期最大化算法计算的。所提出的模型包含正常分布和偏斜的正常分布的有限混合物。由于所提出的模型的尾巴甚至比学生的T分布重，因此可以用作基于强大模型集群的强大模型。通过聚类模拟数据和两组真实数据来证明所提出的模型的性能。
We propose the finite mixture of skewed sub-Gaussian stable distributions. The maximum likelihood estimator for the parameters of proposed finite mixture model is computed through the expectation-maximization algorithm. The proposed model contains the finite mixture of normal and skewed normal distributions. Since the tails of proposed model is heavier than even the Student's t distribution, it can be used as a powerful model for robust model-based clustering. Performance of the proposed model is demonstrated by clustering simulation data and two sets of real data.
