This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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338
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #309004 |
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Title:
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Estimating the Effect of Covariates on Functional Shape
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Author(s):
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Kert Viele*+ and Robin Cooper
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Companies:
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University of Kentucky and University of Kentucky
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Address:
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Department of Statistics, Lexington, KY, 40506-0027,
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Keywords:
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Self-Modeling Regression ;
Functional Data Analysis ;
Synaptic Transmission
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Abstract:
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Self-Modeling regression (SEMOR) provides a parsimonious method for analyzing a set of functions which have a similar shape. The central challenges of SEMOR modeling are estimating the underlying shape function nonparametrically and then estimating the variation in the time axis across curves.
In synaptic transmission data, a nerve cell is repeatedly stimulated with electricity and the voltage is recorded over time. These datasets have been shown to be very closely modeled by a SEMOR structure.
In our talk we illustrate the effect of pharmacological interventions on synaptic transmission, including changes in the self-modeling coefficients and changes in the underlying shape function.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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