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The proof follows the scheme used by Ghosh, Ghosh and Peres: the main step is the construction of a sequence of additive statistics with variance going to zero.
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This is currently a challenge, as it is unclear how to derive additive test statistics from such models.
Noise is additive and its statistics is uncorrelated with speech.
In the SMVAD, the following assumptions are given: 1. Noise is additive and its statistics is uncorrelated with speech.
The computation of the additive trend test statistics on the whole set of markers available in the WTCCC data required 18 sec for the actual phenotypic status of the samples and 50 min for random permutations of their phenotypic status.
However, an adequate information about the statistics of additive noise is not included in RLS computation.
A useful approach is to consider the maximum of the three test statistics under additive, dominant and recessive models (MAX3).
An intuitive approach is to consider the maximum of the three test statistics under additive, dominant and recessive models (MAX3) (Freidlin et al. 2002; Gonzalez et al. 2008).
Family-based analyses were first performed using FBAT, which allows for rapid calculation of statistics under additive, dominant and recessive genetic models.
SABMP algorithm assumes prior Gaussian statistics of the additive noise and the sparsity rate.
The dropping of Vra1 or Vra2 showed a highly significant reduction of the additive LOD (p<0.001 with F statistics).
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