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Higher Criticism Statistic: Detecting and Identifying Non-Gaussianity in the WMAP First Year Data

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 Added by Laura Cayon
 Publication date 2005
  fields Physics
and research's language is English




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Higher Criticism is a recently developed statistic for non-Gaussian detection, proposed in Donoho & Jin 2004. We find that Higher Criticism is useful for two purposes. First, Higher Criticism has competitive detection power, and non-Gaussianity is detected at the level 99% in the first year WMAP data. We find that the Higher Criticism value of WMAP is outside the 99% confidence region at a wavelet scale of 5 degrees (99.46% of Higher Criticism values based on simulated maps are below the values for WMAP). Second, Higher Criticism offers a way to locate a small portion of data that accounts for the non-Gaussianity. Using Higher Criticism, we have successfully identified a ring of pixels centered at (lapprox 209 deg, bapprox -57 deg), which seems to account for the observed detection of non-Gaussianity at the wavelet scale of 5 degrees. Note that the detection is achieved in wavelet space first. Second, it is always possible that a fraction of pixels within the ring might deviate from Gaussianity even if they do not appear to be above the 99% confidence level in wavelet space. The location of the ring coincides with the cold spot detected in Vielva et al. 2004 and Cruz et al. 2005.

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We consider the problem of detecting a sparse mixture as studied by Ingster (1997) and Donoho and Jin (2004). We consider a wide array of base distributions. In particular, we study the situation when the base distribution has polynomial tails, a situation that has not received much attention in the literature. Perhaps surprisingly, we find that in the context of such a power-law distribution, the higher criticism does not achieve the detection boundary. However, the scan statistic does.
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