文章基本信息

标题：Tuning of Hyperparameters for FIR models – an Asymptotic Theory
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作者：Biqiang Mu ; Tianshi Chen ; Lennart Ljung 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：2818-2823
DOI：10.1016/j.ifacol.2017.08.633
语种：English
出版社：Elsevier
摘要：AbstractRegularization of simple linear regression models for system identification is a recent much-studied problem. Several parameterizations (“kernels”) of the regularization matrix have been suggested together with different ways of estimating (“tuning”) its parameters. This contribution defines an asymptotic view on the problem of tuning and selection of kernels. It is shown that the SURE approach to parameter tuning provides an asymptotically consistent estimate of the optimal (in a MSE sense) hyperparameters. At the same time it is shown that the common marginal likelihood (empirical Bayes) approach does not enjoy that property.
关键词：KeywordsLinear system identificationGaussian process regressionKernel-based regularizationMarginal likelihood estimatorsStein’s unbiased risk estimators