Evaluation of Gaussian Linear Model Order Selection Approaches

摘要：

Model order selection approaches are usually evaluated in simulations by comparing the resulting model orders to the true model order. In this paper, the mean Kullback-Leibler divergence (MKD) between the selected model and the true model is proposed as an objective measure for evaluating different model order selection approaches in simulations. For Gaussian linear model order selection problems the Kullback-Leibler divergence are reduced to simple forms and the MKD can be easily computed. Simulation results show that the MKD is a reasonable measure to evaluate different Gaussian Linear model order selection approaches, in terms of signal processing.

关键词： Gaussian Linear model order model order selection MKD AIC MDL

作者: Du Yu-Ming Du Xiao-dan Zhang Fu-gui

作者单位: Electronic Engineering School of ChengDu University of Information Technology Chengdu, Sichuan 61022 Information Science & Technology School of ChengDu University Chengdu, Sichuan 610106, China

会议类型: 国际会议

会议名称: 2009 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2009)(2009年检测技术与机电自动化国际会议)

会议地点: 张家界

会议语种:英文

页码: 767-770

在线出版日期: 2009-04-11（万方平台首次上网日期，不代表论文的发表时间）