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Empirical Modeling Methods Using Partial Data

Published

Author(s)

Gerard N. Stenbakken, Ruimin Liu, Gene Huang

Abstract

Methods were developed to calculate empirical models for device error behavior from data sets with missing data. These models can be used to develop reduced point testing procedures for the devices. The partial data methods reduce the prediction uncertainty for test points that have more modeling data available relative to the prediction uncertainty of partial data test points. Simulations show that the prediction uncertainty for full data test points are comparable to the case where the "missing" data is "known." When these methods are applied to real data where the underlying model has changed the improvements are less than the simulations predict.
Proceedings Title
Proc. IEEE Instrumentation and Technology Conference (IMTC)
Conference Dates
May 21-23, 2002
Conference Location
Anchorage, AK, USA
Conference Title
IEEE Instrumentation and Measurement Conference

Keywords

error model, missing data, partial data, system identification, calibration

Citation

Stenbakken, G. , Liu, R. and Huang, G. (2002), Empirical Modeling Methods Using Partial Data, Proc. IEEE Instrumentation and Technology Conference (IMTC), Anchorage, AK, USA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=33111 (Accessed July 15, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created March 31, 2002, Updated October 12, 2021