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dc.contributor.authorWalker, Eric L.en_US
dc.contributor.authorStarnes, B. Aldenen_US
dc.contributor.authorBirch, Jeffrey B.en_US
dc.contributor.authorMays, James E.en_US
dc.date.accessioned2019-05-08T19:46:18Z
dc.date.available2019-05-08T19:46:18Z
dc.date.issued2010en
dc.identifier.urihttp://hdl.handle.net/10919/89412
dc.description.abstractThis article presents the application of a recently developed statistical regression method to the controlled instrument calibration problem. The statistical method of Model Robust Regression (MRR), developed byMays, Birch, and Starnes, is shown to improve instrument calibration by reducing the reliance of the calibration on a predetermined parametric (e.g. polynomial, exponential, logarithmic) model. This is accomplished by allowing fits from the predetermined parametric model to be augmented by a certain portion of a fit to the residuals from the initial regression using a nonparametric (locally parametric) regression technique. The method is demonstrated for the absolute scale calibration of silicon-based pressure transducers.en
dc.format.extent31 pagesen
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherAmerican Institute of Aeronautics and Astronauticsen
dc.relation.ispartof27th AIAA Aerodynamic Measurement Technology and Ground Testing Conferenceen
dc.relation.ispartofseriesTechnical Report No. 11-3en
dc.rightsThis work is free of known copyright restrictionsen
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/en
dc.titleModel Robust Calibration: Method and Application to Electronically-Scanned Pressure Transducersen
dc.typeTechnical reporten
dc.typePresentationen
dc.type.dcmitypeTexten
dc.identifier.sourceurlhttps://www.stat.vt.edu/content/dam/stat_vt_edu/graphics-and-pdfs/research-papers/Technical_Reports/TechReport11-3.pdfen


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