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Activity Number:
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129
- Quantile and Nonparametric Regression Models
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Type:
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Contributed
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Date/Time:
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Monday, July 31, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #324513
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View Presentation
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Title:
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Adaptive Estimation for Varying Coefficient Models
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Author(s):
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Yixin Chen* and Qin Wang and Weixin Yao
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Companies:
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Sanofi and Virginia Commonwealth University and Department of Statistics, University of California, Riverside
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Keywords:
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Adaptive estimation ;
EM algorithm ;
Kernel smoothing ;
Local maximum likelihood ;
Varying coefficient models
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Abstract:
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A novel adaptive estimation is proposed for varying coefficient models. Unlike the traditional least squares based methods, the proposed approach can adapt to different error distributions. An efficient EM algorithm is provided to implement the proposed estimation. The asymptotic properties of the resulting estimator are established. Both simulation studies and real data examples are used to illustrate the finite sample performance of the new estimation procedure. The numerical results show that the gain of the new procedure over the least squares estimation can be quite substantial for non-Gaussian errors.
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Authors who are presenting talks have a * after their name.
