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Activity Number:
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131
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Type:
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Invited
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Date/Time:
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Monday, August 12, 2002 : 2:00 PM to 3:50 PM
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Sponsor:
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ENAR
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| Abstract - #301472 |
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Title:
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Generalized Functional Regression Models for Curve Data
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Author(s):
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Hans-Georg Mueller*+ and Ulrich Stadtmuller
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Affiliation(s):
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University of California, Davis and Universitat Ulm
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Address:
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One Shields Avenue, Davis, California, 95616, USA
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Keywords:
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Curve data ; Functional regression ; Classification of curves ; Biological trajectories
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Abstract:
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Data in the form of functions or curves are increasingly common in the life sciences. We consider regression models where the predictor is a random function, while the response is a scalar. We extend functional least squares to the case of a generalized functional regression model. A linear predictor is obtained by forming the scalar product of the predictor function with a smooth parameter function, and the expected value of the response is related to this linear predictor via a link function. If, in addition, a variance function is specified, this leads to a functional estimating equation. The special case of functional binomial regression can be utilized for classification and discrimination of stochastic processes and functional data. The necessary dimension reduction is achieved by approximating the predictor processes with a truncated Karhunen-Loeve expansion. We develop asymptotic inference for increasing truncation parameters as the sample size increases. We illustrate the methods with biological curve 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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