吉林大学学报(理学版) ›› 2019, Vol. 57 ›› Issue (5): 1127-1130.

• 数学 • 上一篇    下一篇

E-MS算法的收敛性

徐平峰, 陈婷, 尚来旭   

  1. 长春工业大学 数学与统计学院, 长春 130012
  • 收稿日期:2018-10-22 出版日期:2019-09-26 发布日期:2019-09-19
  • 通讯作者: 徐平峰 E-mail:xupf_stat@126.com

Convergence of E-MS Algorithm

XU Pingfeng, CHEN Ting, SHANG Laixu   

  1. School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
  • Received:2018-10-22 Online:2019-09-26 Published:2019-09-19
  • Contact: XU Pingfeng E-mail:xupf_stat@126.com

摘要: 考虑EMS算法的收敛性. 首先, 给出观测广义信息准则(GIC)最小值点的必要条件; 其次, 在模型空间有限性、 参数空间紧性、 Q函数连续性的条件下, 证明EMS算法产生序列的极限点满足观测GIC最小值点的必要性, 是对EMS算法全局收敛性的补充; 再次, 给出满足该必要条件但不满足全局收敛条件高斯图模型的一个实例; 最后, 证明EMS算法的全局收敛性.

关键词: 缺失数据, 模型选择, 观测GIC, E-MS算法

Abstract: We considered the convergence of EMS algorithm. Firstly, we gave a necessary condition for the minimum point of observed generalized information criterion (GIC). Secondly, under the conditions of finiteness of model space, compactness of parameter space, continuity of Q function, we proved that it was necessary for the limit points of the sequence generated by EMS algorithm to satisfy the minimum points of observed GIC, which was a supplement to the global convergence of EMS algorithm. Thirdly, we gave an example of Gaussian graphical model which satisfied the necessary condition, but did not satisfy conditions for global convergence. Finally, we proved the global convergence of E-MS algorithm.

Key words: missing data, model selection, observed GIC, E-MS algorithm

中图分类号: 

  • O212.1