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Activity Number:
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307
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Type:
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Invited
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Date/Time:
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Tuesday, July 31, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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| Abstract - #308182 |
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Title:
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Iterative Functional Principal Component Analysis for Correlation Reduction
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Author(s):
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Fang Yao*+ and C. M. Lee
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Companies:
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University of Toronto and Colorado State University
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Address:
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100 St. George Street, Toronto, ON, M5S 3G3, Canada
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Keywords:
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Asymptotic ; Functional data ; Penalized spline regression ; Principal components ; Smoothing ; Within-subject correlation
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Abstract:
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We propose an iterative estimation procedure for performing functional principal component analysis. The procedure aims at functional or longitudinal data where the repeated measurements from the same subject are correlated. For the handling of the within-subject correlation, we develop an iterative procedure which would gradually reduce the dependence amongst the repeated measurements made for the same subject. An increasingly popular smoothing approach, penalized spline regression, is used to represent the mean trend. This allows straightforward incorporation of covariates and simple implementation of inference procedures for coefficients. The resulting data after iteration are theoretically shown to be asymptotically independent, which suggests that the general theory of penalized spline regression developed for independent data can also be applied to functional data.
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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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