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Abstract Details
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Activity Number:
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586
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics and Marketing
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| Abstract - #300594 |
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Title:
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Testing a Subset of Regression Coefficients in High-Dimensional Data Analysis
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Author(s):
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Hansheng Wang*+ and Wei Lan and Chih-Ling Tsai
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Companies:
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Peking University and Peking University and University of California at Davis
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Address:
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Guanghua School of Management, Beijing, International, 100871, P. R. China
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Keywords:
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High Dimensional Data ;
Subset Testing ;
Regression Analysis ;
Partial Correlation
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Abstract:
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Modern scientific applications always encounter ultra high dimensional data, for which the method of regression analysis has been found very useful. Such type of problem has attracted considerable attention from the past literature.The past research efforts have been focusing on parameter estimation and variable selection. Nevertheless, much less has been done for hypotheses testing under an ultra high dimensional setup. To solve the problem, we systematically investigate ultra high dimensional testing problems under a regression setup. Theoretical analysis reveals that our method is valid as long as both the sample size and the predictor dimension goes to infinity. Monte Carlo studies and numerical examples are presented to illustrate the performance of the proposed test.
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