Rigorous parameter estimation for noisy mixed-effects models

Alexander S. Danis, Andrew C. Hooker, Warwick Tucker

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

Abstract

We describe how constraint propagation techniques can be used to reliably reconstruct model parameters from noisy data. The main algorithm combines a branch and bound procedure with a data inflation step; it is robust and insensitive to noise. The set–valued results are transformed into point clouds, after which statistical properties can be retrieved. We apply the presented method to a mixed-effects model.
Original languageEnglish
Title of host publicationProceedings of the 2010 International Symposium on Nonlinear Theory and its Applications (NOLTA2010)
Place of PublicationJapan
PublisherInstitute of Electronics, Information and Communications Engineers (IEICE)
Pages67-70
Number of pages4
Publication statusPublished - 2010
Externally publishedYes

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