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Activity Number:
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146
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2008 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #300914 |
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Title:
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Inference on Low-Rank Data Matrices with Applications to Microarray Data
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Author(s):
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Xingdong Feng*+
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Companies:
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University of Illinois at Urbana-Champaign
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Address:
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725, South Wright Street, Champaign, IL, 61820,
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Keywords:
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Microarray ; Hypothesis Test
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Abstract:
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Probe-level microarray data are usually stored in matrices, where the row and column correspond to array and probe, respectively. Scientists routinely summarize each array by a single index as the expression level of each probe-set (gene). We examine the adequacy of a uni-dimensional summary for characterizing the data matrix of each probe-set. To do so, we propose a low-rank matrix model for the probe-level intensities, and develop a useful framework for testing the adequacy of uni-dimensionality against targeted alternatives. This is an interesting statistical problem where inference has to be made based on one data matrix. Applications of the proposed tests to GeneChip data show that evidence against a uni-dimensional model is often indicative of practically relevant features of a probe-set. The work is performed under the direction of Professor Xuming He.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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