Abstract Details
Activity Number:
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244
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Type:
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Contributed
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Date/Time:
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Monday, August 10, 2015 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #315100
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Title:
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On the Sparse Bayesian Learning of Linear Models
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Author(s):
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Chia Chye Yee* and Yves Atchade
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Companies:
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University of Michigan and University of Michigan
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Keywords:
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High-dimensional Regression ;
Sparse ;
Bayes ;
EM algorithm ;
LASSO
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Abstract:
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This work is a re-examination of the sparse Bayesian learning (SBL) of linear regression models of Tipping (2001) in a high-dimensional setting. We propose a hard-thresholded version of the SBL estimator that achieves the non- asymptotic estimation error rate of \sqrt{slog p \over n}, where n is the sample size, p the number of regressors and s the number of non-zero regression coefficients. We also establish that with high-probability the estimator identifies the non-zero regression coefficients. In our simulations we found that sparse Bayesian learning regression performs better than LASSO (Tibshirani (1996)) when the signal to be recovered is strong.
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Authors who are presenting talks have a * after their name.
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