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Activity Number:
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254
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 4, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #303399 |
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Title:
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Functional Mixture Regression
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Author(s):
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Fang Yao*+ and Yuejiao Fu and Thomas C.M. Lee
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Companies:
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University of Toronto and York University and Chinese University of Hong Kong/Colorado State University
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Address:
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Department of Statistics, Toronto, ON, M5S 3G3, Canada
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Keywords:
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Estimated design ; Functional linear model ; Functional principal components ; Mixture regression ; Regularization
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Abstract:
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In classical functional linear models the relationship between the scalar response and the functional predictor process is often assumed to be identical for all subjects. Motivated by both practical and methodological considerations, we relax this assumption and propose a new class of functional regression models that allow the regression structure to vary for different groups of subjects. These new functional regression models are simplified to a framework that is similar to classical mixture regression models. This leads to the proposed approach named as Functional Mixture Regression (FMR). The practical need and empirical performance of FMR are illustrated through numerical simulations and applications to a fertility and longevity analysis of female medflies. Corresponding results demonstrate that the proposed approach achieves substantial gains over functional linear models.
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