.png){kind=icon}
.png){kind=icon}
Return
Nonparametric Testing for Hammerstein Systems
.png){kind=icon}
PRE
AI
DOI：10.1109/TAC.2022.3171389.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We examine the problem of testing a parametric form of the nonlinearity of Hammerstein systems against a nonparametric alternative. This article proposes test statistics relying on nonparametric orthogonal series regression function estimates. An asymptotic theory of the proposed tests is established including the asymptotic normality under the null hypothesis that the parametric form of the system nonlinearity is correct. Moreover, it is shown that under nonparametric alternatives, the probability of detecting that the parametric model is incorrect tends to one. The rate of detecting local alternatives is also established.
Keywords:
Central limit theorem
dependent data
Hammerstein systems
hermite functions
nonparametric testing
nonparametric identification
U-statistics
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W