Robust Semiparametric Optimal Testing Procedure for Multiple Normal Means
In high-dimensional gene expression experiments such as microarray and RNA-seq experiments, the number of measured variables is huge while the number of replicates is small. As a consequence, hypothesis testing is challenging because the power of tests can be very low after controlling multiple test...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2012/913560 |
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| Summary: | In high-dimensional gene expression experiments such as microarray and RNA-seq experiments,
the number of measured variables is huge while the number of replicates is small. As a
consequence, hypothesis testing is challenging because the power of tests can be very low after
controlling multiple testing error. Optimal testing procedures with high average power while
controlling false discovery rate are preferred. Many methods were constructed to achieve high
power through borrowing information across genes. Some of these methods can be shown to
achieve the optimal average power across genes, but only under a normal assumption of
alternative means. However, the assumption of a normal distribution is likely violated in practice.
In this paper, we propose a novel semiparametric optimal testing (SPOT) procedure for high-dimensional
data with small sample size. Our procedure is more robust because it does not
depend on any parametric assumption for the alternative means. We show that the proposed test
achieves the maximum average power asymptotically as the number of tests goes to infinity.
Both simulation study and the analysis of a real microarray data with spike-in probes show that
the proposed SPOT procedure performs better when compared to other popularly applied
procedures. |
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| ISSN: | 1687-952X 1687-9538 |