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Activity Number:
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94
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Type:
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Invited
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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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IMS
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| Abstract - #302916 |
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Title:
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On Varying Coefficient Models Stratified by a Functional Covariate
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Author(s):
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Jianhua Huang*+
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Companies:
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Texas A&M University
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Address:
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Department of Statistics, College Station, TX, 77843-3143,
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Keywords:
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nonparametric regression ; semiparametric regression ; functional data ; functional principal components ; kernel methods
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Abstract:
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A varying coefficient linear model can be interpreted as a conditional linear model where a linear model is assumed when a conditional variable is held at a fixed value. This kind of model has attracted lots of attention because it provides a nice trade-off between model interpretability and flexibility. In this talk, we consider an extension of such model where the conditional variable is a function. Two methods are discussed, one is based on kernel regression, the other is based on functional principal components reduction. We also consider the case when some regression coefficients are not varying and discuss the semiparametric efficiency in estimating these coefficients.
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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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