Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this technical note fundamental integral limitations are derived on the variance of estimated parametric models, for both open and closed loop identification. As an application of these results  show that, for multisine inputs, a ll known asymptotic (in model order) variance expression provides upper bounds on the actual variance of the estimated models for finite model orders. The fundamental limitations established here give rise to a dasiawater-bedpsila effect, which is illustrated in an example.
['Cristian R. Rojas', 'James S. Welsh', 'Juan C. Agüero']
I am writing a research paper on a idea Fundamental Limitations on the Variance of Estimated Parametric Models
[{"from": "human", "value": "I am writing a research paper on a idea Fundamental Limitations on the Variance of Estimated Parametric Models"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this technical note fundamental integral limitations are derived on the variance of estimated parametric models, for both open and closed loop identification. As an application of these results  show that, for multisine inputs, a ll known asymptotic (in model order) variance expression provides upper bounds on the actual variance of the estimated models for finite model orders. The fundamental limitations established here give rise to a dasiawater-bedpsila effect, which is illustrated in an example."}]