文章基本信息

标题：Newton-based maximum likelihood estimation in nonlinear state space models *
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作者：Manon Kok ; Johan Dahlin ; Thomas B. Schön 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：28
页码：398-403
DOI：10.1016/j.ifacol.2015.12.160
语种：English
出版社：Elsevier
摘要：AbstractMaximum likelihood (ML) estimation using Newton's method in nonlinear state space models (SSMs) is a challenging problem due to the analytical intractability of the loglikelihood and its gradient and Hessian. We estimate the gradient and Hessian using Fisher's identity in combination with a smoothing algorithm. We explore two approximations of the log-likelihood and of the solution of the smoothing problem. The first is a linearization approximation which is computationally cheap, but the accuracy typically varies between models. The second is a sampling approximation which is asymptotically valid for any SSM but is more computationally costly. We demonstrate our approach for ML parameter estimation on simulated data from two different SSMs with encouraging results.
关键词：KeywordsMaximum likelihoodparameter estimationnonlinear state space modelsFisher's identityextended Kalman filtersparticle methodsNewton optimization