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Abstract Details
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Activity Number:
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466
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #305278 |
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Title:
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An Efficient Pathwise Variable Selection Criterion in Weakly Sparse Regression Models
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Author(s):
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Ching-Kang Ing*+
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Companies:
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Academia Sinica
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Address:
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128, Academia Rd. Sec. 2, Taipei 115 Taiwan, , Taiwan, Republic of China
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Keywords:
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orthogonal greedy algorithm ;
weak sparsity ;
high-dimensional Akaike's information criterion ;
asymptotically efficient rate
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Abstract:
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We investigate the prediction capability of the orthogonal greedy algorithm (OGA) in high-dimensional regression models with random regressors. A rate of convergence of the OGA predictor is obtained under the weak sparsity condition, which assumes that the ath powers, 0< a=1, of the absolute regression coefficients are summable. In addition, we introduce a method, called high-dimensional Akaike's information criterion (HDAIC), to determine the number of the OGA iterations, and show that OGA+HDAIC can achieve asymptotically efficient rate in situations where a is unknown.
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