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Activity Number:
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120
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #303588 |
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Title:
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Nonparametric Derivative Estimation and the Computation of Posterior Probabilities for Nanoparticle Characteristics
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Author(s):
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Richard Charnigo*+ and Mathieu Francoeur and Patrick Kenkel and M. Pinar Menguc and Benjamin K. Hall and Cidambi Srinivasan
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Companies:
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University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky and University of Kentucky
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Address:
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121 Washington Avenue, Lexington, KY, 40536-0200,
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Keywords:
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compound estimation ; self-consistency ; inverse problem ; nanoscience ; pattern recognition
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Abstract:
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The characterization of nanoparticles from surface wave scattering data is of great interest in applied engineering because of its potential to advance nanoparticle-based manufacturing concepts. Meanwhile, a recent development in methodology for the nonparametric estimation of a mean response function and its derivatives has provided a valuable tool for nanoparticle characterization: namely, a mechanism to identify the most plausible configuration for a collection of nanoparticles given the estimated derivatives of surface wave scattering profiles from those nanoparticles. In this talk, after briefly reviewing the preceding work, we propose an extension that additionally furnishes posterior probabilities for the various possible configurations of nanoparticles. We then present results from an empirical study assessing this extended approach to nanoparticle characterization.
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