The Relationship Between Information, Sampling Rates, and Parameter Estimation Models
- 1 December 2002
- journal article
- Published by ASME International in Journal of Heat Transfer
- Vol. 124 (6) , 1192-1199
- https://doi.org/10.1115/1.1513581
Abstract
To estimate parameters from experiments requires the specification of models and each model will exhibit different degrees of sensitivity to the parameters sought. Although experiments can be optimally designed without regard to the experimental data actually realized, the precision of the estimated parameters is a function of the sensitivity and the statistical characteristics of the data. The precision is affected by any correlation in the data, either auto or cross, and by the choice of the model used to estimate the parameters. An informative way of looking at an experiment is by using the concept of Information. An analysis of an actual experiment is used to show how the information, the optimal number of sensors, the optimal sampling rates, and the model are affected by the statistical nature of the signals. The paper demonstrates that one must differentiate between the data needed to specify the model and the precision in the estimated parameters provided by the data.Keywords
This publication has 7 references indexed in Scilit:
- Using the Concept of Information to Optimally Design Experiments With Uncertain ParametersJournal of Heat Transfer, 2001
- Uncertainty Estimation in the Determination of Thermal Conductivity of 304 Stainless SteelPublished by ASME International ,2000
- Optimal experiment designMeasurement Science and Technology, 1998
- Statistical Data AnalysisPublished by Oxford University Press (OUP) ,1998
- Comparison of some inverse heat conduction methods using experimental dataInternational Journal of Heat and Mass Transfer, 1996
- Design of Experiments Using Uncertainty InformationJournal of Heat Transfer, 1996
- Estimation and testing for functional form and autocorrelation: A simultaneous approachJournal of Econometrics, 1978