This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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231
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 2, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #308603 |
Title:
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Functional Latent Feature Models
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Author(s):
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Naisyin Wang*+
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Companies:
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University of Michigan
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Address:
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Department of Statistics, University of Michigan, Ann Arbor, MI, 48109-1107,
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Keywords:
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functional PCA ;
longitudinal covariates
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Abstract:
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In this talk, we will focus on regression analysis that links functional covariate processes to a primary endpoint. We assume that the response depends on a finite number of latent features in the functional predictor as well as other predictors. We focus on the scenario where the basis functions are data driven, such as with functional principal components. Asymptotic properties are developed. Notably, we show that when the functional features are data driven, the parameter estimates have an increased asymptotic variance, due to the estimation error of the basis functions. Besides present the general methods, we will also present a new interaction models that accommodate the interactions among the functional and regular predictors. Numerical outcomes from simulation and data analysis studies are used to illustrate our findings.
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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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