Abstract Details
Activity Number:
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527
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #315776
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Title:
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Semiparametric Estimation Approach for the Sufficient Dimension-Reduction Model
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Author(s):
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Chin-Tsang Chiang*
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Companies:
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National Taiwan University
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Keywords:
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central subspace ;
pseudo least integrated squares estimation ;
semiparametric efficiency bound ;
semiparametric estimation ;
structural dimension ;
sufficient dimension reduction
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Abstract:
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Different from the existing sufficient dimension reduction approaches, our main achievement is to simultaneously estimate the central subspace (CS) and the optimal bandwidth of a kernel distribution estimator through a pseudo estimation criterion. With an elegant device of kernel weight function, the proposed estimation can be effectively estimated in the forward regression setting. The optimal bandwidth selector is also shown to be a valid tuning parameter for the CS estimator. Another important advantage of this estimation technique is its flexibility to allow a response to be discrete and some of covariates to be discrete or categorical providing that a certain continuity condition holds for each CS direction. Theoretically, we establish the asymptomatic normality of the CS estimator with an estimated rather than exact structural dimension. It is further demonstrated by our extensive numerical experiments that the developed approach generally outperforms the semiparametric competitors. Moreover, the applicability and practicality of the proposal are highlighted through data from previous studies.
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Authors who are presenting talks have a * after their name.
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