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This method assumes data are normally distributed.
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Because the maximum likelihood estimation method assumes that data are normally distributed, the robustness of our findings was tested further.
37 This method assumes that data are missing at random, whereby any systematic differences between the missing and the observed values can be explained by differences in observed data.
Specifically, this method assumes that data are generated by a mixture of probability distributions, either Poisson or Negative Binomial, and defines a likelihood function of the mixture models representing each gene.
This method assumes that the data can be normalised by using a power transformation, which removes skewness from the data set by extending one tail of the distribution and reducing the other [ 26].
The LMS method assumes that the data can be normalized by using power transformation.
The MBC method assumes that the data are generated by a multivariate normal mixture distribution with appropriate means and covariance matrix [ 129].
LDA and MPCA have different advantages and disadvantages, which result from the fact that each method assumes different characteristics for data distributions.
The method assumes that the observed data can be well fit using a sparse linear combination of tensors taken from a fixed collection of possible tensors each having a different orientation.
The GEE method assumes that missingness in data are completely at random.
This method assumes that the input seismic data have been processed to reduce the noise and eliminate multiple contamination.
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