首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Constrained monotone EM algorithms for mixtures of multivariate <Emphasis Type="Italic">t</Emphasis> distributions
Authors:F Greselin  S Ingrassia
Institution:(1) Department of Applied-Mathematics, National Chung Hsing University, Taichung, Taiwan;(2) Graduate Institute of Finance, National Chiao Tung University, Hsinchu, Taiwan;(3) Institute of Statistics, National Chiao Tung University, Hsinchu, Taiwan
Abstract:Mixtures of multivariate t distributions provide a robust parametric extension to the fitting of data with respect to normal mixtures. In presence of some noise component, potential outliers or data with longer-than-normal tails, one way to broaden the model can be provided by considering t distributions. In this framework, the degrees of freedom can act as a robustness parameter, tuning the heaviness of the tails, and downweighting the effect of the outliers on the parameters estimation. The aim of this paper is to extend to mixtures of multivariate elliptical distributions some theoretical results about the likelihood maximization on constrained parameter spaces. Further, a constrained monotone algorithm implementing maximum likelihood mixture decomposition of multivariate t distributions is proposed, to achieve improved convergence capabilities and robustness. Monte Carlo numerical simulations and a real data study illustrate the better performance of the algorithm, comparing it to earlier proposals.
Keywords:
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号