论文标题
使用多变量正常高斯分布的混合物聚集偏斜数据的贝叶斯方法
A Bayesian approach for clustering skewed data using mixtures of multivariate normal-inverse Gaussian distributions
论文作者
论文摘要
非高斯混合物模型正在越来越多地注意基于混合模型的聚类，尤其是在处理具有偏斜和沉重尾巴等功能的数据时。在这里，基于多元正常逆高斯（MNIG）分布提出了这种混合物分布。为了进行混合物的参数估计，使用了通过Gibbs采样器的贝叶斯方法。为此，提供了一种新的模拟单变量的逆向高斯随机变量和基质的逆逆高斯随机矩阵的方法。提出的算法将应用于模拟和真实数据。通过模拟研究和实际数据分析，我们显示了参数恢复，并且我们的方法与其他聚类方法相比提供了竞争性聚类结果。
Non-Gaussian mixture models are gaining increasing attention for mixture model-based clustering particularly when dealing with data that exhibit features such as skewness and heavy tails. Here, such a mixture distribution is presented, based on the multivariate normal inverse Gaussian (MNIG) distribution. For parameter estimation of the mixture, a Bayesian approach via Gibbs sampler is used; for this, a novel approach to simulate univariate generalized inverse Gaussian random variables and matrix generalized inverse Gaussian random matrices is provided. The proposed algorithm will be applied to both simulated and real data. Through simulation studies and real data analysis, we show parameter recovery and that our approach provides competitive clustering results compared to other clustering approaches.
