Global convergence conditions in maximum likelihood estimation
发布时间：2022-09-05
点击次数：
发表刊物：International journal of control
关键字：MLE; optimisation; global/local convergence; consistency; asymptotic efficiency and sufficiency; non-local-minimum conditions
摘要：Maximum likelihood estimation has been widely applied in system identification because of consistency, its asymptotic efficiency and sufficiency. However, gradient-based optimisation of the likelihood function might end up in local convergence. In this article we derive various new non-local-minimum conditions in both open and closed-loop system when the noise distribution is a Gaussian process. Here we consider different model structures, in particular ARARMAX, BJ and OE models.
合写作者：William Heath
第一作者：Yiqun Zou*
论文类型：期刊论文
卷号：85
期号：5
页面范围：475–490
是否译文：否
发表时间：2012-05-05
收录刊物：SCI
发布期刊链接：https://www.tandfonline.com/doi/abs/10.1080/00207179.2012.658085