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Reconstruction of Conditional Expectations from Product Moments with Applications

Published

Author(s)

Robert C. Hagwood

Abstract

Regression calibration is a method for fitting models when the independent variable X is measured with error. In regression calibration, the noisy independent measurement W for X is calibrated with E[X | W] and the model is fitted using whatever method is appropriate using this replacement for X. An important component of this approach is using the data to find a good estimator of E[X | W]. In this paper, the function E[X | W = w] is estimated using the classical moment problem.
Citation
Journal of Computational and Applied Mathematics
Volume
276

Keywords

calibration, regression calibration, moment problem, Vandermonde.

Citation

Hagwood, R. (2015), Reconstruction of Conditional Expectations from Product Moments with Applications, Journal of Computational and Applied Mathematics, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=908278 (Accessed July 19, 2024)

Issues

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

Created April 9, 2015, Updated January 27, 2020